# failproof ai > failproof ai is the end-to-end failure layer for autonomous AI agents - any agent that runs unattended and is failure-sensitive, where a single wrong action is costly. It closes one loop for that agent: observe every run end to end, identify where the agent fails, write a policy that stops it, and enforce it live in realtime, so the bad action never runs and the agent gets steered to what to do instead. It is harness- and framework-agnostic; the same drop-in, local-only CLI also makes coding agents and chat gateways (Claude Code, Codex, GitHub Copilot CLI, Cursor, OpenCode, Pi, Factory Droid, Devin CLI, Antigravity CLI, Goose, Hermes, OpenClaw) more reliable. Policies come as packs - the maintained coding agent pack carries 38 - and are enforced at the agent's hook layer. Evaluations plug into the eval suite you already run: evaluations run in the cloud - code checks, LLM judges, and the eval set you already have (DeepEval, Ragas, promptfoo or your own), brought in as it is. failproof ai is the reliability layer for autonomous agents: agents that run on their own - in production, on a schedule, or in a long loop - where you cannot review every step and a failure is expensive. It closes one end-to-end loop around that agent. Observe: ingest every run in realtime, watch the live event stream, replay any session span-by-span on a trace rail, and query everything in SQL or a visual builder, with a built-in agent that writes the query, reads the trace, and names the failure mode. Identify: failures cluster into named modes (loops, hallucinated tool calls, drift, runaway completion, dangerous actions) so the dominant pattern surfaces without digging. Write a policy: take one from a policy pack, generate one from an audit finding, or write your own. Enforce: the policy fires at the agent's hook layer in realtime, denying or steering the bad action without changing the agent. Audits over finished sessions keep the agent improving. The same layer drops into local coding agents (Claude Code, Codex, Cursor, GitHub Copilot CLI, OpenCode, Goose and more) to make them more reliable too. Install: `npm install -g failproofai` ## The loop: observe, identify, write, enforce - **Observe** - ingest agent runs in realtime; live event stream, session replay on a trace rail, SQL/visual queries over every event and eval, alerting, and dashboards. A built-in agent answers questions in plain English: it writes the query, reads the trace, and names the failure mode. The `fp` CLI, the /v1 API and the skills repo pull the data into your local tools so your coding agent learns from each failure. - **Evaluate** - evaluations run in the cloud on every finished session: code checks drafted from a plain-English description, tested against real sessions and versioned in the dashboard, LLM judges, and the eval set you already have (DeepEval, Ragas, promptfoo or an in-house suite), brought in as it is without rewriting it. - **Identify** - failures cluster themselves into named modes (loops, hallucinated tool calls, context drift, runaway completion, dangerous actions) so the dominant pattern surfaces for you. - **Write a policy** - take one from a policy pack (the coding agent pack carries 38), generate one from an audit finding, or write your own; backtest it against past agent calls before it ships. - **Enforce** - the policy fires at the harness hook layer in realtime across your harness or custom setup, denying or steering the bad action. Deep audits after every run keep agents improving. ## What it solves - **Agent failure handling** - transient failures are the #1 reason ai agents fail in production. failproof retries, repairs, or summarizes-and-restarts so the run completes. - **LLM failure & error handling** - when an agent loops, drifts, or hallucinates, failproof catches the trace at the hook layer and either nudges the agent back or aborts cleanly. - **Bad LLM tool calls** - catches tool/file invocations the agent invented, blocks the call, and tells the agent what does exist nearby instead. - **Destructive actions** - blocks dangerous shell commands, force-pushes, deletions, and other irreversible actions before they execute; logs everything. - **Agent reliability** - the same hook-level integration wraps any agent, so policies behave identically whether the agent is an autonomous production worker or a local coding assistant like Claude Code or opencode. ## Supported agents failproof ai is harness- and framework-agnostic: it wraps any autonomous agent through the hooks or events it exposes, so the observe -> identify -> write a policy -> enforce loop is identical whether the agent runs unattended in production or as a local coding assistant. Drop-in coding-agent integrations shipping today: - Claude Code - Codex - GitHub Copilot CLI - Cursor - OpenCode - Pi - Factory Droid - Devin CLI - Antigravity CLI - Goose - Hermes (chat gateway) - OpenClaw (chat gateway) Custom Python agents are traced with `failproofai-sdk`, with adapters for LangChain/LangGraph, CrewAI, LlamaIndex and Pydantic AI. ## Failure modes covered (sample) - infinite loop - break the loop, summarize progress, restart fresh - hallucination - block calls to nonexistent tools/files, surface what does exist - context drift - pull the agent back to the original goal - intent completion - stop runs that keep going after the task is done - dangerous action - block destructive shell (rm -rf, sudo, curl | sh), ask a human, log everything - destructive git - block force-push, pushes to main, and history rewrites - secret leakage - block .env reads and redact keys, tokens, and env vars before they reach the context window or logs - prompt injection - ignore instructions smuggled in through tool output, fetched web content, or files - runaway cost - cap token and compute spend; stop an agent that keeps burning budget with no progress - data exfiltration - block sending sensitive data to unapproved external endpoints - unsafe terminal - sandbox shell and file access to the allowed workspace; block path escapes - silent stall - detect when an agent stops making progress and escalate or restart instead of hanging (These are a sample. Policies come from packs - the maintained coding agent pack carries 38 - or you write your own for anything specific to your agent.) ## How integration works 1. `npm install -g failproofai` 2. `failproofai policies --install` - wire hooks into your agent CLIs 3. `failproofai policies add FailproofAI/policies` - add the coding agent policy pack (10 of its 38 policies are on by default) 4. `failproofai` - launches the local dashboard at http://localhost:8020 failproof plugs into the hooks or events your agent already exposes - a coding harness or an autonomous runtime. It runs in a separate local process and reads the trace. No telemetry, no cloud round-trip. ## Pricing failproof ai is open-core: the local enforcement path is open-source, the rest is a closed-source managed/cloud service. - **Open-source (MIT): policy + enforcement for local agents.** Writing a policy and enforcing it at your agent's hook layer, fully local, is the free `failproofai` CLI ([github.com/FailproofAI/failproofai](https://github.com/FailproofAI/failproofai)). Drop it into a local coding agent (Claude Code, OpenAI Codex, Cursor, ...) to make it more reliable at no cost - no telemetry, no cloud round-trip. - **Managed + cloud (closed-source): everything around it, at scale.** For companies deploying agents in production, the observe plane (ingesting every run), failure-pattern identification, multi-tenant failure handling, and managed failure policies are a closed-source managed and cloud service that keeps a whole fleet reliable. [Reach the team](https://cal.com/nkagspace/30-minute-chat-failproof-ai) to set it up. See [Pricing](https://befailproof.ai/pricing/) for the full tier breakdown - Free Forever + Open Source, Team ($99/mo), Scale ($599/mo), and Enterprise - with usage-based limits and what each tier includes. ## Canonical pages - [Home](https://befailproof.ai/): product overview, install command, supported agents, the built-in policies, integration walkthrough, demos - [Pricing](https://befailproof.ai/pricing/): the four tiers - Free Forever + Open Source (free, MIT; policy authoring + local enforcement, plus 5,000 runs and 100 evals a month on a hard cap, deep tracing, built-in dashboards, single user), Team ($99/mo; 50,000 runs, 50,000 evals, 5 users, CLI, MCP, the Failproof agent, custom query engine, alerting), Scale ($599/mo; 500,000 runs, 500,000 evals, unlimited users, auto policy authoring, multi-tenant logging, RBAC and SSO/SAML), and Enterprise (custom; multi-tenant policy governance, on-prem, FDE support). Every tier includes everything below it - [Guides](https://befailproof.ai/guides/): index of every guide and how-to, grouped into specific failure modes, reliability & failure handling, safety & policy enforcement, and observability & debugging - [Policy Hub](https://befailproof.ai/policy-hub/): every published policy, searchable and filterable by category. Policies ship as packs - a GitHub release carrying a manifest, an entry module, and a SHA256SUMS that pins both - and install by digest. Take a whole pack, a category, or a single policy. Packs are published by their authors and are not reviewed, audited, or certified by failproof ai - read a pack's source before installing it - [Publish a Policy Pack](https://befailproof.ai/policy-hub/publish/): how to publish your own policies. One command builds and releases the pack; adding a repo topic gets it indexed. No submission form and no approval queue - [8 Best AI Agent Observability Platforms (2026)](https://befailproof.ai/guides/best-ai-agent-observability-platforms/): A deep comparison of the platforms teams use to trace, evaluate, and govern AI agents in production - what each does, where it stops, and which fits your stack. - [How to Make AI Agents Reliable and Follow Instructions (2026)](https://befailproof.ai/guides/how-to-make-ai-agents-reliable/): Agent reliability is a few distinct jobs, not one feature. The tools and approaches that cover each - observability, evals, guardrails, durable execution, and runtime enforcement - and how to combine them. - [Alternatives](https://befailproof.ai/alternatives/): how failproof compares to the observability, eval, and guardrail tools teams evaluate for agent reliability - what each does and where failproof fits - [AI Agent Stuck in a Loop](https://befailproof.ai/agent-stuck-in-a-loop/): failure mode - why agents retry the same failing step forever, how to detect the loop, and the hook-level policy that breaks it, summarizes progress, and restarts clean - [Agent Hallucinated a Tool or File](https://befailproof.ai/agent-hallucinated-tool-call/): failure mode - block calls to tools, files, and APIs that do not exist at the hook layer, and hand the agent the closest real matches instead - [Agent Context Drift](https://befailproof.ai/agent-context-drift/): failure mode - agents wander off the original goal as context fills up; detect the drift and pull the agent back at the hook layer - [Agent Won't Stop After Finishing](https://befailproof.ai/agent-wont-stop/): failure mode - the task is done but the agent keeps going; stop the run the moment intent is complete and surface the result - [AI Failure Handling](https://befailproof.ai/ai-failure-handling/): reference page - what ai failure handling is, the five categories, and how failproof handles each at the hook layer - [LLM Agent Reliability](https://befailproof.ai/llm-agent-reliability/): reference page - the reliability gap between demo and production, the 3U framework, hook-level enforcement, per-harness coverage - [Agent Error Recovery](https://befailproof.ai/agent-error-recovery/): reference page - the retry/repair/block taxonomy and the recovery strategy per failure mode - [AI Agent Observability](https://befailproof.ai/ai-agent-observability/): reference page - what agent observability is, why logs aren't enough, and the four pieces failproof ships (live event stream, session replay, queries, alerts/dashboards) plus the built-in agent and CLI + MCP into local Claude - [Agent Session Replay](https://befailproof.ai/agent-session-replay/): how-to - replay any run span-by-span on the trace rail to see exactly where it looped, drifted, or got blocked - [Query Agent Traces](https://befailproof.ai/query-agent-traces/): how-to - SQL and a visual builder over every event, session, and eval; saved queries, dashboards, and alerts, with the built-in agent writing the SQL for you - [Agent Failure Alerting](https://befailproof.ai/agent-failure-alerting/): how-to - trigger on a metric threshold, a saved query, or an eval score; incidents and triage; pull alerts into your local Claude - [Stop Claude Code From Running Dangerous Commands](https://befailproof.ai/stop-claude-code-dangerous-commands/): how-to - install the PreToolUse hooks that block rm -rf, sudo, curl | sh, terraform destroy, force push, and the rest - [Block rm -rf in Claude Code](https://befailproof.ai/block-rm-rf-claude-code/): how-to - the block-rm-rf policy in detail, what it matches, how the safe-path allowlist works - [Claude Code Safety Hooks Setup](https://befailproof.ai/claude-code-safety-hooks/): complete tutorial - PreToolUse, PostToolUse, Stop. 39 built-in policies grouped by category. Three configuration scopes. Custom policies in JavaScript. - [Prevent Claude Code From Accessing .env Files](https://befailproof.ai/prevent-claude-code-env-file-access/): how-to - block-env-files, protect-env-vars, and the five sanitize-* PostToolUse redactors that keep secrets out of the agent context window - [Sandbox Claude Code Terminal Access](https://befailproof.ai/sandbox-claude-code-terminal/): how-to - policy sandbox without a container or VM. PreToolUse confinement, PostToolUse sanitization, Stop gates. - [Prevent AI Agent Force Push](https://befailproof.ai/prevent-ai-agent-force-push/): how-to - block-force-push, block-push-master, block-work-on-main across claude code, codex, cursor agent, gemini cli, copilot, picode, opencode - [The Token Times](https://befailproof.ai/thetokentimes/): failproof ai's monthly newspaper on the AI industry - an eight-page A3 broadsheet covering models, funding, launches, prices, and retired models, every claim dated and sourced. Read online free or download the print PDF - [The Token Times no. 2](https://befailproof.ai/thetokentimes/2026-october/): aug–sep 2026, 8 pages. dario amodei asked the industry to slow down on sept. 12, and within ten days anthropic, openai and xai had all shipped new models. plus a model that cannot chat outdraws opus 5.5, nvidia agrees to buy hugging face, and openai comes for the whole workday. - [The Token Times no. 1](https://befailproof.ai/thetokentimes/2026-august/): jul–aug 2026, 8 pages. an openai model the public cannot use answers a question mathematicians left open for 27 years — then gets slowed on cyber six days later. plus jeff dean leaves google, stripe buys openrouter, and gpt-5.6 luna drops 80 percent. - [Blog](https://befailproof.ai/blog/): the failproof ai journal - field writing on agent reliability, durability, and production AI - [Book a demo](https://cal.com/nkagspace/30-minute-chat-failproof-ai): 30-minute call with the founder ## Alternatives failproof vs the tools teams evaluate for agent reliability. Built for agents, not LLMs: agent-runtime tracing plus realtime enforcement that stops failures, on-prem or cloud. - [failproof vs Langfuse](https://befailproof.ai/alternatives/langfuse/): the langfuse alternative for teams running high-impact autonomous agents - end-to-end reliability that stops failures, not just tracing that records them. - [failproof vs Galileo](https://befailproof.ai/alternatives/galileo/): the galileo alternative for mid-market teams that want agent-action enforcement included - not hosted guardrails gated to a sales-led Enterprise tier. - [failproof vs Arize](https://befailproof.ai/alternatives/arize/): the arize alternative for teams that need agent-runtime reliability and enforcement, not just ML-grade model evaluation. - [failproof vs LangSmith](https://befailproof.ai/alternatives/langsmith/): the langsmith alternative for teams that want agent-runtime reliability across any stack - not tracing and evals tied to LangChain. - [failproof vs Helicone](https://befailproof.ai/alternatives/helicone/): the helicone alternative for teams that need to govern what agents do - not just log the LLM calls they make. - [failproof vs Braintrust](https://befailproof.ai/alternatives/braintrust/): the braintrust alternative for teams that need to govern agents in production - not just evaluate model quality before they ship. - [failproof vs Datadog](https://befailproof.ai/alternatives/datadog/): the datadog alternative for teams that need to stop agent failures - not just monitor and alert on them next to infra metrics. - [failproof vs Guardrails AI](https://befailproof.ai/alternatives/guardrails-ai/): the guardrails ai alternative for teams that need to govern agent actions at runtime - not just validate and repair LLM text outputs. - [failproof vs Lakera](https://befailproof.ai/alternatives/lakera/): the lakera alternative for agent reliability - lakera secures the model against attacks; Failproof AI keeps the agent itself reliable. here is where each fits. - [failproof vs NeMo Guardrails](https://befailproof.ai/alternatives/nemo-guardrails/): the nemo guardrails alternative for teams governing autonomous agents - not just adding dialog rails to conversational LLM apps. - [failproof vs Temporal](https://befailproof.ai/alternatives/temporal/): temporal makes your workflows durable; Failproof AI makes the agents inside them reliable. here is where each fits - and why teams run both. - [failproof vs DBOS](https://befailproof.ai/alternatives/dbos/): dbos makes your workflows durable in-code; Failproof AI makes the agents inside them reliable. here is where each fits - and why teams run both. - [failproof vs Judgment Labs](https://befailproof.ai/alternatives/judgment-labs/): the judgment labs alternative for mid-market teams - judges that score every run, plus policies that stop the failure, with public pricing and no demo required. - [failproof vs Raindrop](https://befailproof.ai/alternatives/raindrop/): the raindrop alternative for teams that want the silent failure stopped, not just posted to slack - enforcement included, and a lower price to start. - [failproof vs Latitude](https://befailproof.ai/alternatives/latitude/): the latitude alternative for teams that need an agent failure stopped while it happens - latitude traces and fixes after the fact, failproof enforces at runtime. - [failproof vs Future AGI](https://befailproof.ai/alternatives/future-agi/): the future agi alternative for teams that want enforcement at the agent itself - not only on traffic routed through a gateway - without operating a many-service self-host stack. ## Comparisons Head-to-head comparisons of the agent evaluation, monitoring and guardrail tools teams shortlist - Judgment Labs, Raindrop, Latitude, Future AGI, Galileo and others. Each line carries the page's question and its short answer. - [Agent Control vs Failproof AI policies: two ways to gate tool calls](https://befailproof.ai/compare/galileo-agent-control-vs-failproof-policies/): Should you gate agent tool calls with Galileo's Agent Control or with Failproof AI policies? Both gate tool calls before they run. Pick Agent Control (Apache-2.0) for agents you build on LangChain, CrewAI, Google ADK or Strands: a decorator marks each step, and controls defined on a server you run return deny, steer, warn, log or allow. Pick Failproof AI (MIT) for agents in Claude Code, Codex, Cursor or nine other harnesses: policies run at their hooks with no server, and a 38-policy coding agent pack is ready to add. - [Agent Judge vs Failproof AI evaluations: a judge, or the whole loop](https://befailproof.ai/compare/judgment-labs-agent-judge-vs-failproof-evaluators/): How does Judgment Labs Agent Judge compare with Failproof AI evaluations? Both run LLM and code judges on agent traces and sessions, with reasoning behind every verdict. The difference is the product around the judge. Agent Judge scores sampled traces on Judgment Labs' platform and feeds monitoring and alerts, bought through a demo. Failproof AI evaluates every trace and session, gives each customer its own evaluations, groups failures into findings with a fix, and turns a finding into a backtested policy that blocks the next failure, self-serve from $0. - [Evals vs guardrails: measuring agents vs stopping them](https://befailproof.ai/compare/evals-vs-guardrails/): What is the difference between evals and guardrails for AI agents, and which do I need? Evals measure: they score agent sessions after they run, or a test set before release, and they find failures nobody predicted. Guardrails act: they check model input and output, or the agent's tool calls, on the request path, and block or redirect before damage is done. Use evals to discover what goes wrong, turn the repeatable, checkable failures into guardrails, and keep evaluating to see whether the guardrails hold. - [Failproof AI vs Future AGI: finding agent failures and stopping them](https://befailproof.ai/compare/failproof-vs-future-agi/): How does Failproof AI compare with Future AGI for finding and stopping agent failures? Both evaluate agent runs and both can block. Failproof AI runs evaluations in the cloud on every trace and session, including the eval set you already have, groups failures into findings with a fix, and blocks the next one inside the agent harness before the tool runs. Future AGI is a broad Apache-2.0 platform whose guardrails act on model traffic routed through its gateway, with simulation and prompt optimization alongside. Both publish prices and start free. - [Failproof AI vs Galileo: finding agent failures and stopping them](https://befailproof.ai/compare/failproof-vs-galileo/): How does Failproof AI compare with Galileo for finding and stopping agent failures? Both evaluate agent runs and both can deny a tool call. Failproof AI runs evaluations in the cloud on every trace and session, including the eval set you already have, groups failures into findings with a fix, and blocks the next one inside the agent harness before the tool runs, from the free tier. Galileo ships built-in metrics and Luna-2 judge models; its hosted guardrails and Luna-2 are Enterprise, and open-source Agent Control is a server you run. - [Failproof AI vs Judgment Labs: features, pricing, and where each fits](https://befailproof.ai/compare/failproof-vs-judgment-labs/): How does Failproof AI compare with Judgment Labs, feature by feature? On judging, the two are at par: both run code and LLM judges on single traces and whole sessions, point at the step that failed, and return pass or fail with reasoning. The difference is the product around the judge. Failproof AI gives each customer organization its own evaluations, attaches a recommended fix to each issue, backtests it as a policy, and blocks the next failure at runtime. Judgment Labs is sold through a demo and publishes no price. - [Failproof AI vs Latitude: finding agent failures and stopping them](https://befailproof.ai/compare/failproof-vs-latitude/): How does Failproof AI compare with Latitude for finding and fixing agent failures? Both find agent failures in production. Failproof AI runs evaluations in the cloud on every trace and session, including the eval set you already have, groups failures into findings with a fix, then blocks the next one before it runs. Latitude scores sessions after they complete and sends a coding agent to open a fix PR; it blocks nothing. Latitude is MIT and free to self-host, and both entry plans are $99 a month. - [Failproof AI vs Raindrop: finding silent agent failures and stopping them](https://befailproof.ai/compare/failproof-vs-raindrop/): How does Failproof AI compare with Raindrop for silent agent failures? Both find silent agent failures in production. Failproof AI evaluates every trace and session with code checks, LLM judges or your existing eval suite, groups failures into findings with a fix, then blocks the next one before it runs. Raindrop classifies events with its own model, groups failures into Issues and alerts in Slack; it blocks nothing. Failproof AI starts free, then $99 a month; Raindrop Pro is $299 a month plus a per-event fee. - [Future AGI Protect vs Failproof AI policies: model traffic vs agent actions](https://befailproof.ai/compare/future-agi-protect-vs-failproof-policies/): Should you guard agents with Future AGI Protect or with Failproof AI policies? Use Future AGI Protect when the risk is in what reaches or leaves the model: its checks block, warn, mask or log text, image and audio, in the Agent Command Center gateway or through a protect() SDK call. Use Failproof AI policies when the risk is what the agent does: they run at the hook layer of Claude Code, Codex, Cursor and other harnesses and deny a tool call before it executes. An agent that talks to users and touches systems can need both. - [Future AGI vs Galileo: gateway guardrails vs Agent Control and Enterprise](https://befailproof.ai/compare/future-agi-vs-galileo/): Should a team pick Future AGI or Galileo for guardrails and evaluation? Pick Future AGI if you want guardrails on every tier, running in an OpenAI-compatible gateway or through an SDK call, inside an Apache-2.0 platform you can self-host. Pick Galileo if evaluation depth matters most, with 20+ out-of-box evals, CLHF and Luna-2, and you will buy Enterprise for hosted real-time guardrails or run the open-source Agent Control yourself. Galileo Protect is deprecated; Agent Control replaces it. - [Future AGI vs Langfuse: a broad open-source platform vs focused observability](https://befailproof.ai/compare/future-agi-vs-langfuse/): Should a team pick Future AGI or Langfuse as its open-source LLM and agent platform? Pick Future AGI if you want one open-source platform to do more than observe: 50+ eval metrics with its own Turing judge models, text and voice simulation, prompt optimization, an OpenAI-compatible gateway, and Protect guardrails that can block model I/O and gateway tool calls. Pick Langfuse if you want focused observability - tracing, prompt management, evals, datasets - on a lighter self-host stack with a much larger community. Langfuse does not block at runtime. - [Galileo vs Braintrust: enterprise evals and guardrails vs eval-first tooling](https://befailproof.ai/compare/galileo-vs-braintrust/): Should a team pick Galileo or Braintrust to evaluate and guard its AI agents? Pick Galileo if you want evaluation packaged: 20+ built-in evals, Luna-2 small judge models, judge tuning from a few labels, and hosted real-time guardrails, though Luna-2 and those guardrails are Enterprise-only. Pick Braintrust if you want evaluation as code: datasets, immutable experiments, a GitHub Action on every pull request and online scoring, with Pro at $249 a month. Galileo Pro is $100 a month, billed yearly and sold by demo. - [judgeval vs DeepEval: two open-source Python eval libraries](https://befailproof.ai/compare/judgeval-vs-deepeval/): Should a Python team use judgeval or DeepEval to evaluate its LLM agents? Use DeepEval if you want a local-first library: pytest-style tests, ready-made LLM-as-a-judge metrics for agents, RAG and multi-turn chat, any judge model, and results that stay on your machine unless you log in to Confident AI. Use judgeval if you are adopting the Judgment Labs platform: it traces your agent over OpenTelemetry and sends the data to hosted judges and production monitoring. Both are Apache-2.0. - [Judgment Labs vs Braintrust: judges in production vs experiments in CI](https://befailproof.ai/compare/judgment-labs-vs-braintrust/): Should a team pick Judgment Labs or Braintrust to evaluate its agents? Pick Judgment Labs if your hard problem is judging live agent behavior: Agent Judge scores long trajectories against a rubric, and Behavior Discovery looks for failure patterns you have not named. Pick Braintrust if your hard problem is shipping changes safely: immutable experiments, a GitHub Action that posts eval results on every pull request, and published pricing from $0. Both score production traffic after the fact; neither blocks an action. - [Judgment Labs vs Future AGI: focused judges vs a platform with a gateway](https://befailproof.ai/compare/judgment-labs-vs-future-agi/): Should a team pick Judgment Labs or Future AGI to evaluate and control its agents? Pick Future AGI if you want one open-source platform for evals, simulation, a model gateway and guardrails, including tool-call blocking for traffic routed through its gateway, with a free tier and published add-ons. Pick Judgment Labs if you want focused, rubric-driven judging of long agent trajectories and discovery of unnamed failures, with nothing added to the request path; it is hosted, priced by demo, and scores after the run. - [Judgment Labs vs Galileo: research-led judges vs an enterprise eval platform](https://befailproof.ai/compare/judgment-labs-vs-galileo/): Should a team pick Judgment Labs or Galileo to evaluate its AI agents? Pick Galileo if you want to start today on a free tier, want 20+ evals out of the box, and expect to need guardrails, VPC deployment or SSO, which sit on its Enterprise plan. Pick Judgment Labs if the hard part is judging long agent trajectories and finding failure modes nobody has named yet, and a hosted, demo-led engagement suits how you buy. Judgment Labs publishes no pricing and does not block actions. - [Judgment Labs vs Langfuse: agent judges vs open-source LLM observability](https://befailproof.ai/compare/judgment-labs-vs-langfuse/): Should a team pick Judgment Labs or Langfuse to trace and evaluate its agents? Pick Judgment Labs if judging agent behavior is the whole job: Agent Judge scores long trajectories against a rubric, and Behavior Discovery looks for failures you did not know to test for. Pick Langfuse if you want an open-source observability base you can self-host for free today, with prompt management, managed LLM-as-a-judge evaluators and published prices from $0. Neither blocks an agent action at runtime. - [Judgment Labs vs Latitude: an open SDK or an open platform](https://befailproof.ai/compare/judgment-labs-vs-latitude/): Should a team pick Judgment Labs or Latitude to monitor and evaluate its agents? Pick Latitude if your traces must stay in the EU or on your own infrastructure today: the whole platform is MIT, self-hosts with Docker Compose or Helm, and its cloud runs in AWS eu-central-1, from free to $99 a month. Pick Judgment Labs if grading long trajectories with rubric and Python judges, and discovering unnamed failure modes, matters more than where the platform runs; it is hosted, with self-hosting listed as coming soon. - [Judgment Labs vs Raindrop: rubric judges vs production signals](https://befailproof.ai/compare/judgment-labs-vs-raindrop/): Should a team pick Judgment Labs or Raindrop to catch silent agent failures in production? Pick Raindrop if you run a product with real user volume and want to describe a failure in plain English, get a yes/no signal, and compare cohorts of logged production events to see whether each fix worked: Pro is $299 a month plus $0.003 per event. Pick Judgment Labs if your agents run long, expensive trajectories that need a rubric to grade, and you test fixes offline before shipping. Judgment Labs publishes no pricing; neither blocks actions. - [Latitude vs Future AGI: two open-source platforms, one self-host decision](https://befailproof.ai/compare/latitude-vs-future-agi/): Should a platform team self-host Latitude or Future AGI? Pick Latitude if you want to self-host a focused agent observability platform under a plain MIT licence, on Docker Compose or its Helm chart, with evals, annotation and Agent Dispatch fix PRs. Pick Future AGI if you want far more in one Apache-2.0 platform, including simulation, built-in evaluators, a model gateway and runtime guardrails, and can run it on Docker Compose, its only supported self-host path today. Both need PostgreSQL, ClickHouse, Redis and Temporal. - [Latitude vs Galileo: open-source observability vs an enterprise eval platform](https://befailproof.ai/compare/latitude-vs-galileo/): Should a team pick Latitude or Galileo to evaluate its AI agents? Pick Latitude if you want an MIT platform you can self-host free, with rule, LLM-judge and JavaScript evaluations, annotation and Agent Dispatch fix PRs, from $99 a month. Pick Galileo if you want 20+ built-in evals, judges you can tune from a handful of labels, and a path to runtime guardrails; its free tier is self-serve, Pro at $100 a month billed yearly starts with a demo, and guardrails, Luna-2 and VPC deployment are Enterprise. Latitude never blocks a response. - [Latitude vs Langfuse: two open-source platforms, two ideas of what comes next](https://befailproof.ai/compare/latitude-vs-langfuse/): Should a team pick Latitude or Langfuse as its open-source agent observability platform? Pick Latitude if you want failures turned into action: Signals generate evaluations, humans annotate and check judge alignment, and Agent Dispatch sends Claude Code or Cursor to open fix PRs, with unlimited seats on every plan. Pick Langfuse if you want prompt management, datasets and a broad evaluation toolkit in the larger open-source project. Both are MIT, both self-host for free, and neither blocks agent actions at runtime. - [LLM judge vs code judge: which checks belong where](https://befailproof.ai/compare/llm-judge-vs-code-judge/): Should you evaluate an AI agent with code checks or with an LLM judge? Use a code judge when the criterion has an exact answer you can compute from the session: a tool error, a count, a comparison, a schema, a file path. Use an LLM judge when the criterion needs reading comprehension: whether the task matched the user's intent, whether an answer is grounded, whether an escalation was warranted. Run code checks first on every session, and spend judge calls only on what code cannot settle. - [Online vs offline agent evals: what each one catches](https://befailproof.ai/compare/online-vs-offline-agent-evals/): Should you evaluate AI agents offline before deploy or online in production? Use offline evals to stop regressions before deploy: a frozen dataset run against every change, with ground truth where you have it. Use online evals to catch what the dataset never contained: real sessions scored after they finish, with code checks on all of them and judges on a sample. Most teams need both. Neither blocks a bad action while it happens; that takes a runtime policy. - [Pairwise vs pointwise LLM judges: compare two or score one](https://befailproof.ai/compare/pairwise-vs-pointwise-llm-judges/): Should an LLM judge score outputs one at a time or compare them in pairs? Use pointwise judging - one output, one rubric, one verdict - for production monitoring, because it gives an absolute pass rate you can chart and alert on. Use pairwise judging to decide between two versions of an agent or prompt run on the same inputs, because relative calls pick up smaller differences. Always run pairwise judgments in both orders and count disagreements as ties, since judges favour one position. - [Raindrop vs Braintrust: production truth vs offline evals](https://befailproof.ai/compare/raindrop-vs-braintrust/): Should a team pick Raindrop or Braintrust to catch agent failures? Pick Raindrop if you have enough users that production tells you more than a test set can: Signals classify silent failures, issues are ranked by severity, and Experiments compare cohorts of logged production events after you ship a change, from $299 a month plus per event. Pick Braintrust if you need to know before merge: datasets, immutable experiments, a CI action and online scoring, from $0. Many teams need both halves; neither blocks agent actions. - [Raindrop vs Future AGI: production signals vs an eval and simulation platform](https://befailproof.ai/compare/raindrop-vs-future-agi/): Should a team pick Raindrop or Future AGI for its AI agents? Pick Future AGI if you need to test an agent before it has users, including simulated voice conversations, or want evals, a model gateway and runtime guardrails in one Apache-2.0 platform with a generous free tier. Pick Raindrop if you already have production traffic and want the sharpest tool for finding silent failures in it: plain-language Signals, ranked Issues in Slack and Experiments on logged cohorts, at $299 a month plus events. Raindrop does not block actions. - [Raindrop vs Galileo: learning from production vs evals that become guardrails](https://befailproof.ai/compare/raindrop-vs-galileo/): Should a team pick Raindrop or Galileo to monitor and evaluate its AI agents? Pick Raindrop if you ship an AI product with real user volume and want to learn from production: plain-language Signals, ranked Issues in Slack, and Experiments that compare cohorts of logged production events, from $299 a month plus events. Pick Galileo if you want 20+ evals before and after release and a path to runtime guardrails, from a free tier of 5,000 traces; guardrails, Luna-2 and VPC deployment are Enterprise. Raindrop does not block actions. - [Raindrop vs LangSmith: production signals vs a tracing and evals platform](https://befailproof.ai/compare/raindrop-vs-langsmith/): Should a team pick Raindrop or LangSmith to monitor its agents in production? Pick Raindrop if your agent serves many users and the failures you fear are silent: plain-language Signals, severity-ranked issues and Experiments that compare cohorts of logged production events, with Pro at $299 a month plus per-event pricing. Pick LangSmith if you want tracing, offline and online evals, and alerts to webhooks or PagerDuty in one framework-agnostic platform, with a free developer seat and Plus at $39 per seat. Neither blocks agent actions. - [Raindrop vs Latitude: Slack-first triage vs a coding agent that opens the PR](https://befailproof.ai/compare/raindrop-vs-latitude/): Should a team pick Raindrop or Latitude to find and fix silent agent failures? Pick Raindrop if you want the most polished loop from silent failure to ranked issue, alerts in Slack, and experiments that compare logged cohorts before and after each fix, at $299 a month plus $0.003 per event. Pick Latitude if you want an MIT platform you can self-host, alerts by email as well as Slack, and Agent Dispatch sending Claude Code or Cursor to open a fix PR, from free to $99 a month. Neither blocks actions. ## Answers One question per page about agent eval and monitoring tools - pricing, self-hosting, open source, runtime blocking - answered in the first paragraph. - [Agent evaluation tools with runtime guardrails](https://befailproof.ai/answers/agent-eval-tools-with-runtime-guardrails/): Which agent evaluation tools can block unsafe actions at runtime? Failproof AI, Galileo and Future AGI offer runtime guardrails. Failproof AI can stop a tool at the agent hook before it runs. Galileo Agent Control checks model and tool inputs and outputs. Future AGI Protect checks traffic routed through its gateway. Judgment Labs, Raindrop and Latitude evaluate completed runs and alert you, but they do not block actions. - [Agent observability pricing compared: eight platforms, one table](https://befailproof.ai/answers/agent-observability-pricing-compared/): How do agent observability and evaluation platforms compare on price? Published entry prices run from $29 a month for Langfuse Core to $299 a month for Raindrop Pro. Failproof AI Team and Latitude Pro cost $99, Galileo Pro is listed at $100 billed yearly through a demo, Braintrust Pro costs $249, and Future AGI is usage-based with add-ons from $250. Judgment Labs does not publish pricing. Compare the products on the work they remove as well as the usage meter: Failproof AI is built to find failure modes automatically with code-based and LLM-based evaluations, turn them into findings and recommended fixes, and then steer or block the agent when needed. - [An agent eval platform for mid-market teams: six requirements, six vendors](https://befailproof.ai/answers/agent-eval-platform-for-mid-market-teams/): What should a mid-market company look for in an agent evaluation platform? A mid-market company should prioritize six things in an agent evaluation platform: a self-serve trial, published pricing, useful evaluations below Enterprise, a workflow from failure discovery to a verified fix, a credible deployment path and tools a small team can operate. Failproof AI is the strongest fit when production failure discovery and remediation matter. Latitude and Langfuse are stronger for free self-hosting, while Galileo and Judgment Labs suit teams willing to buy through sales. - [Best agent behavior monitoring tools for finding and fixing failures](https://befailproof.ai/answers/best-agent-behavior-monitoring-tools/): What are the best tools for monitoring AI agent behavior in production? Choose Failproof AI when you want the platform to find recurring failure modes automatically, recommend and test fixes, and stop preventable failures, with evaluations and alerts built into the same workflow. Judgment Labs is strongest for hosted judge tooling, Raindrop for production signals and cohort comparisons, Galileo for built-in metrics at enterprise scale, Braintrust and Langfuse for eval-led workflows, and Latitude or Future AGI when open-source self-hosting is a requirement. - [Best Judgment Labs alternatives for agent evaluation and monitoring](https://befailproof.ai/answers/judgment-labs-alternatives/): What are the alternatives to Judgment Labs for monitoring and evaluating AI agents? The best Judgment Labs alternative depends on why you are switching. Choose Failproof AI to find recurring production failures and turn tested fixes into behavioral policies, Raindrop for production behavior signals and cohort comparisons, Latitude or Langfuse for open-source self-hosting, Galileo for built-in evaluation models, Future AGI for simulation and gateway guardrails, or Braintrust for experiment-driven evaluation. - [Best LLM-as-a-judge tools for evaluating AI agents](https://befailproof.ai/answers/best-llm-as-a-judge-tools/): Which tools are best for running LLM-as-a-judge evaluations on AI agents? For production agents, Failproof AI is the strongest all-round option because it runs LLM and code-based evaluations, analyzes recurring failures across sessions, alerts owners and helps turn a tested fix into a behavioral policy. Use DeepEval for a lightweight CI library, Langfuse for open-source self-hosting, Braintrust for experiment-driven evaluation, or Galileo and Judgment Labs when judge tooling itself is the priority. - [Build vs buy for agent evals: build the judgment, buy the plumbing](https://befailproof.ai/answers/buy-vs-build-agent-evals/): Should we build our own agent evaluation pipeline or buy a platform? Build the judgment and buy the plumbing, unless you already run the plumbing. Rubrics, labeled sessions and judge calibration are yours either way; no vendor can write them for you. A platform adds trace storage, a UI for reading sessions, scheduled or per-session scoring, charts, alerts, retention and access control. Build when one agent runs at low volume on infrastructure your team already operates; buy once several agents are in production and someone is on call for them. - [Can you self-host Judgment Labs? The SDK yes, the platform not yet](https://befailproof.ai/answers/can-you-self-host-judgment-labs/): Can you self-host Judgment Labs? You cannot self-host the Judgment Labs platform today. The Apache-2.0 judgeval SDK runs in your code, but hosted tracing, evaluation, monitoring and alerts use the Judgment platform. Its docs list platform self-hosting as coming soon, with early access through contact@judgmentlabs.ai. Failproof AI offers its complete Cloud platform as an Enterprise deployment in your environment. Latitude and Langfuse can be self-hosted under MIT, while Future AGI publishes an Apache-2.0 core with separately licensed enterprise code. - [Can you self-host Raindrop? VPC deployment is a limited beta](https://befailproof.ai/answers/can-you-self-host-raindrop/): Can you self-host Raindrop on your own infrastructure? Raindrop is primarily a cloud service. It offers deployment in a customer VPC through a beta rollout for selected partners, rather than as a generally available self-hosted package. Workshop, its MIT-licensed local debugger and eval runner, runs on a developer machine but does not replace the production platform. - [Cisco acquired Galileo: what it means for customers](https://befailproof.ai/answers/galileo-cisco-acquisition/): Did Cisco acquire Galileo, and what does it mean for Galileo customers? Yes. Cisco announced its intent to acquire Galileo on 9 April 2026, and Splunk confirmed completion on 28 May. Galileo was renamed Splunk Agent Observability on 7 August. The purchase price was not disclosed. Existing customers should confirm renewal pricing, support ownership, data processing terms and the migration path from Galileo Protect to Agent Control. - [Do you need an agent evaluation platform?](https://befailproof.ai/answers/do-we-need-an-eval-platform/): Does a team running AI agents need an evaluation platform, or is a CI script enough? You probably do not need an evaluation platform while one team runs one agent and can review its sessions by hand. Start with code checks, one task-completion judge and a small set of human labels in CI. Consider a platform when production traffic is too large to inspect manually, failures repeat across sessions, several people or agents need the same view, or results need alerts and assigned follow-up. - [Does Future AGI block tool calls?](https://befailproof.ai/answers/does-future-agi-block-tool-calls/): Does Future AGI block tool calls made by AI agents? Yes. Future AGI can block tool calls that pass through its Agent Command Center gateway. Tool Permissions checks which tools an agent may call, while MCP Security inspects MCP traffic. In Enforce mode, a failed check returns a 403. Confirm that every action you need to control passes through the gateway, because local shell commands and file edits may happen inside the agent harness instead. - [Does Judgment Labs block agent actions? No - here is what does](https://befailproof.ai/answers/does-judgment-labs-block-agent-actions/): Does Judgment Labs block agent actions before they run? No. Judgment Labs scores agent traces and alerts on them after execution. It does not block, pause or rewrite an agent's action. Its monitoring runs off the request path, while blocking requires a separate check before the tool executes. Failproof AI can analyze sessions to find recurring behavioral failures, then use low-latency policies at supported agent hooks when a finding maps to an action that should be steered or blocked. - [Does Raindrop block agent actions?](https://befailproof.ai/answers/does-raindrop-block-agent-actions/): Does Raindrop block or stop agent actions at runtime? No. Raindrop detects agent failures after they happen. Signals label matching events, Issue Detection ranks them, alerts go to Slack, and the Triage Agent can pass the root cause to a coding agent over MCP. PII Guard protects the copy Raindrop stores by redacting personal data at ingestion, but it does not stop the agent's action. - [Future AGI alternatives: six options and who each one fits](https://befailproof.ai/answers/future-agi-alternatives/): What are the best alternatives to Future AGI for evaluating and controlling AI agents? The main Future AGI alternatives are Failproof AI for automatically finding failure modes and turning findings into tested fixes, Langfuse for open-source tracing and evaluations, Latitude for MIT-licensed observability with fix PRs, Galileo for built-in metrics and judge models, Judgment Labs for rubric-based judging, and Raindrop for production signals and cohort comparisons. - [Future AGI pricing: free tier, usage rates and the three add-ons](https://befailproof.ai/answers/future-agi-pricing/): How much does Future AGI cost? Future AGI is free to start with 50 GB of storage, 2K AI credits, 100K gateway requests, 30-day retention and unlimited seats. Usage above those allowances is billed separately. Optional add-ons cost $250 for Boost, $750 for Scale and $2,000 for Enterprise each month. Confirm SSO, compliance and SLA terms before buying because the pricing and enterprise pages describe some of them differently. - [Future AGI self-hosting requirements: hardware, stack and caveats](https://befailproof.ai/answers/future-agi-self-hosting-requirements/): What do you need to self-host Future AGI? Self-hosting Future AGI means Docker Compose today; its docs say there are no Helm charts or Kubernetes manifests yet. The stack includes PostgreSQL, ClickHouse, Redis, Temporal, MinIO and a code executor (the README also lists RabbitMQ), needs Docker Engine 24.0+ and Compose v2.24+, and is sized from 4 cores and 8 GB of RAM for a trial to 16+ cores and 32+ GB for production. Telemetry is on until you set FUTURE_AGI_TELEMETRY_DISABLED=1. - [Galileo alternatives: seven options and who each one fits](https://befailproof.ai/answers/galileo-alternatives/): What are the best alternatives to Galileo for evaluating and guarding AI agents? By need: Failproof AI to find recurring failures in agent behavior and turn them into tested behavioral policies; self-run Agent Control for Galileo's control plane without Enterprise; Future AGI for a broad open-source platform with gateway guardrails; Langfuse for open-source tracing and evals; Judgment Labs for rubric judges; Raindrop for production signals; and Latitude for a fully MIT self-host. If Galileo's 20+ built-in evals are what you rely on, staying is reasonable. - [Galileo pricing: Free, Pro at $100 a month, and what Enterprise adds](https://befailproof.ai/answers/galileo-pricing/): How much does Galileo cost? Galileo has three plans: Free at $0, with 5,000 traces a month and unlimited users; Pro at $100 a month billed yearly, with 50,000 traces, standard RBAC and Slack support, priced up with trace volume; and Enterprise at a quoted price, with unlimited traces, SSO, real-time guardrails, Luna-2, and hosted, VPC or on-prem deployment. - [Galileo Protect is deprecated: what to use instead](https://befailproof.ai/answers/galileo-protect-deprecated/): Galileo Protect is deprecated - what should you use instead? Galileo deprecated Protect in June 2026, and its docs tell new users to "set up Agent Control instead". Agent Control is Apache-2.0 and self-run, and gates LLM and tool inputs and outputs with deny, steer, warn, log or allow decisions. Managed Agent Control is available on Galileo Enterprise. Future AGI Protect is another option for model traffic, while Failproof AI can find recurring behavioral failures and turn suitable findings into tested policies at supported agent hooks. - [How much do agent evals cost? Judge tokens plus the platform fee](https://befailproof.ai/answers/how-much-do-agent-evals-cost/): How much does it cost to run evaluations on AI agent sessions? Agent evals cost two things: judge tokens, billed by your model provider, and a platform fee, billed in whatever unit the vendor meters. Judge cost is sessions judged × judge calls per session × tokens per call × your per-token price, so it grows with transcript length as much as with volume. Code checks before judges, sampling and trimming the transcript can cut the token bill by an order of magnitude; the platform fee depends on the meter. - [Is Galileo Luna-2 Enterprise-only? Yes, and other ways to judge cheaply](https://befailproof.ai/answers/is-galileo-luna-2-enterprise-only/): Is Galileo's Luna-2 only available on the Enterprise plan? Yes. Galileo's docs state that "Luna-2 is only available in the Enterprise tier", and Splunk Agent Observability's docs, which replaced them in August 2026, say the same. Luna-2 is a pair of small evaluation models, 3B and 8B, fine-tuned from Llama and listed at $0.02 per million tokens. On Free and Pro, cheaper judging comes from code checks first, sampling, smaller judge models you host, and shorter judge prompts. - [Is judgeval open source? Yes - the SDK, not the platform](https://befailproof.ai/answers/is-judgeval-open-source/): Is judgeval, the Judgment Labs SDK, open source? Yes. judgeval, Judgment Labs' Python SDK, is open source under Apache-2.0, with smaller JavaScript, Go and Java repositories. The Judgment platform is not open source: its hosted quickstart needs a Judgment API key and organization ID, and the docs list platform self-hosting as coming soon. - [Judgment Labs pricing: not published, and what to ask instead](https://befailproof.ai/answers/judgment-labs-pricing/): How much does Judgment Labs cost? Judgment Labs does not publish its pricing: judgmentlabs.ai/pricing returns a 404, the sitemap has no pricing page, and the homepage sends new users to "Book demo". Plans, a free tier and feature gating are not public. The judgeval SDK is free under Apache-2.0, but its hosted workflows use a Judgment API key, so you need a quote for the platform. - [Latitude alternatives: what to use instead, and when to stay](https://befailproof.ai/answers/latitude-alternatives/): What are the best alternatives to Latitude for AI agent observability? The main Latitude alternatives are Failproof AI for production failure analysis and fixing, Langfuse for open-source tracing and prompt management, Raindrop for production behavior signals, Judgment Labs for rubric judges, Galileo for built-in evaluation models, and Future AGI for an open-source platform with a gateway. If you only need to replace Latitude V1's gateway, LiteLLM covers that part. - [Latitude pricing: tiers, credits, and the cost of self-hosting](https://befailproof.ai/answers/latitude-pricing/): How much does Latitude cost for AI agent observability? Latitude's managed cloud has three tiers: Starter is free with 20K credits a month, Pro costs $99 a month with 100K credits and Enterprise is custom priced. Every tier has unlimited seats. The platform is also MIT-licensed for self-hosting. The missing piece is a public definition of what consumes a credit, so traffic cannot be converted into a reliable monthly bill from the pricing page alone. - [Latitude self-hosting: what you deploy and what you operate](https://befailproof.ai/answers/latitude-self-hosting/): Can you self-host Latitude, and what does it take to run? Yes. Latitude is MIT-licensed and self-hosts for free: Docker Compose or Docker Swarm on one host, a Helm chart on Kubernetes, or a Railway template, all deploying the same images as Latitude's cloud. You run five application services on Postgres with pgvector, ClickHouse, Redis, Temporal and S3-compatible object storage, and you own backups, upgrades and TLS. The managed alternative runs in AWS eu-central-1, Frankfurt. - [Latitude V1 gateway removed: what changed and what to do now](https://befailproof.ai/answers/latitude-v1-gateway-removed/): Latitude V2 removed the V1 gateway - what should V1 users do now? Latitude V2 removed the V1 AI gateway, PromptL, hosted tools, triggers and server-side conversation storage. Existing V1 accounts still run, but new development is focused on V2 and no shutdown date is published. Latitude recommends moving prompts into your code, calling model providers directly and wrapping those calls with capture(). Use a separate gateway if you still need centralized keys, routing, spend controls or request-time guardrails. - [Migrating agent evals between platforms: what moves and what does not](https://befailproof.ai/answers/migrate-agent-evals-between-platforms/): How do you migrate agent traces, datasets, judges and alerts from one evaluation platform to another? Agent traces, datasets, labels and evaluator code are the most portable parts of an evaluation stack. Historical dashboards, findings, alert state and calibration rarely transfer cleanly. Export everything before the old contract ends, send new traffic to both platforms during an overlap, verify that trace fields survive the move and recalibrate every judge on the new representation. - [Open-source agent eval tools: what is actually open in each](https://befailproof.ai/answers/open-source-agent-eval-tools/): Which agent and LLM evaluation tools are open source, and what exactly is open in each? Open-source agent eval tools come in two sizes. Libraries you run inside your own code: DeepEval (Apache-2.0), Ragas (Apache-2.0), Promptfoo (MIT), Braintrust's autoevals (MIT) and judgeval (Apache-2.0). Platforms you can self-host whole: Langfuse (MIT outside its ee folders), Latitude (MIT), Future AGI (Apache-2.0 plus an enterprise-licensed part) and Arize Phoenix (Elastic License 2.0, source-available). Failproof AI's open source is its MIT local CLI, not its Cloud. - [Questions to ask an agent eval vendor, and what a good answer sounds like](https://befailproof.ai/answers/questions-to-ask-an-agent-eval-vendor/): What should you ask an agent evaluation vendor before you buy? Ask how the platform discovers failures you have not already defined, how it evaluates known problems, what evidence appears behind a finding, who gets alerted and how a finding becomes a tested fix. Then check data handling, exports, SSO, pricing, self-hosting and whether you can test the product on your own failed sessions before buying. - [Raindrop AI pricing: the plans, the meter and the arithmetic](https://befailproof.ai/answers/raindrop-ai-pricing/): How much does Raindrop cost for AI agent monitoring? Raindrop has three plans. Hobby is free for 1,000 events a month, and ingestion stops at the cap. Pro is $299 a month with a 14-day trial, plus $0.003 per event up to 1 million and $0.002 after, with no events included. Enterprise is custom. An event is one agent turn, so 100,000 events cost $599 a month and 1 million events cost $3,299. - [Raindrop alternatives: seven options and who each fits](https://befailproof.ai/answers/raindrop-alternatives/): What are the best alternatives to Raindrop for monitoring AI agents? The main Raindrop alternatives are Failproof AI for finding production failures and turning tested fixes into policies, Latitude for an MIT-licensed self-hosted platform, Judgment Labs for rubric judges, Galileo for built-in evaluations, Future AGI for gateway guardrails, Langfuse for open-source tracing and evals, and Sentry if your team already uses it. Stay on Raindrop if you rely on Experiments or Signals; none of these has the same cohort comparison. - [Security review checklist for agent observability vendors](https://befailproof.ai/answers/security-review-checklist-for-agent-observability-vendors/): What should a security review ask an agent observability or evaluation vendor? A security review of an agent observability vendor should cover nine areas: what data leaves your machines and when, where PII and secrets are redacted, retention and deletion, which tier includes SSO/SAML, RBAC, audit logs, self-host or on-prem options, compliance reports such as SOC 2, and subprocessors, including any model provider a hosted judge sends transcripts to. Treat agent traces as sensitive by default: they hold tool inputs and outputs, not just prompts. - [Self-hosted agent evaluation platforms: what you can run yourself today](https://befailproof.ai/answers/self-hosted-agent-evaluation-platforms/): Which agent evaluation platforms can you self-host today, and on what license or tier? Langfuse, Latitude and the Apache-2.0 parts of Future AGI can be self-hosted without a platform licence fee. Arize Phoenix is self-hostable under the source-available Elastic License 2.0. Braintrust, LangSmith, Galileo and Failproof AI offer Enterprise deployments. Judgment Labs lists platform self-hosting as coming soon, while Raindrop limits VPC deployment to selected partners. - [Self-service agent evaluation platforms: pricing, trials and sales calls](https://befailproof.ai/answers/agent-eval-platform-without-a-sales-call/): Which agent evaluation platforms are self-service? You can start self-service at a published price with Failproof AI (free tier, Team $99/month), Langfuse (free Hobby, Core $29/month), Latitude (free Starter, Pro $99/month), Braintrust (free Starter, Pro $249/month), Raindrop (free Hobby, Pro $299/month with a 14-day trial), LangSmith and Future AGI. Galileo's Free tier is self-service, but its Pro tier starts with a demo. Judgment Labs routes all new users to a demo, and every Enterprise tier is sales-led. - [Sentry for AI agents: error tracking for failures that throw nothing](https://befailproof.ai/answers/sentry-for-ai-agents/): What does "Sentry for AI agents" mean, and which tools actually do it? "Sentry for AI agents" means bringing the error-tracking loop to failures that do not throw exceptions: capture the full session, detect bad behavior, group related failures, alert an owner and track the fix. Sentry covers agent traces, tool errors, latency and cost. Behavioral failures such as an incorrect result, skipped step or unsafe action need evaluations or failure analysis on top of those traces. - [What is agent behavior monitoring, and what can it not do?](https://befailproof.ai/answers/what-is-agent-behavior-monitoring/): What is agent behavior monitoring? Agent behavior monitoring analyzes production traces to understand what agents actually do. Code-based and LLM-based evaluations measure known behaviors, while failure analysis groups related evidence to surface recurring problems the team did not already encode as a check. The useful output is a finding with affected sessions, evidence, severity and a recommended fix, followed by alerts and verification that the change worked. Runtime policies are an optional control for specific high-risk actions. ## Learn: agent evals and LLM judges How to set up judges that score agents, calibrate them against human labels, run them on production traffic, and turn what they find into runtime policies. - [Async evaluators for long LLM judgments](https://befailproof.ai/learn/async-evaluators-for-long-judgments/): How do you run LLM judgments that take minutes rather than seconds? Run a long judgment as async code that fans its model calls out concurrently with a cap on parallelism, puts a time limit on the whole job, and caches each result by model, rubric and prompt. That makes interrupted jobs safe to retry and avoids paying twice for completed work. In Failproof AI, Cloud runs LLM evaluations and your existing evaluation suite when a session finishes. - [Evaluate AI agent policy compliance: what to check and what to block](https://befailproof.ai/learn/judge-agent-safety-and-policy-compliance/): Should you judge agent policy compliance after the fact or block violations before they happen? Sort each agent rule by the evidence needed to check it. Rules visible in one tool call, such as "never force-push" or "no refund over $200", can be checked before the call and blocked when the consequence is serious. Sequence rules, such as "run tests after the last edit", need the ordered trace. Rules about meaning, such as "do not claim work you did not complete", need an LLM-based evaluation or human review; user-facing text can also pass through an application-level output check before delivery. - [How many eval examples do you need?](https://befailproof.ai/learn/how-many-eval-examples-do-you-need/): How many examples do you need for an LLM or agent evaluation to be trustworthy? Start with 50 representative examples to find obvious failures, use about 200 when you need a useful estimate of a pass rate near 90%, and expect hundreds more when comparing versions only a few points apart. The right number is the smallest sample whose confidence interval is narrower than the decision you need to make. At an observed 90% pass rate, 50 independent examples give a 95% Wilson interval of 78.6% to 95.7%; 200 narrow it to 85.1% to 93.4%. - [How to alert on eval score drops with Failproof AI](https://befailproof.ai/learn/alert-on-eval-score-drops/): How do I alert when my agent's evaluation scores drop? Open Analyze → Alerts in Failproof AI and create an evaluation_score alert for one important evaluation, or an eval_compound alert when several results should fail together. Set the severity and delivery route, test the alert, and investigate the resulting incident under Analyze → Issues. Failproof can notify by email, Slack, webhook or the dashboard. - [How to audit agent sessions for failure patterns with Failproof AI](https://befailproof.ai/learn/audit-agent-sessions-for-failure-patterns/): How do you find failure patterns across many AI agent sessions? Create a Failproof AI Cloud audit with one clear goal, the agents and environments to include, a time window and a cadence from one hour to seven days. The audit combines trace evidence, evaluation results and policy decisions, then groups related failures into findings with severity, affected sessions and a recommended fix. Review the evidence, turn confirmed findings into assigned issues and rerun the audit to verify the change. - [How to build a Failproof AI evaluation in Python](https://befailproof.ai/learn/build-a-failproof-evaluator-in-python/): How do you build a Failproof AI evaluation in Python? Open Analyze → eval authoring and describe the check in plain English. The assistant drafts a hosted Python code check - one expression, no imports and no network - that you test against up to 10 real sessions before enabling it. Failproof AI runs the evaluation in the cloud alongside LLM judges and the evaluation suite you already have. The CLI does not run evaluations locally; it reads and manages Cloud results. - [How to build an eval dataset from production traces](https://befailproof.ai/learn/build-an-eval-dataset-from-production-traces/): How do I build an evaluation dataset for my agent from production traces? Build it from three buckets of real sessions: failures (low scores, tool errors, escalations), edge cases (rare agents, long or unusual sessions) and a random slice for balance. Scrub personal data before anything is stored, label each case against a written rubric, then freeze the set as an immutable, hashed version. Never edit a version in place: add cases in the next one, so scores stay comparable. - [How to compare two agent versions](https://befailproof.ai/learn/compare-agent-versions-with-evals/): How do you compare two versions of an AI agent? Run the current and candidate versions in separate environments and send both sets of sessions to Failproof AI with distinct agent IDs and environment values. Apply the same code-based and LLM-based evaluations to both, run audits with the same goal and window, and use saved queries and dashboards to compare scores, cost, latency and recurring failure modes over time. Open the sessions behind each difference before deciding whether to promote the candidate. - [How to debug a failing LLM evaluation](https://befailproof.ai/learn/debug-a-failing-llm-judge/): How do I debug an LLM evaluation that gives wrong or missing scores in Failproof AI? Start in Observe → evaluations and separate missing results from wrong results. Query the affected sessions by agent, environment and evaluation version, then run a Failproof audit to find what the failures share. Review the trace evidence, give feedback on the finding and update the evaluation. Re-evaluate individual sessions while iterating, backfill the accepted version across historical sessions, and alert if missing or incorrect scores return. - [How to detect silent agent failures in production](https://befailproof.ai/learn/detect-silent-agent-failures-in-production/): How do I detect silent AI agent failures in production with Failproof AI? Send complete production sessions to Failproof AI, including tool calls and results. Add code-based evaluations for exact failures and LLM-based evaluations for semantic failures such as unsupported success claims. Then run audits across the session population to discover recurring patterns outside those known checks. Review each finding, assign the fix, alert on recurrence and keep the evaluations running to verify recovery. - [How to evaluate a RAG agent for groundedness](https://befailproof.ai/learn/evaluate-rag-agents-for-groundedness/): How do you evaluate a RAG agent for groundedness with Failproof AI? Send the RAG agent's question, retrieved chunks, tool results and final answer to Failproof AI in the same session trace. Run separate evaluations for citation validity, groundedness, retrieval relevance and answer completeness. Then use audits to group failed sessions into recurring findings, such as stale documents, missing index coverage or a query rewrite that selects the wrong source. Query and dashboard the results by agent and environment, fix the retrieval or generation path, and keep the evaluations running to verify recovery. - [How to evaluate agent cost and latency against a budget](https://befailproof.ai/learn/evaluate-agent-cost-and-latency-budgets/): How do you evaluate an AI agent against cost and latency budgets? Measure cost per successful task and p50 and p95 latency for each task type, not only cost per model call or average response time. In Failproof AI, query complete production sessions by agent and environment, save the results to dashboards and alert when a budget is exceeded. Then audit the affected sessions to find recurring causes such as retry loops, oversized context, unnecessary tool calls or a slow dependency, fix the agent and keep the same measurements running to verify recovery. - [How to evaluate agent trajectories, not just final answers](https://befailproof.ai/learn/evaluate-agent-trajectories-not-just-final-answers/): How do you evaluate an AI agent trajectory instead of only its final answer? Capture the complete agent session, including prompts, model turns, tool calls, tool results, errors and the final response. Run code-based evaluations for exact trajectory facts and LLM-based evaluations for behavior that requires context. Then use Failproof audits to find recurring and previously unknown failure patterns across sessions, inspect the supporting traces, fix the prompt, model, tools or workflow, and keep the same evaluations running to verify that behavior improved. - [How to evaluate agents without ground truth](https://befailproof.ai/learn/evaluate-agents-without-ground-truth/): How do you evaluate an AI agent when there is no ground-truth answer? Define an agent contract describing the task, required behavior and unacceptable outcomes. Send complete sessions to Failproof AI, then use code-based evaluations for exact trace evidence and LLM-based evaluations for criteria such as task completion, instruction following and groundedness. Run audits to discover recurring failures outside those checks, review the supporting sessions, give feedback on findings and update the evaluations. Track the accepted criteria with queries, dashboards and alerts to verify improvement in production. - [How to evaluate an agent after a model upgrade](https://befailproof.ai/learn/evaluate-agents-after-a-model-upgrade/): How do you evaluate an AI agent after upgrading its model? Run the current and upgraded models in separate environments with distinct agent IDs. Send complete sessions from both to Failproof AI, apply identical code-based and LLM-based evaluations, and run audits with the same goals and windows. Compare completion, behavior, cost, latency and failure findings through saved queries and dashboards. Inspect the affected traces, then promote the new model gradually while alerts and audits continue watching for regressions. - [How to evaluate an AI customer support agent](https://befailproof.ai/learn/evaluate-customer-support-agents/): How do you evaluate an AI customer support agent in production? Send the complete support session to Failproof AI, including the customer request, retrieved context, tool calls, tool results and final response. Use code-based evaluations for exact requirements and LLM-based evaluations for resolution, groundedness, policy adherence and escalation quality. Run audits across production sessions to find recurring failures outside those checks, assign the findings, fix the prompt, tools, knowledge or workflow, and keep dashboards and alerts running to verify improvement. - [How to evaluate coding-agent sessions](https://befailproof.ai/learn/evaluate-coding-agent-sessions/): How do you evaluate Claude Code, Codex and other coding-agent sessions? Connect the coding agent to Failproof AI, then evaluate the complete session rather than only its final message or diff. Use code-based checks for facts such as whether tests ran after the last edit, and LLM-based checks for contextual questions such as whether the task was completed. Run audits across sessions to discover recurring failure modes, inspect the supporting traces, alert the owner and use a tested policy only when a known high-risk action should be stopped before execution. - [How to evaluate multi-agent systems](https://befailproof.ai/learn/evaluate-multi-agent-systems/): How do you evaluate a multi-agent system and find which agent caused a failure? Evaluate a multi-agent system at three levels: the system outcome, each agent against its own job given the input it received, and every handoff between agents. Validate handoff messages against a schema in code, judge whether each handoff kept the user's constraints, and attribute failures by replaying with one agent's output corrected. Treat LLM-based blame as a lead: in a 2025 benchmark the best method named the responsible agent 53.5% of the time. - [How to evaluate tool selection and tool calls in AI agents](https://befailproof.ai/learn/evaluate-tool-selection-and-tool-calls/): How do you evaluate whether an AI agent chose the right tools and called them correctly? Evaluate tool calls on four questions: should the agent have called a tool at all, did it pick the right one, were the arguments valid and grounded in the request, and did it use the result. Check arguments against each tool's JSON schema and ordering rules in code, measure selection precision and recall on labeled turns, and use an LLM judge for necessity and result use. - [How to judge multi-turn agent conversations](https://befailproof.ai/learn/judge-multi-turn-agent-conversations/): How do you evaluate a multi-turn conversation with an AI agent, including sessions too long for the judge? Score multi-turn conversations at two levels. A session-level judge reads the whole transcript for goal met, consistency, repeated questions and honoured corrections; a turn-level judge scores each assistant turn using only the history before it. When a session exceeds the judge's context, keep the first and last turns verbatim, summarize the middle while preserving corrections, errors and commitments, and cover the rest with code checks. - [How to measure hallucination in AI agent runs](https://befailproof.ai/learn/measure-hallucination-in-agent-runs/): How do you measure hallucination in AI agent runs? Measure agent hallucination as separate rates. Unknown tools and unsupported action claims can be checked from tool events; missing files and IDs with no observed provenance are useful signals that still need context. Invented factual claims in prose need an LLM-based check that compares each claim only with evidence available in the session. Report the categories separately so each one points to a specific fix. - [How to reduce the cost of LLM-as-a-judge evals](https://befailproof.ai/learn/reduce-llm-judge-cost/): How do I reduce the cost of running LLM-as-a-judge evaluations on my agents? For online evaluations, run inexpensive code checks broadly, then use session filters and a representative sample to decide which sessions need an LLM-based evaluation. Next trim the trace to relevant evidence, combine criteria that use the same context, cache identical inputs and batch non-urgent offline work. Move to a smaller model only after it agrees with your human labels. - [How to regression test AI agents before you deploy](https://befailproof.ai/learn/regression-test-agents-before-deploy/): How do I regression test an AI agent before deploying a new version? Build a frozen set from real production failures and representative successful sessions, run those inputs through the candidate agent in staging, and send the resulting sessions to Failproof AI. Apply the same code-based and LLM-based evaluations used in production, compare the candidate with the current version case by case, and inspect every regression in session replay. Block deployment when a must-pass case fails, a critical score crosses its floor or the candidate creates a meaningful cluster of new failures. - [How to run agent evals in CI with Failproof AI](https://befailproof.ai/learn/run-agent-evals-in-ci/): How do I run LLM agent evals in CI and fail the build on a regression? Have CI run a fixed smoke set through the candidate agent in staging and send those sessions to Failproof AI. Cloud evaluations apply the same code-based and LLM-based checks used in production, while session replay shows why individual cases failed. Gate the release on must-pass cases, absolute score floors and incomplete evaluation runs; run the larger set nightly or before release. - [How to run agent evaluations without a data team](https://befailproof.ai/learn/agent-evals-for-teams-without-a-data-team/): How can a small team run agent evaluations without a data or ML team? Start with three code checks: tool errors, one rule the agent must never break, and a step or cost limit. Add one LLM judge for task completion, then compare its verdicts with about 50 sessions reviewed by the person who owns the agent. Alert on the result and block the most damaging repeatable action. A small team can do this without a custom dashboard or a large metric suite. - [How to sample production traffic for agent evals](https://befailproof.ai/learn/sample-production-traffic-for-evals/): How should I sample production agent sessions for LLM-judge evaluation? Run cheap code checks on every session and send only a sample to the LLM judge: a fixed random rate for an unbiased trend line, stratified quotas so small agents get enough judged sessions, and triggers that always judge sessions with errors or failed code checks. Decide by hashing the session id, so a retried request makes the same choice, and reweight oversampled groups before you report a pass rate. - [How to score task completion for AI agents](https://befailproof.ai/learn/score-task-completion-for-ai-agents/): How do you measure whether an AI agent actually completed its task? Measure task completion by writing down what "done" means for each agent, then checking the end state rather than the agent's claim: the tests pass and the change is committed, the refund exists in billing, the ticket is resolved or escalated. Where no state can be checked, have an LLM judge apply the same definition. Report completion as a binary rate and keep partial progress as a separate diagnostic. - [How to set up LLM judges for AI agents, step by step](https://befailproof.ai/learn/set-up-llm-judges-for-ai-agents/): How do I set up LLM judges to evaluate my AI agents? Set up an LLM judge by choosing one clear criterion, writing a rubric based on evidence visible in the agent trace, returning a structured verdict, and testing the judge against human-labeled sessions. Once calibrated, run it on production traffic, monitor pass rates and investigate recurring failures before changing prompts, tools or behavioral policies. - [How to turn agent eval results into runtime guardrails](https://befailproof.ai/learn/turn-judge-findings-into-runtime-policies/): How do you turn agent eval results into runtime guardrails? Turn an evaluation finding into a runtime policy when it identifies a repeatable, high-risk action that can be recognized before execution: a destructive command, an edit to a protected file or a refund above a limit. Use the finding's trace evidence to write the narrowest practical check, backtest it against past calls, run it in observe mode, and then enforce it with a message that helps the agent recover. Keep the original evaluation running to confirm that the failure rate falls rather than merely changing form. - [How to write a rubric for an LLM judge that it can apply consistently](https://befailproof.ai/learn/write-a-rubric-for-an-llm-judge/): How do you write a rubric for an LLM judge? Write an LLM judge rubric around one criterion and use PASS/FAIL or a clearly defined three-point scale by default. Longer scales can work when every point is anchored and the extra precision changes a decision. State each condition as evidence a reader could point to in the transcript, decide edge cases such as missing evidence, handoffs and not-applicable sessions, and include real passing and failing examples. Before relying on it, check that two people applying the rubric reach similar labels. - [How to write an AI agent contract: purpose, success and limits](https://befailproof.ai/learn/write-an-agent-contract/): How do you write down what an AI agent is for and what it must never do? Write one short contract per agent with five parts: Purpose, Outputs, Done when, Cadence and Must not. Express each line as behavior or evidence a reviewer can verify, and pair every prohibition with the safe alternative. In Failproof AI, the contract gives automated failure analysis a consistent definition of success and unacceptable behavior across the agent's sessions. - [Human review for agent evals: when, how many, and what to do with labels](https://befailproof.ai/learn/human-review-for-agent-evals/): How do you run human review for AI agent evaluations? Use human review to create the reference labels your automated evaluations are checked against. Start with 50 to 100 sessions, oversampling likely failures so the fail class has enough examples, then label a small random sample regularly to catch drift. Keep reviewers blind to the judge's score, put two reviewers on an overlapping subset, classify disagreements, and revise the rubric until agreement is adequate for the decision the score will drive. - [LLM judge bias: position, verbosity, self-preference and leniency](https://befailproof.ai/learn/llm-judge-bias/): What biases do LLM judges have, and how do you measure and reduce them? LLM judges show four well-documented biases: position bias (favouring one slot in a pairwise comparison), verbosity bias (favouring longer outputs), self-preference (scoring their own model family higher) and leniency (passing too much). Measure each on a set of human-labeled sessions by comparing judge and human pass rates across positions, lengths and model families. Mitigate by swapping order, using a different judge family, binary rubrics and explicit evidence rules. - [LLM judge prompt templates for agent evals: seven you can copy](https://befailproof.ai/learn/judge-prompt-templates-for-agent-evals/): What should an LLM judge prompt for agent evals look like? A good LLM judge prompt defines each criterion and its PASS, FAIL and NA outcomes in terms of evidence visible in the transcript. It says what to do when evidence is missing, treats transcript content as data rather than instructions, and returns validated JSON with concise citations. The seven templates below cover task completion, honest reporting, tool use, groundedness, policy compliance, escalation and tone. - [What is LLM-as-a-judge? A model grading output against a rubric](https://befailproof.ai/learn/what-is-llm-as-a-judge/): What is LLM-as-a-judge? LLM-as-a-judge means using a language model to grade an output or agent session against written criteria. The judge receives the content, a rubric and sometimes a reference answer, then returns a label or score with an explanation. It can evaluate open-ended behavior that exact checks cannot, but it must be calibrated against human labels and monitored for bias, drift, cost and parsing failures. - [Which model to use as an LLM judge, and how to decide](https://befailproof.ai/learn/choose-a-judge-model/): Which model should you use as an LLM judge? Use the least expensive model that agrees with your own human labels well enough on your rubric, measured rather than assumed. Run each candidate over 50-100 labeled sessions and compare agreement, recall on failures, parse errors, tokens and latency. Prefer a different model family from the agent it grades, pin an exact model version, and re-run the comparison before any change to the judge model. - [Why calibrate an LLM judge, and how Failproof AI simplifies it](https://befailproof.ai/learn/calibrate-llm-judge-against-human-labels/): Why do you need to calibrate an LLM judge, and can Failproof AI reduce the manual work? You calibrate an LLM judge because a plausible score is not automatically a correct one. Rubrics can be ambiguous, evidence can be missing, and production behavior changes over time. The traditional solution is a manually labeled dataset and repeated agreement testing. Failproof AI shortens that loop: audits find recurring failure patterns, reviewers confirm or reject the findings, that feedback improves the relevant evaluations, and the updated versions are checked against historical and new sessions. ## Jev, RLCD, evals and policies Source-backed guides to TypeSafe AI's Jev System One model: what it is, what RLCD and calibrated decisions mean, how Jev can evaluate completed agent sessions, and how a typed Jev decision can serve as one signal inside an explicit runtime policy. - [Jev: typed AI decisions, evals and policies](https://befailproof.ai/jev/): What is Jev? Jev is TypeSafe AI’s first System One model. It reads text or structured text state, answers caller-defined Choice, Score or Noul questions, and returns typed decisions with probabilities. Jev does not generate prose, write code or replace an agent’s LLM. Its useful role is a fast semantic decision inside software, including agent evaluations and carefully tested pre-action policy checks. - [What is RLCD? Reinforcement learning for calibrated decisions](https://befailproof.ai/jev/rlcd/): What is RLCD? RLCD means reinforcement learning for calibrated decisions. TypeSafe AI uses the term for training models such as Jev to return bounded decisions and probabilities rather than generated text. Calibration is a property measured across groups of predictions: events assigned probability 0.8 should occur about 80% of the time. It does not guarantee that any individual 0.8 answer is correct. - [Jev evals: using typed decisions to evaluate AI agents](https://befailproof.ai/jev/evals/): Can Jev evaluate AI agents? Yes. Jev can evaluate a completed agent session when the criterion has a bounded answer: a Noul for whether a condition held, a Choice for one outcome category, or a Score for an ordered rubric. A useful Jev eval stores the full distribution, model and question version beside trace evidence, then validates thresholds against human labels or downstream outcomes. - [Jev policies: using typed decisions in runtime guardrails](https://befailproof.ai/jev/policies/): Can Jev enforce an AI agent policy? Jev can supply a semantic decision to a runtime policy, but Jev does not enforce the policy itself. The surrounding policy code decides whether to allow, observe, review or block an action. Keep exact permissions and invariants deterministic, calibrate Jev thresholds on representative outcomes, define failure behavior, and backtest in observe mode before enforcing consequential actions. - [What is Jev? TypeSafe AI’s typed decision model](https://befailproof.ai/jev/what-is-jev/): What is Jev? Jev is TypeSafe AI’s first System One model, built for software that needs a bounded semantic decision rather than generated text. A caller sends relevant text state and one or more Choice, Score or Noul questions. Jev returns typed answers and probabilities that code can inspect, threshold and combine. It does not write prose, explain reasoning or execute actions. - [What is TypeSafe AI? The company behind Jev](https://befailproof.ai/jev/typesafe-ai/): What is TypeSafe AI? TypeSafe AI is the AI company that develops Jev, its first System One model. The company focuses on machine-consumed intelligence: software sends text state and typed questions, and Jev returns bounded decisions with probabilities. TypeSafe publishes a hosted API, Python and JavaScript SDKs, documentation, cookbooks and an agent skill. Jev itself is a proprietary hosted model, not an open-weight release. - [What is a System One model? Jev’s model category explained](https://befailproof.ai/jev/system-one-model/): What is a System One model? System One model is TypeSafe AI’s name for an AI model that makes fast, focused decisions for software. It evaluates text state against bounded typed questions and returns decisions with probabilities instead of generating prose or a reasoning trace. Jev is TypeSafe’s first System One model. Code supplies the workflow, exact rules and actions around those judgments. - [Jev primitives: Choice, Score and Noul explained](https://befailproof.ai/jev/primitives/): What are the Jev primitives? Jev has three typed question primitives. Choice selects one option from a defined set and returns every option’s probability plus confidence. Score rates state against ordered descriptive levels and returns an expected score, level probabilities and confidence. Noul evaluates one yes/no condition and returns the probability of yes. Choose by the meaning of the answer, not the desired UI. - [Jev limitations: known jagged edges in Jev 1.13](https://befailproof.ai/jev/limitations/): What are Jev’s limitations? Jev 1.13 is designed for focused semantic judgments, not generation or exact computation. TypeSafe documents weaknesses with literal wording, counting, arithmetic, numeric representations, date comparison, multi-hop indirection, long irrelevant state, adversarial content, contradictory criteria and assumed probability identities. Keep deterministic work in code, minimize state, write direct criteria and test every consequential decision on representative data. - [Jev pricing, context window and rate limits](https://befailproof.ai/jev/pricing/): How much does Jev cost? TypeSafe lists Jev 1.13 at $42 per billion input tokens, equal to $0.042 per million, as verified September 22, 2026. Output tokens are free. The documented direct-API limits are 250,000 tokens per second and 1,200 requests per minute, but TypeSafe says those limits are adjusting dynamically. Gateway pricing and limits can differ from the direct API. - [Jev AI vs JEV: the model and virus are unrelated](https://befailproof.ai/jev/jev-ai-vs-japanese-encephalitis-virus/): Does JEV mean Jev AI or Japanese encephalitis virus? “Jev” in AI usually means TypeSafe AI’s System One decision model. Uppercase “JEV” in medicine means Japanese encephalitis virus, a mosquito-borne flavivirus that can cause Japanese encephalitis. The two entities are unrelated. For model documentation use TypeSafe AI sources; for symptoms, vaccination, travel or public-health guidance use WHO and qualified medical authorities. - [Jev confidence vs probability: what each number means](https://befailproof.ai/jev/confidence-vs-probability/): What is the difference between Jev confidence and probability? In Jev, probability belongs to an answer outcome. Choice returns one probability per option, Score returns one per rubric level, and Noul returns the probability of yes. Choice and Score also return confidence, which summarizes how concentrated their distribution is. Confidence does not prove correctness, data sufficiency or permission to act, and Noul has no separate confidence field. - [Jev as an LLM judge: where typed evaluation fits](https://befailproof.ai/jev/evals/jev-as-llm-judge/): Can Jev replace an LLM-as-a-judge evaluator? Jev can replace an LLM judge when the evaluation is a bounded Choice, Score or yes/no decision and a prose explanation is unnecessary. It is not a universal judge replacement: open-ended critique, evidence narration and complex reasoning still suit generative models. Compare both on identical labeled sessions, including calibration, repeatability, coverage, latency, cost and consequential error rates. - [Jev evals vs Jev policies: measurement before enforcement](https://befailproof.ai/jev/evals-vs-policies/): What is the difference between a Jev eval and a Jev policy? A Jev eval scores completed behavior and helps discover or measure failure patterns. A Jev-backed policy uses a typed semantic judgment before an action, then explicit policy logic decides whether to allow, observe, review or block. Start with the eval, validate it against outcomes, identify a narrow preventable action, backtest the proposed policy and observe it before enforcement. - [Jev use cases: where typed AI decisions fit](https://befailproof.ai/jev/use-cases/): What can you build with Jev? Jev fits software that repeatedly needs a bounded semantic decision: classification and routing, rubric scoring, claim verification, RAG context selection, agent evaluation, tool-call screening, data labeling and realtime interface choices. The strongest designs keep exact computation and control flow in code, use Jev only for language understanding, retain uncertainty, and measure decisions against real outcomes. - [Jev projects and X threads: a source-graded ecosystem tracker](https://befailproof.ai/jev/research/community-projects/): What are developers building and discussing with Jev? Public Jev projects cluster around model and skill routing, agent evaluation, guardrails, retrieval, email and log triage, semantic search, browser control, data labeling and open Jev-compatible models. Treat repositories and X threads as leads, not validation. Verify a real Jev API call, runnable tests, dataset, license and claim methodology before citing performance or adopting a project. - [What is a Jev Noul? Yes probability without a Boolean shortcut](https://befailproof.ai/jev/primitives/noul/): What is a Noul in Jev? A Noul is Jev’s yes/no question primitive. It evaluates one condition against the supplied state and returns noul, the probability that the answer is yes or the statement is true, from 0 to 1. A Noul is not a Boolean, has no separate confidence field and needs an application-owned threshold calibrated for the specific decision and consequence. - [Jev Choice: selecting among typed options](https://befailproof.ai/jev/primitives/choice/): What is a Choice question in Jev? Choice is the Jev primitive for selecting one option from a caller-defined set. Its response contains the selected key, a probability for every listed option and confidence describing distribution concentration. Choice is relative: it finds the best listed alternative even when every option is poor, so include a no-match choice or a separate fit check whenever the candidate set may be incomplete. - [Jev Score: rating against an ordered rubric](https://befailproof.ai/jev/primitives/score/): What is a Score question in Jev? Score is the Jev primitive for rating state against caller-defined ordered levels. It returns a probability for each level, confidence and a probability-weighted expected score that can fall between level indices. Each level should describe a concrete semantic situation. Jev 1.13 Score is not intended for arithmetic, counting or reconstructing an exact numeric value. - [Jev alternatives: LLMs, classifiers, rules and open models](https://befailproof.ai/jev/alternatives/): What are the alternatives to Jev? Jev alternatives depend on the decision. Use deterministic rules for exact facts, a trained classifier or reranker for stable high-volume tasks with labels, a structured-output LLM for flexible judgments or explanations, and humans for novel or consequential ambiguity. Community Jev-compatible models offer local experimentation, but matching Jev’s API shape does not establish matching weights, RLCD training, calibration or quality. - [RLCD vs RLHF vs RLVR: different post-training objectives](https://befailproof.ai/jev/rlcd-vs-rlhf-vs-rlvr/): How do RLCD, RLHF and RLVR differ? TypeSafe describes RLCD as reinforcement learning for calibrated decisions: training toward bounded decisions and probabilities. RLHF trains toward responses people prefer, while RLVR rewards outcomes that can be verified, often in reasoning tasks. These are different objectives, not a universal quality ranking. A production system may use an RLHF model for conversation, an RLVR model for hard reasoning and Jev for repeated typed judgments. - [When not to use Jev](https://befailproof.ai/jev/when-not-to-use-jev/): When should you not use Jev? Do not use Jev when code can compute the answer exactly, when the output must be generated rather than selected, or when the task requires several dependent reasoning steps. Jev 1.13 is also a poor default for counting, arithmetic, date comparison, long irrelevant context and adversarial state. Use rules, parsers, specialist models, reasoning LLMs or human review instead. - [Jev launch tracker: models, SDKs, providers and verified changes](https://befailproof.ai/jev/research/launch-tracker/): What is the current status of Jev? TypeSafe AI listed jev-1.13.0 as Jev’s stable model when checked September 22, 2026. Since then, OpenAI announced a Decisions API, Cloudflare launched Clef and Clef-flash, and Perplexity documented its own Decisions API. Laya and Kev-9B have published independent model cards. These are alternatives, not Jev versions. Public OpenAI endpoint details remained unverified October 2, 2026; Jev evals and policies are live for Failproof beta users. - [How does Jev work? Requests, parallel decisions and runtime architecture](https://befailproof.ai/jev/how-does-jev-work/): How does Jev work? Jev receives a shared text or structured-text state plus independent Choice, Score or Noul questions. TypeSafe says its new architecture and parallel sampler evaluate those bounded questions without autoregressively generating prose. Jev returns typed answers and probability distributions. Application code then applies validation, permissions, thresholds, fallbacks and actions; Jev does not execute the workflow. - [Is Jev an LLM? The precise answer and architectural boundary](https://befailproof.ai/jev/is-jev-an-llm/): Is Jev an LLM? Jev processes natural-language state, but TypeSafe does not position it as a general-purpose generative LLM. Jev is a System One decision model: it answers bounded Choice, Score and Noul questions with probabilities instead of producing an open-ended token sequence, explanation or tool call. Use an LLM for generation and dependent reasoning; use Jev for repeated semantic decisions that code can measure. - [Is Jev generative AI? Why typed decisions are different](https://befailproof.ai/jev/is-jev-generative-ai/): Is Jev generative AI? Jev is not a generative-AI interface: it does not produce prose, code, images or arbitrary extracted values. A caller defines a bounded Choice, Score or Noul answer space, and Jev returns a typed decision with probabilities. A generative LLM can be constrained to JSON, but it still generates tokens; output schema alone does not make that system equivalent to Jev. - [Jev model card: version, limits, price and known behavior](https://befailproof.ai/jev/jev-model-card/): What are Jev’s current model specifications? As verified September 22, 2026, TypeSafe lists jev-1.13.0 as the current stable Jev model; jev-latest and jev-preview both resolve to it. The direct API is text-only, accepts 64k total tokens with a 32k state-plus-longest-question limit, charges $0.042 per million input tokens, and returns output tokens free. Published rate limits are dynamic, and documented Jev 1.13 weaknesses still require task-specific evaluation. - [jev-latest vs jev-preview: aliases, pinning and safe upgrades](https://befailproof.ai/jev/jev-latest-vs-jev-preview/): Should I use jev-latest, jev-preview or a pinned Jev version? Use jev-latest for low-risk exploration, jev-preview only when deliberately testing the preview channel, and a pinned ID such as jev-1.13.0 for calibrated evaluators or consequential policies. As verified September 22, 2026, both aliases resolve to jev-1.13.0; that temporary equality does not make them interchangeable contracts. Log the resolved model and replay labeled traffic before accepting an alias move. - [Who created Jev? TypeSafe AI, provenance and primary sources](https://befailproof.ai/jev/who-created-jev/): Who created Jev? TypeSafe AI created and serves Jev, its first System One model. Its launch article is written by founder and CEO Diogo Almeida and dated September 15, 2026. TypeSafe’s team page also names co-founders Sasha Sheng, COO, and Erik Gafni, CTO. The company maintains Jev’s documentation, hosted API, official Python and JavaScript SDKs, and agent skill; the model weights are not public. - [Machine-native intelligence: TypeSafe AI’s architecture thesis](https://befailproof.ai/jev/machine-native-intelligence/): What does TypeSafe AI mean by machine-native intelligence? Machine-native intelligence is TypeSafe AI’s term for model output designed to be consumed directly by software rather than read first by a person. With Jev, an application supplies state and bounded typed questions and receives decisions with probabilities. The useful engineering idea is a narrow measurable component; it does not imply autonomous action, deterministic correctness or a standardized research category. - [What does Jev mean? The William Stanley Jevons origin](https://befailproof.ai/jev/jev-name/): What does Jev stand for? TypeSafe says Jev is named after the nineteenth-century economist and logician William Stanley Jevons. The company’s launch article does not present J-E-V as an acronym, so invented expansions should not be repeated. It is the name of TypeSafe AI’s System One decision model, which returns typed probabilistic judgments for software rather than generated prose. - [Jev calibration: what probabilities mean and how to test them](https://befailproof.ai/jev/calibration/): Is Jev calibrated? TypeSafe says RLCD trains Jev for calibrated decisions, but calibration is not a permanent property of the model or a guarantee about one answer. It must be measured for the exact model, question, criteria, state projection and deployment population. Group predictions into probability ranges, compare predicted and observed frequencies, report proper scoring and decision metrics, and repeat by operationally important slice. - [Jev thresholds: choose act, review and decline bands from risk](https://befailproof.ai/jev/thresholds/): What Jev threshold should I use? There is no universal Jev threshold. Choose thresholds on a held-out calibration set for one versioned question and population, using the cost of false actions, missed actions and human review. Prefer separate act, review and decline bands when consequences differ. Report coverage and error inside each band, preserve raw probabilities, and revalidate after model, prompt, state or traffic changes. - [Jev abstention and human review: designing a selective decision system](https://befailproof.ai/jev/abstention-human-review/): How should Jev escalate uncertain decisions to humans? Abstention means choosing not to automate a decision when the available evidence is too weak or ambiguous. The application can send that case to a human, ask for more information or use a safe fallback. Jev does not provide one universal abstain command: your code defines act, review and fallback regions from tested probability thresholds, while handling service failures separately. - [Jev Choice vs Noul: exclusive classes or independent conditions?](https://befailproof.ai/jev/choice-vs-noul/): Should I use Jev Choice or Noul? Use Choice when exactly one option should win from a defined candidate set. Use one Noul per condition when labels can coexist or when you need an independent yes probability. A Choice is relative and will select a listed option even if none fits; add a no-match option or separate fit Noul. Do not assume a Choice probability equals a separately asked Noul probability. - [Jev Score vs Choice: ordered rubrics or nominal alternatives?](https://befailproof.ai/jev/score-vs-choice/): Should I use Jev Score or Choice? Use Score when the answer levels have a meaningful order, such as low, medium and high urgency. Use Choice when alternatives are nominal, such as billing, technical and account teams. Score returns level probabilities and a probability-weighted numeric summary, but the spacing between rubric levels is a modeling convention—not measured physical distance or permission to average unrelated risks. - [Multiple Jev questions in one call: parallelism and dependencies](https://befailproof.ai/jev/multiple-questions-one-call/): Can Jev answer multiple questions in one API call? Yes. A Jev request can contain multiple questions that share the same state, and TypeSafe says they are evaluated independently and in parallel. Use one call when judgments read the same evidence but do not depend on one another. If answer A changes the evidence, candidates or meaning of question B, make the dependency explicit in application code and issue a second request. - [Test Jev calibration with reliability diagrams, ECE and Brier score](https://befailproof.ai/jev/calibration-ece-brier-score/): How do I measure Jev calibration? Freeze one Jev question and evaluate its probabilities against independently labeled held-out outcomes. Plot a reliability diagram, calculate Brier score and ECE with a published binning rule, add uncertainty intervals, and report coverage and risk at operational thresholds. Repeat by important slice. ECE alone is insufficient because bin choices can hide local error and aggregate calibration can hide subgroup miscalibration. - [Jev quickstart: Python, TypeScript and HTTP architecture](https://befailproof.ai/jev/build/quickstart/): How do I start using Jev? Create a TypeSafe API key, keep it in a server-side TYPESAFE_API_KEY environment variable, and call Jev through the official Python SDK, JavaScript SDK or POST /v1/systemone. Send relevant state, a model ID and named Choice, Score or Noul questions. Preserve the returned model, typed answer distributions and token usage; application code owns thresholds, retries, permissions and actions. - [Jev Python SDK: typed questions, lifecycle and production boundaries](https://befailproof.ai/jev/build/python/): How do I use Jev from Python? Install the official typesafe-sdk package, store the direct-service key in TYPESAFE_API_KEY, and use TypeSafeClient.system_one with typed Choice, Score or Noul question objects. Manage the synchronous client with a context manager, pin the model for evaluated workflows, preserve the complete response, and wrap the call with application-specific timeout, retry, fallback and outcome logging. - [Jev TypeScript SDK: inferred answers, retries and server boundaries](https://befailproof.ai/jev/build/typescript/): How do I use Jev with TypeScript? Install TypeSafe’s official @typesafe-ai/sdk package on Node 20 or newer, keep TYPESAFE_API_KEY in a trusted server runtime, and call TypeSafeClient.systemOne. The SDK infers each answer type from the question object. Pin the model for evaluated workflows, bound the per-attempt timeout and retry policy, and log full answers, resolved model, usage and request IDs. - [Jev HTTP API: request, response and production contract](https://befailproof.ai/jev/build/http-api/): What is the Jev HTTP API request and response format? Send an authenticated JSON POST to https://api.typesafe.ai/v1/systemone with required state, model and nonempty questions. Each named question is a Noul, Choice or Score. The response returns the resolved model, an answers map under the same IDs, and token usage. Validate answer discriminators, retain complete distributions and handle 401, 422, 429 and 529 separately. - [Jev errors, retries and rate limits: design a total failure budget](https://befailproof.ai/jev/build/errors-retries-rate-limits/): How should a Jev integration handle errors, retries and rate limits? Classify Jev failures before retrying. Fix 401 and 422 responses rather than repeating them; back off for 429 rate limits and 529 overload; bound retries for connection, timeout and server failures within the workflow’s total deadline. The current JavaScript SDK defaults to a 10-second timeout per attempt and two retries. Explicitly define fallback behavior and keep failures separate from uncertain model answers. - [How to structure Jev state: evidence projection and context control](https://befailproof.ai/jev/build/state/): How should I structure state for Jev? Treat Jev state as a versioned evidence projection, not a transcript dump. Prefer a JSON object with descriptive field names, include only facts needed by the questions, compute exact values in code, separate trusted policy from untrusted text, and preserve source references. Jev 1.13 loses accuracy with irrelevant long state and can be steered by adversarial content, so test additions with ablations. - [How to write good Jev questions: atomic criteria and regression tests](https://befailproof.ai/jev/build/write-good-questions/): How do I write effective Jev questions? Write one operational judgment per Jev question. State the exact condition directly, choose the primitive that matches the label structure, define observable criteria and boundary cases, and point to named state fields where needed. Avoid double negatives, hidden multi-step reasoning, contradictory criteria and requests for arithmetic. Version the question and evaluate every revision on blind labeled fixtures. - [Jev speculative fan-out: parallel questions without hidden dependencies](https://befailproof.ai/jev/build/speculative-fan-out/): How does speculative fan-out work with Jev? Speculative fan-out sends independent questions for several possible branches in one Jev request, then lets application code consume only the answers relevant to the selected branch. It can remove a sequential model round trip because Jev evaluates questions in parallel, but it computes answers that may be discarded. Use it only when every question can be answered from the original shared state. - [Composite Jev scoring: combine atomic judgments without hiding risk](https://befailproof.ai/jev/build/composite-scoring/): How do I build a composite score from Jev outputs? Ask Jev for separate atomic semantic judgments, normalize only genuinely comparable values, and combine them with explicit code-owned weights. Keep hard vetoes and authorization outside the weighted average, define missing-value behavior, and retain every raw distribution. Fit weights and action thresholds on outcomes; do not treat a composite as a calibrated probability unless that claim is tested separately. - [Build a Jev model router: candidates, confidence and outcome evaluation](https://befailproof.ai/jev/build/model-routing/): How do I route LLM requests with Jev? Filter candidate models in code for availability, capability, policy and budget, then use a Jev Choice to select among the eligible routes and an explicit no-fit or review gate. Execute the chosen model with a bounded fallback and evaluate the completed task. Router probability predicts the configured routing judgment; it is not proof that the selected model succeeded. - [Build Jev RAG reranking: shortlist design, costs and evaluation](https://befailproof.ai/jev/build/rag-reranking/): How do I use Jev to rerank RAG results? Retrieve a small candidate set with BM25, vectors or a hybrid method, then ask Jev one consistent relevance question per query-document pair or one bounded Choice over a shortlist. Preserve document IDs and first-stage scores, sort in code, and compare nDCG, recall and downstream answer quality with the original retriever. A public X field report found one Jev setup did not beat its vector baseline. - [Jev structured extraction: generate candidates, then select](https://befailproof.ai/jev/build/structured-extraction/): Can Jev extract structured data? Jev cannot generate arbitrary strings, dates or identifiers. For extraction, first produce candidates with deterministic parsing, retrieval or a generative structured-output model; validate and normalize them in code; then use Jev Choice or Noul to select or verify a bounded candidate. Include not-stated handling, preserve source spans and evaluate extraction and selection as separate stages. - [Run Jev on Cloudflare Workers AI: request shape and boundaries](https://befailproof.ai/jev/build/cloudflare-workers-ai/): How do I run Jev through Cloudflare Workers AI? Cloudflare Workers AI lists Jev under model ID typesafe/jev. From a Worker, call env.AI.run("typesafe/jev", { state, questions }); through HTTP, post to the account Workers AI run endpoint with model typesafe/jev and the Jev payload under input. Treat Cloudflare credentials, pricing, context, quotas, errors and data path as provider-specific rather than copying TypeSafe direct-API assumptions. - [Jev on OpenRouter: current availability and verification guide](https://befailproof.ai/jev/build/openrouter/): Can I use Jev through OpenRouter? OpenRouter announced Jev in beta and publishes a TypeSafe/Jev route page. However, its public /api/v1/models catalog returned no TypeSafe or Jev entry when checked September 22, 2026, so this page cannot verify a current model ID or request contract. Check the live catalog and make a test request before integrating, or use TypeSafe’s direct API or Cloudflare’s documented route. - [Jev evals in Failproof AI: beta workflow and evidence architecture](https://befailproof.ai/jev/evals/failproof/): Can I run Jev evals in Failproof AI? Yes. Jev evals and policies are live in Failproof AI for beta users. Failproof connects completed agent-session evidence with typed Jev evaluation results, audits and recurring findings, then supports the path from measured failures to runtime policies. Email nikita@befailproof.ai for access or help getting started. - [Online vs offline Jev evals: architecture, latency and feedback loops](https://befailproof.ai/jev/evals/online-vs-offline/): Should Jev evaluations run online or offline? Run Jev online only when its result must influence a current request or immediate operational response. Run it asynchronously or offline for regression suites, backfills, audits, threshold fitting and expensive analysis. Most teams need both: offline labeled evaluation establishes validity, while online observation measures production drift. A low-latency model claim does not eliminate network, retry or action-budget constraints. - [Evaluate agent trajectories with Jev: evidence, criteria and attribution](https://befailproof.ai/jev/evals/agent-trajectories/): How do I evaluate agent trajectories with Jev? Project each agent trajectory into the smallest evidence needed for an atomic criterion, then ask separate Jev questions about completion, tool appropriateness, grounding, recovery or handoff. Preserve message and tool-event references with every result. Keep counts, schemas and permissions deterministic, represent missing evidence explicitly, and evaluate Jev against blind human labels and eventual task outcomes. - [Evaluate agent tool calls with Jev: intent, execution and outcome](https://befailproof.ai/jev/evals/tool-calls/): How do I evaluate agent tool calls with Jev? Use Jev for a bounded semantic question such as whether a proposed tool matches the user’s intent or whether its result supports a claim. Keep tool existence, schema validity, permissions, argument constraints and execution status deterministic. Evaluate proposal, execution and downstream outcome separately, using only evidence available at each stage and preserving trace references. - [Jev RAG groundedness evaluation: claims, evidence and citations](https://befailproof.ai/jev/evals/rag-groundedness/): How do I evaluate RAG groundedness with Jev? Split a generated answer into atomic verifiable claims, pair each claim with its cited passages, and ask Jev a bounded support question such as supported, contradicted, insufficient or not applicable. Preserve citation IDs and full distributions. Evaluate retrieval recall separately because a groundedness judge cannot recover missing evidence, and use humans or an explanatory judge when reviewers need a rationale. - [Evaluate safety and policy compliance with Jev](https://befailproof.ai/jev/evals/safety-policy-compliance/): How can Jev evaluate safety or policy compliance? Translate a written policy into atomic, observable criteria and evaluate each against the smallest relevant trace evidence with a Jev Noul, Choice or Score. Keep permissions, schemas, exact secret patterns and prohibited operations deterministic. Validate adversarial and subgroup errors, preserve distributions and evidence, and never present Jev as a complete safety boundary—TypeSafe documents adversarial steering in Jev 1.13. - [Compare Jev with LLM judges: a controlled benchmark protocol](https://befailproof.ai/jev/evals/compare-llm-judges/): How should I compare Jev with LLM-as-a-judge systems? Give Jev and each LLM judge the same bounded criterion, evidence and blind held-out labels, while preserving each interface’s native output. Compare class errors, calibration, repeatability, coverage at operational thresholds, latency, total cost and explanation usefulness. Pin all versions and publish prompts, questions and raw outputs. A single vendor or LangChain experiment does not establish a universal winner. - [Jev regression testing: versioned fixtures, tolerances and CI gates](https://befailproof.ai/jev/evals/regression-testing/): How should I regression-test a Jev evaluator? Freeze the complete measurement instrument: evidence fixture, state projection, Jev question, criteria, resolved model, provider and decision rule. Assert task behavior on representative and boundary cases, not exact floating-point probabilities. Separate deterministic contract tests from live model tests, choose tolerances on held-out data, cap CI cost and latency, and require human triage before accepting a changed baseline. - [Build a Jev evaluation dataset: sampling, labels, splits and lineage](https://befailproof.ai/jev/evals/build-dataset/): How do I build a reliable dataset for Jev evaluations? Start from one operational criterion and define the unit being labeled. Sample production traffic across important slices, add boundary and adversarial cases, preserve missing-evidence examples, and have reviewers label without seeing Jev. Record disagreements and provenance. Split by leakage groups such as customer, incident or conversation—not random rows—then publish a versioned dataset card and keep final test labels untouched. - [Monitor Jev evaluator drift: distributions, calibration and replay](https://befailproof.ai/jev/evals/monitor-drift/): How should I monitor drift in a Jev evaluator? Log the full Jev distribution with model, provider, question, state-projection and threshold versions. Monitor input mix, missing evidence, answer distributions, confidence, action coverage, overrides and eventual outcomes by slice. Distribution movement is an investigation signal, not proof of quality loss. Confirm drift with representative labels or replay, separate deployment changes from traffic changes, and alert on sustained operational impact. - [Evaluate a Jev evaluator: validity, calibration, robustness and utility](https://befailproof.ai/jev/evals/evaluate-evaluator/): How do I know whether a Jev evaluator is good enough? Treat the configured evaluator—not Jev in isolation—as the system under test. Validate that its criterion measures the intended construct, compare outputs with blind adjudicated labels, assess class errors and probability calibration, stress wording and evidence changes, and measure latency, failures, review load and downstream decision value. Predefine release gates by consequence and report uncertainty and slice results. - [Test Jev evaluators with pytest: fixtures, async calls and CI isolation](https://befailproof.ai/jev/evals/pytest/): How can I test a Jev evaluator with pytest? Use pytest to separate fast application contract tests from explicitly marked live Jev checks. Parametrize versioned evidence fixtures, validate response shape with fakes, and assert semantic decisions or justified probability bands rather than exact floats. Keep credentials in CI secrets, pin the model for release tests, bound concurrency and retries, and print full distributions and evaluator versions when a live case fails. - [Jev vs LLM-as-a-judge: outputs, evidence and benchmark design](https://befailproof.ai/jev/vs-llm-as-judge/): How does Jev compare with an LLM used as a judge? Jev is designed for bounded typed decisions and returns a probability distribution without generating an explanation. An LLM judge can interpret flexible rubrics, critique an answer and cite evidence, but its label must be extracted from generated output. Neither is universally better. Compare them on the same blind labels, evidence, task definition, error costs, repeatability, latency and total workflow cost. - [Jev vs LLM structured output: similar JSON, different systems](https://befailproof.ai/jev/vs-structured-output/): Is Jev the same as using structured output from an LLM? No. Both can produce machine-readable fields, but structured output constrains how a generative LLM serializes an answer, while Jev is a decision-only model whose native primitives return distributions over caller-defined answers. Use structured generation when you need arbitrary extracted values or explanations. Use Jev for bounded judgments where probabilities and a compact decision contract matter, then benchmark both on the target task. - [Jev vs embedding classifiers: semantics, data needs and hybrid retrieval](https://befailproof.ai/jev/vs-embedding-classifier/): When should I use Jev instead of an embedding classifier? Use embeddings when similarity to examples or documents is the task, especially for retrieval, clustering or stable high-volume routing. Add a trained classifier when you have enough representative labels and control over retraining. Jev is useful when a bounded semantic criterion can be stated directly and labeled training data is limited. Compare all candidates on held-out labels, calibration, latency, cost and drift. - [Jev vs rules: deterministic controls and semantic judgment](https://befailproof.ai/jev/vs-rules/): Should I use Jev or deterministic rules? Use deterministic rules whenever the condition can be computed exactly: permissions, paths, schemas, amounts, allowlists, rate limits and required fields. Use Jev only when the decision depends on the meaning of text and can be expressed as a bounded question. In mixed systems, run hard rules first, let Jev evaluate the remaining semantic condition, and keep final action logic in code. - [Jev vs human review: selective automation, capacity and accountability](https://befailproof.ai/jev/vs-human-review/): Can Jev replace human review? Jev can automate or prioritize bounded, well-tested decisions, but it should not be assumed to replace human judgment universally. Keep humans where policy is subjective, evidence is incomplete, consequences are high, explanations or appeals are required, or the evaluator is outside its validated domain. Measure a selective workflow: automation error, review coverage, queue delay, disagreement and downstream outcomes. - [Is Jev open source? Weights, SDKs, licenses and alternatives](https://befailproof.ai/jev/is-jev-open-source/): Is the TypeSafe AI Jev model open source? No public source or open-weight release for TypeSafe AI’s hosted Jev model was identified as of September 22, 2026. TypeSafe does publish official Python and JavaScript SDKs and an agent skill in public GitHub repositories. Open client code does not make the remote model open source. Community Jev-compatible or Jev-like projects are separate implementations and do not establish weight, training or calibration parity. - [Jev privacy and data retention: verified facts and open questions](https://befailproof.ai/jev/privacy-data-retention/): Does Jev retain requests or use them for training? TypeSafe’s model documentation states that customer requests and responses are not used for training. That statement does not by itself specify request retention duration, abuse-monitoring logs, backups, regional processing, deletion SLAs or every provider’s data handling. Verify the current contract and privacy documentation for the route you use, minimize Jev state, redact secrets, and document each processor before sending sensitive data. - [Can you self-host Jev? Deployment boundaries and alternatives](https://befailproof.ai/jev/self-hosting/): Can I self-host the official TypeSafe AI Jev model? No official downloadable Jev weights or self-hosted inference package was identified as of October 2, 2026. TypeSafe’s SDKs call a hosted model, and Cloudflare offers another managed route. For local inference, Ollama lists Nimble and Tev1, while SGLang documents a decision endpoint for supported chat models; Ollaya is another runtime. These routes do not serve official Jev weights. Check each artifact and license, then remeasure quality and calibration before substituting a local model. - [Jev language support: multilingual evaluation and rollout](https://befailproof.ai/jev/language-support/): Which languages does Jev support? TypeSafe’s model documentation says Jev is strongest in English. It does not provide a complete per-language quality table that supports treating all languages as equivalent. Jev can receive text in other languages, but production support must be established per language, locale and criterion with native-speaker labels, calibration, boundary tests and monitored coverage. Translate or route unsupported cases explicitly rather than assuming parity. - [Jev vs OpenAI Decisions API: what is known so far](https://befailproof.ai/jev/vs-openai-decisions-api/): How does Jev compare with OpenAI’s Decisions API? OpenAI announced its Decisions API at DevDay 2026, and Vercel says the GPT-6 Luna-powered service is in limited preview for questions over text or images. Jev already publishes its Choice, Score and Noul request contract and probability outputs. On October 2, OpenAI had not published a Decisions API schema, price or comparable benchmark. You can plan a test now, but you cannot make a measured speed or quality comparison yet. - [Jev vs Cloudflare Clef: API, weights and benchmark claims](https://befailproof.ai/jev/vs-cloudflare-clef/): How does Jev compare with Cloudflare Clef? Cloudflare launched Clef and Clef-flash on Workers AI on October 1, 2026. It says the interface accepts Jev-style requests, and it publishes Clef weights and visual input support. Jev remains TypeSafe’s separate hosted model. Cloudflare’s own benchmark table has wins for each model on different tasks. Reuse your question definitions if the client passes a contract test, then measure accuracy and fit new thresholds on your labels. - [Jev vs Perplexity Decisions API: contract, model and cost](https://befailproof.ai/jev/vs-perplexity-decisions-api/): How does Jev compare with Perplexity’s Decisions API? Perplexity has a documented Decisions API you can call today at POST /v1/decisions. It serves pplx-decider-v1-27b for Noul, Choice and Score questions, accepts text, JSON and images, and returns probabilities. Its listed input price was $0.04 per million tokens on October 2, 2026. Jev has a separate contract and model. Test both on the same labeled task before treating a similar API or list price as evidence of similar results. - [Jev vs Laya: hosted decisions and local multilingual models](https://befailproof.ai/jev/vs-laya/): How does Jev compare with Laya? Convai Innovations publishes Laya as an Apache 2.0 family you can run locally. Its Python runtime takes state plus Choice, Score or Noul questions, and it offers separate English and multilingual checkpoints. Jev is TypeSafe’s hosted model. Laya’s language and speed figures come from its publisher. For a multilingual ticket queue, test the exact languages and scripts you receive, then count errors, calibration, hardware use and review cost. - [Jev vs Kev-9B: self-hosted compatible decision model](https://befailproof.ai/jev/vs-kev-9b/): How does Jev compare with Kev-9B? Jared Palmer’s Kev-9B pairs a Qwen3.5-9B-Base model with an Apache 2.0 adapter and pointer head. The project documents a System One-compatible local server and a 24 GB-class GPU setup. Jev is TypeSafe’s separate hosted model. Kev’s calibration setting and published scores belong to its own checkpoint. Run contract tests and fit new action thresholds before swapping it into a Jev workflow. - [Decision-model launches after Jev: October 2026 source tracker](https://befailproof.ai/jev/news/decision-model-launches-october-2026/): What decision-model offerings launched after Jev? The October 2, 2026 snapshot has three kinds of new offerings. Perplexity and Cloudflare publish callable decision routes; OpenAI has announced a Decisions API but no verified public request contract. Intern-Decision, Julia 1, Laya and Kev-9B are independent model projects, while Ollama, Ollaya and SGLang provide serving paths. Check each source for weights, license, inputs and actual access. A similar question format gives you a starting fixture, not Jev-equivalent behavior. - [Jev vs Intern-Decision: multimodal open models and calibration](https://befailproof.ai/jev/vs-intern-decision/): How does Jev compare with Intern-Decision? InternLM publishes Intern-Decision as Apache 2.0 checkpoints at 0.8B, 2B and 4B, with local inference code and optional image input. Jev is TypeSafe’s separate hosted model. InternLM’s benchmark table is a publisher-run result on a selected panel. If you need image-assisted triage or local serving, run both paths on matched labeled cases, then calibrate and set thresholds for each checkpoint separately. - [Jev vs Julia 1: hosted decisions and a small local encoder](https://befailproof.ai/jev/vs-julia-1/): How does Jev compare with Julia 1? Julia 1 is a 144.3M-parameter model from Supersonic Labs, with Apache 2.0 weights, Python inference and a separate ONNX/WebGPU artifact. It may suit a CPU or browser deployment where Jev’s hosted route does not. The publisher’s benchmark card uses supplied Jev reference figures rather than a fresh paired run. Test Julia’s language coverage, 2–20 option limit and calibration against Jev on your own labeled cases. - [Jev evals in DeepEval: JevEval scores, questions and limits](https://befailproof.ai/jev/evals/deepeval/): How do I run Jev evals in DeepEval? DeepEval provides JevEval for single-turn tests and ConversationalJevEval for conversations. Pick the test-case fields Jev can see, write Noul, Choice or Score questions, and DeepEval turns the returned distributions into a weighted score. Its text reason is assembled in code. That score reflects your questions and weights; it is not a measured chance of agent success. Check it against blind human labels before setting a release threshold. - [Jev benchmark methodology: a reproducible evaluation protocol](https://befailproof.ai/jev/research/benchmark-methodology/): How should a Jev benchmark be designed? Define one operational decision, its evidence boundary and the cost of each mistake before choosing models. Freeze grouping-safe data and blind labels, then run Jev and every baseline on the same examples through pinned, observable harnesses. Compare paired errors, calibration, coverage, failures, latency and total workflow cost with uncertainty at the independent-example level. Publish the protocol, configurations, raw outputs and exclusions, and limit conclusions to the tested population and date. - [LangChain’s Jev eval experiment: claims, limits and reproduction](https://befailproof.ai/jev/research/jev-evals-langchain/): What does LangChain’s Jev evaluation experiment show? LangChain tested Jev against GPT-5.6 Luna, GPT-5.6 Terra and Claude Sonnet 4.6 on five fixed weather-agent runs, repeated 100 times. Jev matched one human reviewer’s binary labels on all 500 repeated decisions and had the lowest reported continuous-score variance, latency and cost. That is promising integration evidence, but five unique examples cannot establish broad evaluator accuracy. Reproduce it on representative traces. - [Jev reranking field report: what one negative result can teach](https://befailproof.ai/jev/research/reranking-field-report/): Does the reported Jev reranking test show that Jev does not work for search? No. GoSailGlobal reported on X that, in one test involving 33,047 items and 164 queries, Jev reranking alone did not beat vector retrieval. That is valuable counterevidence to universal improvement claims, but it is not a general verdict on Jev or reranking. The result needs its corpus, candidate depth, question design, metric, labels, model and code to be reproduced. - [Open Jev models and compatible APIs: how to verify equivalence claims](https://befailproof.ai/jev/research/open-jev-models/): Are open-source Jev models equivalent to TypeSafe AI’s Jev? No equivalence should be assumed. A project may reproduce Jev’s Choice, Score or Noul request shape while using different weights, training data and objectives. Evaluate “open” separately for weights, inference code, training code, data and license. Then test API behavior, probabilities, calibration, limitations, latency and security on the same held-out tasks before calling an implementation compatible or substitutable. - [Jev claim check: speed, price, calibration, privacy and evidence](https://befailproof.ai/jev/research/claim-check/): Which claims about Jev are verified? As checked September 22, 2026, TypeSafe documents Jev’s typed primitives, current model aliases, direct price, limits, English-first guidance and no-training use of requests, and announced general availability without a waitlist. Its latency and benchmark statements remain first-party measurements. LangChain provides one independent evaluator experiment, while public field reports include both working prototypes and a negative reranking result. Universal superiority remains unverified. - [Jev on X and LinkedIn: sourced claims, experiments and counterpoints](https://befailproof.ai/jev/research/x-roundup/): What are developers saying about Jev on X and LinkedIn? Jev discussion clusters around four claims: typed decisions are a distinct interface from text generation; fast bounded judgments can replace some LLM calls; Jev-like interfaces may be reproduced by open models; and application results depend heavily on task design. X contains launches, demos and counterexamples, while Reddit and Hacker News contain broader debate. Treat every post as attributed evidence, then inspect code, data and methods. - [Jev resources: the complete Jev link directory](https://befailproof.ai/jev/cheat-sheet/): Where can I find all Jev resources? This page is the complete directory of Failproof’s Jev resources. Start with the first grid, or jump to fundamentals, primitives, implementation guides, evaluations, comparisons, research, platforms or use cases. Each link opens a focused article with technical architecture, examples, external sources and related reading. - [Jev for classification](https://befailproof.ai/jev/use-cases/classification/): How do you use Jev for classification? Use Jev Choice when every item should select one label from a complete set, and use separate Nouls when several labels may apply independently. Define each label with clear inclusion and boundary criteria, provide a no-match or review outcome, and retain the full probability distribution. Validate class-level precision, recall, calibration and abstention on representative labeled data. - [Jev for customer support automation](https://befailproof.ai/jev/use-cases/customer-support/): How can you use Jev in customer support? Jev can turn a support message and relevant account context into separate typed decisions for intent, urgency, frustration, refund request and escalation need. Keep account lookups, refund arithmetic, eligibility rules and ticket mutations in code. Calibrate each semantic question against reviewed tickets, preserve uncertainty and require human review for consequential or ambiguous outcomes. - [Jev for content moderation](https://befailproof.ai/jev/use-cases/content-moderation/): How can you use Jev for content moderation? Use Jev to evaluate explicit moderation dimensions independently, such as harassment, self-harm risk, sexual content or commercial spam, and use Score only where ordered severity levels are clearly defined. Keep exact blocklists and account rules deterministic. Calibrate every category by language and population, retain an appeal path, and avoid one opaque “safe” score that hides distinct harms. - [Jev for llm model routing](https://befailproof.ai/jev/use-cases/model-routing/): How can you use Jev for model routing? Use Jev Choice to select among models that code has already filtered for availability, capability, policy and budget. Provide the request and relevant route metadata, include a safe fallback, and evaluate whether the routed model actually completed the task. Route confidence alone is not success: measure quality, latency, cost, retries and cache disruption end to end. - [Jev for agent and skill routing](https://befailproof.ai/jev/use-cases/agent-routing/): How can you use Jev for agent routing? Use Jev Choice to select one agent, handler or skill from candidates that code has already filtered for availability and permission. Add a no-match outcome or separate fit Noul so the model can decline the set. Evaluate route success after execution, and never let a selected agent inherit tools or privileges the caller was not already authorized to use. - [Jev for llm and agent guardrails](https://befailproof.ai/jev/use-cases/guardrails/): How can you use Jev for guardrails? Jev can provide bounded semantic checks on an LLM input, output or proposed tool call, such as whether text discloses protected context or violates one policy definition. It is one signal inside a guardrail, not the enforcement system. Keep permissions and exact rules deterministic, test adversarial state, define outage behavior and shadow the check before blocking actions. - [Jev for rag retrieval and groundedness](https://befailproof.ai/jev/use-cases/rag/): How can you use Jev in a RAG pipeline? Jev can score or select retrieved passages for a query and check whether a specific claim is supported by cited evidence. Retrieve candidates first, keep source identity intact and ask one bounded question per candidate or claim. Benchmark against the current lexical/vector pipeline because an extra semantic stage does not guarantee better retrieval, and retain citations for human inspection. - [Jev for search reranking](https://befailproof.ai/jev/use-cases/search-reranking/): How can you use Jev for search reranking? Use ordinary retrieval to produce a small candidate set, then ask Jev to judge query-to-candidate relevance with a consistent Choice, Score or Noul design. Do not apply Jev blindly to an entire corpus. Measure top-k relevance, ordering errors, latency and cost against BM25 or vector retrieval; a public field report found one Jev reranking setup did not improve its vector baseline. - [Jev for data labeling and curation](https://befailproof.ai/jev/use-cases/data-labeling/): How can you use Jev for data labeling? Jev can assign probabilistic Choice, Score or Noul labels to text records using a versioned definition. Treat them as weak labels until measured against independent human truth. Sample uncertain and apparently confident records for audit, preserve model and question versions, monitor class balance and subgroup errors, and never train and evaluate a downstream model on labels derived from the same examples. - [Jev for semantic code review and linting](https://befailproof.ai/jev/use-cases/code-review/): How can you use Jev for code review? Use Jev for narrow semantic review questions that static tools cannot express completely, such as whether a change follows one documented architectural convention or needs a specialist reviewer. Keep syntax, types, dependencies, secrets, vulnerabilities and test results in deterministic tools. Run Jev findings as review evidence first, measure them against maintainers and avoid presenting a semantic score as proof of correctness. - [Jev for email triage](https://befailproof.ai/jev/use-cases/email-triage/): How can you use Jev for email triage? Jev can classify an email into a routing category and independently judge urgency, reply need, promotion or suspicious content. Keep mailbox access, sender rules, exact phishing indicators and message actions deterministic. Send uncertain or high-risk mail to review, test by language and sender population, and minimize sensitive content sent to the model under the deployment’s approved data policy. - [Jev for recruiting workflows](https://befailproof.ai/jev/use-cases/recruiting/): How can you use Jev in recruiting? Jev can help organize job-related evidence against explicit criteria, such as identifying whether an application describes a required experience. It should not make autonomous hiring or rejection decisions. Keep protected attributes out of state, test subgroup errors, require trained human review, preserve the evidence and provide correction or appeal under applicable employment and privacy law. - [Jev for legal document workflows](https://befailproof.ai/jev/use-cases/legal/): How can you use Jev in legal workflows? Jev can support legal-document triage, bounded clause classification, passage relevance and checks that a cited passage supports one claim. It does not provide legal advice or replace qualified review. Preserve source text and jurisdiction, keep date and numeric logic deterministic, measure errors on representative documents, and route consequential or ambiguous interpretations to legal professionals. - [Jev for financial operations](https://befailproof.ai/jev/use-cases/finance/): How can you use Jev in financial workflows? Jev can classify financial messages, route exceptions and judge whether supplied text appears to support one bounded policy condition. It should not calculate balances, compare amounts, authorize transactions or make autonomous regulated decisions. Keep ledgers and permissions deterministic, validate by product and population, require review for consequential cases and preserve a complete audit trail. - [Jev for scientific research workflows](https://befailproof.ai/jev/use-cases/scientific-research/): How can you use Jev in scientific research? Jev can screen papers against explicit inclusion criteria, label passages with predefined themes and check whether cited text supports a bounded claim. Preserve source provenance, preregister the question and metrics, sample disagreements for expert review and publish model/version details. Jev does not establish scientific truth, calculate statistics or replace domain experts reviewing methods and evidence. - [Jev for games and realtime interfaces](https://befailproof.ai/jev/use-cases/games-realtime-ui/): How can you use Jev in games and realtime interfaces? Jev can select among bounded game actions or interface adaptations from compact text state when semantic understanding matters. Code must define legal actions, enforce game rules, render the interface and handle timeouts. Measure complete interaction latency and decision quality on the target hardware and network; do not treat TypeSafe or community latency examples as a universal realtime guarantee. - [Jev for smart-home assistants](https://befailproof.ai/jev/use-cases/smart-home/): How can you use Jev in a smart-home assistant? Jev can classify a household request and select among permitted device actions from current textual state. The home system must authenticate the speaker, read actual device state, enforce physical-safety and privacy rules, validate parameters and request confirmation for consequential actions. Treat ambiguity or service failure as a reason to clarify or stop, not permission to guess. - [Jev for browser automation](https://befailproof.ai/jev/use-cases/browser-automation/): How can you use Jev for browser automation? Jev can choose the next browser action from a bounded set derived from the current page and goal. The harness must ground candidates to real elements, validate that they still exist, enforce origin and permission rules, and require confirmation for consequential actions. Low confidence, changed state or unavailable service should stop or escalate rather than trigger a guessed click. - [Jev for feature engineering on text](https://befailproof.ai/jev/use-cases/feature-engineering/): How can you use Jev for feature engineering? Jev can convert text into reusable probability features such as evidence strength, urgency or topic fit. Store the raw probabilities once, then let downstream code or a supervised model combine them with structured features. Define questions without target leakage, split data before tuning, compare with simpler text features and monitor whether a Jev model or question change shifts the feature distribution. - [Where to try Jev: platforms, APIs and developer tools compared](https://befailproof.ai/jev/platforms/): Where can I try Jev? Start in TypeSafe’s browser Playground for a manual model test or use its direct API and official SDKs in an application. Cloudflare Workers AI publishes a Jev route. OpenRouter has a Jev URL, but its public model catalog returned no TypeSafe/Jev entry on September 22, 2026, so availability is not confirmed. Jev evals and policies are live for Failproof AI beta users. - [Jev eval platforms compared: DeepEval, jevals, LangSmith and Failproof](https://befailproof.ai/jev/eval-platforms/): Which platforms can run or compare Jev evaluations? You can call Jev directly through TypeSafe, build a metric with DeepEval’s JevEval, use the independent jevals library, or follow LangSmith’s published experiment. Vercel tools expose Jev too. Failproof AI connects Jev evals to traces and policies for beta users. For a platform choice, inspect the stored questions, raw outputs, labels and failures. A working model call alone tells you little about how the team will investigate a bad result. - [Jev vs LLM: typed decisions compared with generated text](https://befailproof.ai/jev/vs-llm/): How is Jev different from an LLM? Jev accepts text but returns bounded typed decisions and probabilities; a generative LLM produces an open-ended token sequence. TypeSafe says Jev uses a parallel sampler instead of autoregressive output generation and trains with RLCD for calibrated decisions. Jev fits repeated classification, scoring and verification. LLMs fit writing, coding, explanation and multi-step reasoning. Most agent systems can use both. ## Project links - [GitHub: FailproofAI/failproofai](https://github.com/FailproofAI/failproofai) - [npm: failproofai](https://www.npmjs.com/package/failproofai) - [X / Twitter: @failproofai](https://x.com/failproofai) - [Instagram: @failproofai](https://www.instagram.com/failproofai/) ## Blog posts - [How to Add Observability to Your Hermes Agent in 60 Seconds](https://befailproof.ai/blog/add-observability-to-hermes-agent/): A Hermes agent runs behind a chat gateway, on a cron, when nobody is watching - which is exactly why the failure you care about is the one nobody saw. Two commands forward every tool call, model request and error it makes to a dashboard you can replay. - [How We Made Agent Enforcement Sub-Millisecond](https://befailproof.ai/blog/sub-millisecond-agent-enforcement/): Enforcement used to run as a fresh process per tool call: 700ms of runtime boot before a single policy saw the command, and no way to tell a working install from a broken one. Replacing it with a Rust daemon and a warm worker took p50 to ~0.7ms and, for the first time, made fail closed a guarantee we can state in one sentence. - [AI Workers. New Buzzword?](https://befailproof.ai/blog/ai-workers-new-buzzword/): A year ago most AI companies were building agents. Now the same companies ship AI employees, AI coworkers, digital labor. The rename tracks a real shift: an agent produces an output, a worker owns an outcome, and the two fail in very different ways. - [Hallucinations, Over-Persistence, and Dangerous Actions in Frontier LLMs like GPT-5.6 Sol](https://befailproof.ai/blog/hallucinations-over-persistence-dangerous-actions/): In the week after GPT-5.6 Sol shipped, agents deleted a developer's Mac, wiped a production database, and force-removed worktrees — no confirmation asked. Why hallucination and over-persistence turn destructive in agentic systems, and why runtime policy enforcement is the defense that holds. - [How 100 Rows Cost Us 3.4 GB of RAM in ClickHouse](https://befailproof.ai/blog/how-100-rows-cost-3-4-gb-clickhouse/): A LIMIT 100 query was OOM-killing our 4 GiB ClickHouse pod at three concurrent requests. The cost scaled with table size, not rows returned — one wide column decompressed through FINAL on every poll. The post-mortem, and the one-line mental model that fixed it. - [AgentEye With/Without Skill on Claude](https://befailproof.ai/blog/cli-with-and-without-skill-file/): A skill file didn't make Claude smarter — it gave Claude the operational knowledge experienced CLI users pick up over time: pagination limits, silently-skipped confirmations, hidden SQL, and knowing when to stop and ask a human. - [Modern Software Product Engineering](https://befailproof.ai/blog/self-improving-software-loops/): Sprints and standups were built for a constraint that's gone. Here's the closed loop that observes, diagnoses, tests, and ships on its own — and where the CTO's job goes when reviewing every diff stops scaling. - [The Agent Didn't Fail. It Was Just Told Too Much, Too Soon.](https://befailproof.ai/blog/ai-agent-failure-causes/): Why progressive disclosure is the most underrated concept in agent reliability, and why hooks are the primitive that finally makes it possible. - [The Competitor Vertical AI Founders Refuse to Name](https://befailproof.ai/blog/the-competitor-vertical-ai/): A wake-up call for vertical AI companies. The reliability gap is the unnamed competitor - and it's eating market share quietly. - [Stop Praying in Prompts. Start Enforcing with Hooks.](https://befailproof.ai/blog/stop-praying-in-prompts/): Git figured this out decades ago and agents are about to go further. The enforcement gap is where reliability dies. - [The Future of Reliable Orchestration](https://befailproof.ai/blog/future-agent-orchestration/): What does infrastructure designed from the ground up for AI agents actually look like? Pre-warmed sessions, direct context streaming, stateful routing, sandboxed isolation, and a unified control plane. - [Retry Is Not Enough: The 3U Framework for Agentic Reliability](https://befailproof.ai/blog/why-retry-is-not-enough-3u/): In traditional software, retries fix transient errors. In AI agents, failures are cognitive. Observability after the fact doesn't help when every failure is unique. The 3U framework introduces a new model for reliability: Uncover, Understand, Utilize. - [Why Temporal Breaks When You Put an AI Agent Inside It](https://befailproof.ai/blog/why-temporal-breaks-with-ai-agents/): Temporal is the industry standard for durable execution. We tried using it as the durability layer for Claude Agents. Here are the 12 specific ways it falls apart. - [What Does Durability Mean for Agentic Software?](https://befailproof.ai/blog/durable-agents/): Durability frameworks were built for deterministic workflows. Agents are probabilistic, stateful, and long-running. Here's why the old playbook breaks and what should replace it. - [Observability Was Built for Servers. Agents Need Oversight.](https://befailproof.ai/blog/observability-was-built-for-servers/): The industry has spent 18 months building increasingly sophisticated observability tooling for agents: LLM-native traces, session replays, eval pipelines. It's still not enough. - [Two Env Vars and Done: Claude Code for Every Developer, Zero API Keys](https://befailproof.ai/blog/two-env-vars-and-done/): Open source to allow every developer to use all possible Anthropic models and credits across clouds - without juggling API keys. - [Stop Shipping Code. Start Building Factories.](https://befailproof.ai/blog/stop-shipping-code-start-building/): The next generation of software companies will not be defined by the code they write, but by the automated assembly lines they build. - [Missing sauce for your agents](https://befailproof.ai/blog/your-agent-sucks-because/): A thought piece on engineering culture, agentic systems, and what separates great products from everything else. - [The Future is SaaaS (Subagent as a Service)](https://befailproof.ai/blog/the-future-is-saaas/): A thought piece on how the most important companies of the next decade won't build software applications - they will become subagents. - [Launching the AIRE Principles: Industry Standards for AI Agent Reliability](https://befailproof.ai/blog/launching-aire-principles/): The first open framework translating SRE practices into actionable standards for production AI systems. Five principles, measurable metrics, and a proven path to agent reliability. ## Optional - [sitemap.xml](https://befailproof.ai/sitemap.xml): full URL list for crawlers - [robots.txt](https://befailproof.ai/robots.txt): bot-friendly crawl rules (AI agents allowed)