failproof ai vs future agi

What Each Product Is

Future AGI covers most of the LLM app lifecycle in one Apache-2.0 platform: tracing through traceAI on OpenTelemetry, evaluations with built-in metrics such as Tool Call Accuracy and Trajectory Match graded by its own Turing models or a judge you bring, Error Feed to group failures, multi-turn text and voice simulation, prompt optimization, and the Agent Command Center, an OpenAI-compatible gateway to more than 100 model providers. Protect runs checks on the traffic through that gateway and returns block, warn, mask or log.

Failproof AI finds agent failures and then prevents them. It connects natively to twelve agent harnesses, including Claude Code, Codex, Cursor, GitHub Copilot CLI and Goose, and its Python SDK traces custom agents. Evaluations run in the cloud on every trace and session: code checks, LLM judges, and the eval set you already have, brought in as it is. Audits group failures into findings with a severity and a recommended fix; from an issue, generate policy drafts a policy you backtest against calls your agents already made, and the open-source failproofai CLI enforces it at PreToolUse, before the tool runs.

Both evaluate, and both can block. The difference is where the block happens and what feeds it. Future AGI's guardrails sit on the network hop to the model provider and act on traffic routed there. Failproof AI's policies sit inside the harness, see the tool call itself, and come out of the failures its evaluations and audits found.

Features Compared

Future AGI's column comes from its public site, docs and repository as of September 2026. Every page used is linked under Sources.

Both find failures. The rows that separate them come later in the loop: whether a fix is tested against your own past agent activity, and whether the next failure is stopped where the agent acts or only where its model traffic happens to pass.

Where Future AGI Stands Out

• Breadth in one platform. Multi-turn text and voice simulation, prompt optimization with methods such as ProTeGi, PromptWizard and GEPA, and an OpenAI-compatible gateway to more than 100 model providers sit alongside tracing and evals.
• Its own evaluator models. Turing Large, Turing Small and Turing Flash grade outputs, and Turing Large takes text, image and audio.
• Checks on model traffic. Protect scans model inputs and outputs for PII, prompt injection, secrets and moderation across text, image and audio, with adapters for Lakera Guard, Presidio and Llama Guard.
• License and seats. Apache-2.0, free to self-host, with VPC and air-gapped options on its enterprise page and unlimited seats on every plan.

Where Failproof AI Goes Further

• Prevention that comes from what you found. Audits group failures into findings with a recommended fix, generate policy drafts a policy from the issue, and a backtest replays it against calls your agents already made before it ships. Future AGI's Error Feed suggests root causes; configuring a guardrail for one is a separate step.
• Bring your own evals. Failproof AI runs the eval set you already have (DeepEval, Ragas, promptfoo or your own) in the cloud as it is, alongside code checks and LLM judges.
• Every trace and session, per customer. Evaluations run on every trace and session with results beside the trace, and each customer organization has its own evaluations, audits, issues and keys.
• Nothing to reroute or host. Install the CLI, wire the hooks and connect the free cloud tier. Self-hosting Future AGI means running its full stack; its Docker Compose guide starts with "all 21 services".

Where the Block Happens: Gateway or Hook

Future AGI puts its agent guardrails on the network. Its May 2026 post on runtime guardrails says they "run at the gateway, the single network hop between your app and the model providers." A Tool Permissions guardrail "enforces which tools an agent may invoke, at the moment it tries", and an MCP Security guardrail inspects "the calls the agent makes to MCP servers and the responses it gets back." In Enforce mode, a request that fails a check is stopped with a 403.

That design covers every request routed through the Agent Command Center, and only those. Future AGI's docs do not say whether it reaches a coding agent's local shell commands or file writes, so if that is your question, ask them with your harness named.

Failproof AI makes the decision where the action happens: in the harness. At PreToolUse a policy receives the tool name and its input (the command for Bash, the file_path and content for Write) and returns allow, deny or instruct before anything executes, in all twelve supported harnesses. No traffic is rerouted. Here is least privilege for coding agents that should never change infrastructure:

// infra-policies.js: least privilege for coding agents, enforced in the harness
import { customPolicies, allow, deny } from "failproofai";

customPolicies.add({
  name: "no-infra-changes",
  description: "Deny infrastructure changes from coding agents on this machine",
  match: { events: ["PreToolUse"] },
  fn: async ({ toolName, toolInput }) => {
    if (toolName !== "Bash") return allow();
    const command = String(toolInput?.command ?? "");
    if (/\b(terraform\s+apply|kubectl\s+(apply|delete)|helm\s+(install|upgrade))\b/.test(command)) {
      return deny("Coding agents may not change infrastructure. Describe the change you need and open a PR instead.");
    }
    return allow();
  },
});

Install it locally with failproofai policies --install --custom ./infra-policies.js, or publish it from the policy editor, backtest it, and deploy it in observe mode before you enforce it. The coding agent pack, FailproofAI/policies, adds 38 ready-made policies, including block-env-files, block-secrets-write and block-force-push.

One Failure, Both Tools

An engineer's Claude Code session reads a log line that tells it to curl the .env file to an outside host. The run returns no error, and the transcript looks normal.

1. Future AGI: trace, evaluate, guard the gateway

   traceAI records the session, and an evaluation can flag the upload after the fact. Whether the curl itself is stopped depends on whether that session's traffic runs through the Agent Command Center and what its checks are set to catch, which is a setup to confirm with Future AGI rather than assume.

2. Failproof AI: evaluate, audit, fix, block

   An evaluation flags the session, and an audit groups every session where an agent acted on instructions found in a file into one finding with a recommended fix. From the issue, generate policy drafts a PreToolUse rule; you backtest it against past sessions and enforce it beside block-env-files from the coding agent pack.

3. The next attempt

   With Failproof AI, the command is denied before it runs, the agent is told why, and the denial lands in the session trace. With Future AGI, the outcome still depends on how that session is routed.

Both products can see the failure. Failproof AI is the one that stops the next attempt where the agent acts, whatever route its model traffic takes.

Pricing

Both publish prices and both let you start without a sales call. The meters are different, so the numbers do not convert directly.

Future AGI's pricing page and its enterprise page describe SLA and feature gating differently (99.5% and 99.9% on the add-ons, 99.99% on the enterprise page), so get the tier you are buying in writing. The full breakdown is in Future AGI pricing.

Moving from Future AGI, or Running Both

If a customer-facing application already runs through Future AGI's gateway, you do not have to remove it; keep Protect on that traffic. Add Failproof AI to the agents themselves: its evaluations and audits find the failures across every trace and session, and its policies block the failing action inside the harness before it runs. If your evals already live in DeepEval, Ragas or promptfoo, Failproof AI runs them in the cloud as they are, with nothing rewritten.