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how to set up judges that score your agents, calibrate them against human labels, run them on production traffic without the bill running away, and turn what they find into policies that stop the failure next time.
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Async evaluators for long LLM judgments
Some judgments do not fit in a few seconds: several judge calls, a long trajectory judged in chunks, a rate-limited model. Run the calls concurrently with a cap, give the whole judgment a time limit, and make every judge call safe to repeat.
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Evaluate AI agent policy compliance: what to check and what to block
Some agent rules can be checked from a single tool call before it runs. Others need the whole session or an understanding of meaning. Sort the rules by the evidence they require, then use the result to choose code checks, LLM-based evaluations or runtime controls.
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How many eval examples do you need?
Enough that the error bar is smaller than the decision you are making. A pass rate from 50 examples is honest about large problems and silent about small ones. The formulas and worked numbers below tell you which situation you are in.
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How to alert on eval score drops with Failproof AI
Failproof AI already turns evaluation results into routed alerts and assignable incidents. Set the trigger, test the delivery, inspect the failed sessions and keep the alert running to verify the fix.
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How to audit agent sessions for failure patterns with Failproof AI
Failproof AI audits analyze a population of sessions, group related failures into findings and recommend what to fix. Set the goal, scope and cadence, then assign and verify each finding.
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How to build a Failproof AI evaluation in Python
Failproof AI runs evaluations in the cloud: Python code checks the assistant drafts from a description, LLM judges, and the eval set you already have. This takes one evaluation from a plain-English sentence to a tested, versioned check, then reads the results.
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How to build an eval dataset from production traces
Production traces contain real failures, edge cases and ordinary requests that synthetic examples miss. Learn how to sample them, remove sensitive data, label outcomes and freeze versioned datasets you can replay against every change.
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How to compare two agent versions
Run the current and candidate agents in separate environments, identify every session by agent and environment, apply the same evaluations and audits, and compare behavior over time with native queries and dashboards.
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How to debug a failing LLM evaluation
Inspect the evaluation results, use audits and queries to find which sessions are missing or mis-scored, update the evaluation from reviewed evidence, and backfill the new version before enabling it.
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How to detect silent agent failures in production
Capture complete production sessions, evaluate known failure signals, let audits discover recurring patterns you did not define, and turn findings into owned fixes with alerts and verification.
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How to evaluate a RAG agent for groundedness
Trace the retrieved evidence, run separate groundedness, relevance and completeness evaluations, use audits to find recurring retrieval failures, and verify fixes with queries, dashboards and alerts.
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How to evaluate agent cost and latency against a budget
Measure cost and latency across complete agent sessions, compare them with task-level quality, and find the behaviors responsible for expensive or slow runs. Failproof AI turns those questions into queries, dashboards, alerts and trace-backed findings.
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How to evaluate agent trajectories, not just final answers
A correct answer can hide the wrong tool, wasted steps, ignored errors or unsafe actions. Evaluate the complete session path, find recurring behavior across runs and use the evidence to improve the agent.
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How to evaluate agents without ground truth
Most production sessions have no answer key. Evaluate the agent against its contract and trace evidence, then use audits to discover recurring failures and reviewer feedback to improve the checks over time.
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How to evaluate an agent after a model upgrade
Run the old and new models as separate cohorts, apply the same evaluations and audits, and compare behavior, quality, cost and latency before expanding the rollout.
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How to evaluate an AI customer support agent
Trace complete support sessions, evaluate resolution and safe behavior, find recurring failures automatically, and turn the evidence into owned fixes, dashboards and alerts.
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How to evaluate coding-agent sessions
Capture the complete session, evaluate what the agent did, find recurring failure patterns across runs and turn proven risks into policies. Failproof AI was built to make that loop practical for coding agents.
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How to evaluate multi-agent systems
When a planner, three workers and a reviewer produce a wrong answer, the agent that shows the error is often not the one that caused it. Score the system, each agent and every handoff, and treat automated blame as a lead rather than a verdict.
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How to evaluate tool selection and tool calls in AI agents
Tool failures often look like answer failures: the wrong tool, an invented argument or an error the agent ignored. Check the mechanical parts in code, measure selection against labels, and use a judge for the questions code cannot answer.
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How to judge multi-turn agent conversations
A conversation can be ten reasonable replies that add up to a failure. Score the session as a whole and each turn on its own history, and when a session will not fit in the judge, cut it in a way that keeps the evidence.
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How to measure hallucination in AI agent runs
Agents hallucinate in two ways. They invent facts in prose, and they invent things to act on - tools, files, IDs, commands, work they claim to have done. The second kind is checkable in plain code from the tool events. Measure it first.
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How to reduce the cost of LLM-as-a-judge evals
Judge spend is sessions judged, times model calls per session, times tokens per call. Every useful cost lever reduces one of those terms. Here they are in the order to try, with a worked estimate whose assumptions you can replace with your own.
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How to regression test AI agents before you deploy
Replay a frozen set of real cases through the current and the candidate agent, score both the same way, and block the release on a regression you can defend. The hard parts are a nondeterministic agent and a noisy judge. Here is how to handle both.
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How to run agent evals in CI with Failproof AI
Run a small set of real cases against the candidate agent, let Failproof AI apply the same evaluations used in production, inspect regressions in session replay and stop the release when a critical case or score fails.
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How to run agent evaluations without a data team
You do not need a dedicated data or ML team to evaluate an agent. Start with a few deterministic checks, one task-completion judge and a small set of human-reviewed sessions. The person who owns the agent can maintain it in about an hour a week.
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How to sample production traffic for agent evals
Judging every session is the most accurate plan and usually the most expensive. How to pick which sessions a judge sees - random, stratified or triggered - so rare agents and real failures are not averaged away, with the cost math spelled out.
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How to score task completion for AI agents
An agent saying "done" is a claim, not a measurement. Write down what done means for each agent, check the end state where you can, judge against the same definition where you cannot, and keep partial credit out of the headline number.
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How to set up LLM judges for AI agents, step by step
Build a judge around one observable criterion, test it against human labels, run it on production sessions and connect low scores to investigation, alerts and fixes. Includes runnable Python.
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How to turn agent eval results into runtime guardrails
Evaluations can reveal a recurring agent behavior across many sessions. When the failure includes a recognizable, high-risk action, turn the evidence into a tested runtime policy while keeping the evaluation in place to measure whether the fix works.
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How to write a rubric for an LLM judge that it can apply consistently
Most judge problems are rubric problems. A binary or three-point scale, one criterion, conditions a reader can see in the transcript, the edge cases decided in advance, and a template you can copy.
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How to write an AI agent contract: purpose, success and limits
A short written contract defines what an agent is for, what it produces, when it is done, how often it runs and what it must never do. That context turns vague observations into specific, actionable failure findings.
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Human review for agent evals: when, how many, and what to do with labels
Every judge and every threshold is only as good as the human labels it was checked against. A small, well-run review loop - the right sessions, blind labels, two raters on an overlap - is what makes the automated scores worth believing.
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LLM judge bias: position, verbosity, self-preference and leniency
LLM judges can favor one position, longer answers, their own model family or a passing grade. Measure these effects on your own labeled examples, then choose mitigations based on the bias you actually observe.
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LLM judge prompt templates for agent evals: seven you can copy
Seven judge prompts for the failures agents actually have - an unfinished task, claimed actions that never happened, bad tool calls, ungrounded answers, broken rules, missed escalations and tone - each with its output format, plus one runner that parses them all defensively.
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What is LLM-as-a-judge? A model grading output against a rubric
A language model reads an output, a criterion and sometimes a reference, and returns a verdict. The four shapes a judge comes in, what changes when the output is an agent session, where judges break, and what plain code does better.
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Which model to use as an LLM judge, and how to decide
There is no best judge model, only the cheapest one that agrees with your labels on your rubric. What to weigh, a bake-off script that measures agreement, tokens and latency per candidate, and how to pin and upgrade the judge.
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Why calibrate an LLM judge, and how Failproof AI simplifies it
A model-based evaluation will misread edge cases and drift as production behavior changes. Older tools make teams maintain label sets and tune rubrics by hand. Failproof AI learns from audit findings and reviewer feedback, updates evaluations and verifies them against real sessions.
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