What is AI agent evaluation?

AI agent evaluation is the practice of measuring whether an agent does the right thing — picks the right tool, passes the right arguments, gets the right outcome — across many scenarios. It's distinct from LLM evaluation, which measures the model's text output, because agent eval has to score actions and outcomes, not just words.

How is replay-based evaluation different from a static test suite?

Static suites ask the agent the same hand-written questions on every run. They're good for catching obvious regressions but miss the long tail of real user behavior. Replay-based eval pulls actual production traces — including the failures your users hit — and re-runs them against the candidate agent. The eval set grows automatically with your traffic.

Should I use LLM-as-judge for scoring?

It's useful for fuzzy criteria like answer quality or tone, but on its own it's a weak signal — judges drift, prefer their own family of models, and can't verify tool correctness. Pair it with deterministic scorers: tool-call success, policy decisions, schema validity, and human-override rate from the approval queue.

Can I run agent eval in CI?

Yes. SafeRun's evaluate() runs against a fixed set of replayed traces and exits non-zero on regressions. Wire it into the same CI step as your tests; block PRs that make real traffic worse.

Does this work for multi-agent systems?

Yes. The trace records every tool call across every agent in the loop, including handoffs. Eval scores the system end-to-end — task resolved or not — and per-agent at the action layer.

Open app

Pillar guide

AI agent evaluation, powered by replay.

Static prompt suites pass while production breaks. SafeRun evals run real traces against your candidate agent — and fail the build when it regresses.

The problem

Evals that don't reflect reality don't catch real bugs.

Most agent eval suites are a folder of hand-written prompts and an LLM-as-judge rubric. They're easy to write and almost always green — because real users never ask the questions you imagined.

Production traffic is the only fair eval set. The job of an eval system is to capture it, replay it against new versions, and score what actually happened on the action layer.

Six methods

How agent evaluation actually works.

Replay-based eval

Re-run real production failures against a candidate agent. The eval set writes itself from the bugs your users already found.

Model & prompt diffs

Compare runs across models (GPT-5 vs Claude vs Gemini) or prompt versions. See which one picks the right tool, returns the right value, costs less.

Outcome scoring

Score by tool-call success, policy decisions, human-approval overrides, and final task resolution — not just BLEU or token loss.

Reliability score over time

One number per agent that rolls up success, blocks, retries, and approvals. Trend it across releases.

Regression catching

Snapshot known-good runs. Fail the build when a candidate version regresses on any captured trace.

Synthetic + real traffic

Mix curated test cases with sampled production traces. Catches both happy-path bugs and weird-prompt edge cases.

Avoid these

Four eval patterns that look fine and aren't.

Pattern	Why it misses
Static prompt eval suites	Real users don't ask the questions you wrote. Your suite passes; production breaks.
LLM-as-judge only	Judges drift, agree with themselves, and miss tool-call correctness. Use them, but pair with tool-outcome scoring.
Token-level metrics	BLEU and perplexity say nothing about whether the agent refunded the right customer.
Eval-once-at-launch	Models, tools, and prompts change weekly. Eval has to run on every change, against real traces.

Drop-in CI

Block regressions before they ship.

eval.tstypescript

import { evaluate } from "@saferun/sdk";

const result = await evaluate({
  agent: candidateAgent,
  traces: { source: "production", limit: 200 },
  scorers: ["tool-success", "policy-decision", "human-override"],
});

if (result.regressions.length > 0) process.exit(1);
// Wire into CI. Block PRs that regress real production traces.

FAQ

Common questions about AI agent evaluation.

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