AI evaluation

AI agent evaluation guide: traces, datasets, tool calls, and regression tests

Learn how to evaluate AI agents before production: trace review, task datasets, tool-call correctness, route quality, safety checks, online evals, human feedback, and regression gates.

Updated 2026-06-1110 min readIntermediate to advanced

Read LLM evaluation tools Read LLM observability tools

Best for

Teams launching agents with tools, memory, routing, or multi-step workflows
Developers building regression tests for agent behavior
AI product managers defining launch gates and success metrics
Security and support teams reviewing autonomous actions before rollout

Not for

Judging an agent only by one impressive demo
Using answer similarity alone for workflows with tool calls
Shipping agents without trace capture or replayable examples

Comparison

Choose by workflow, not brand

Option	Best for	Strengths	Tradeoffs	Use when
Offline evals	Pre-release regression tests on curated tasks and edge cases	Cheap to repeat and useful for comparing prompts, models, routes, and tool schemas.	Can miss live distribution shift and real user ambiguity.	You need a release gate before changing an agent.
Online evals	Production monitoring, drift detection, and issue discovery	Captures real user behavior and operational failures.	Requires privacy controls, sampling, latency budgets, and human review.	The agent is already serving users and needs continuous quality checks.
Human review	High-risk actions, ambiguous judgment, policy cases, and calibration	Grounds LLM-as-judge scores in business reality.	Costs more and needs clear rubrics.	Wrong actions create customer, legal, financial, or trust risk.

Score intermediate steps

An agent can produce a plausible final answer after calling the wrong tool, skipping permission checks, or ignoring a failed API response. Evaluate the trace as part of the answer.

Check whether the chosen tool was appropriate.
Validate tool arguments against fixtures and policy.
Score route choices, retries, handoffs, and final response separately.

Build representative datasets

A useful dataset includes normal tasks, edge cases, adversarial requests, missing data, tool failures, and examples where the correct answer is to refuse or ask for clarification.

Keep examples tied to real product workflows.
Version datasets so prompt and model changes can be compared.
Include negative tests where the agent must not take action.

Use evals as release gates

Agent evals become valuable when they block risky changes. Tie eval results to deployment decisions, rollback rules, and incident review.

Set minimum pass rates for critical task classes.
Track latency, cost, and validation failures alongside quality.
Review failed traces before expanding autonomy.

Decision Rules

A practical checklist

Evaluate task success, tool choice, tool arguments, safety, latency, and cost separately.

Use offline evals as release gates and online evals as production monitoring.

Calibrate LLM-as-judge scores with human review for important workflows.

Do not give an agent more autonomy until its traces are observable and replayable.

Related Guides

Continue the decision path

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LLM evaluation tools

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LLM red teaming guide

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Chinese Archive

Aligned deeper reading

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Chinese AI product testing and release notes.

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AI agent archive

Chinese agent workflow and implementation materials.

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Topic Hubs

Explore the wider search cluster

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RAG and models

Plan RAG systems, local LLM deployment, model APIs, cloud AI platforms, vector databases, evaluation, observability, rate limits, and cost optimization.

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FAQ

Common questions

How do you evaluate an AI agent?

Evaluate both the final answer and the intermediate process: task success, tool choice, argument validity, route quality, policy adherence, latency, cost, and recovery behavior.

Are LLM-as-judge evals enough?

No. They are useful, but important workflows need human-calibrated rubrics, deterministic checks, and trace review.

What should an agent eval dataset include?

It should include common tasks, edge cases, failure cases, tool errors, permission boundaries, adversarial inputs, and examples where the agent should ask for clarification.

Source Links

Primary references used for this guide

Reference

OpenAI evaluation best practices

OpenAI guidance for designing evals and current Evals platform transition notes.

Open

Reference

LangSmith evaluation docs

LangSmith documentation for evaluation workflows and agent eval tutorials.

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Reference

Phoenix evaluation docs

Arize Phoenix documentation for LLM output evaluation.

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Reference

Langfuse LangGraph agent eval guide

Langfuse guide for tracing and evaluating LangGraph agents.

Open

Build your own evaluation note

The strongest decision is always local to your workflow. Save the vendor links, define a representative task, record the exact prompt or command, and compare the final evidence instead of the marketing claim.

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