Evaluating AI Agents

What this book helps you do

This book turns agent evaluation into a decision system rather than a leaderboard. It starts with a measurement contract, builds a portfolio of tasks and testable environments, grades outcomes and trajectories, measures stochastic reliability, and connects simulation, red teaming, production monitoring, and human review to explicit release gates.

Problems this book helps you solve

• A golden set checks final text but ignores tool use, state changes, and permissions.
• One average score hides rare serious failures and weak user segments.
• Tasks do not specify environment state, allowed tools, budgets, or reset behavior.
• LLM graders are used without calibration, disagreement review, or version control.
• The benchmark improves while production incidents reveal new failure families.
• Evaluation runs cannot be reproduced because prompts, models, tools, or fixtures drifted.

Decisions you will be able to make

• What decision the evaluation supports and what evidence is sufficient for that decision.
• Which task families, user segments, risks, frequencies, and environments belong in the portfolio.
• Which checks should be deterministic, rubric-based, model-graded, or human-reviewed.
• How many repeated trials are necessary for the reliability claim being made.
• Which serious failures block release regardless of an aggregate score.
• How offline evaluation, shadow traffic, canaries, and production monitoring complement each other.

Reading path

1. Write the measurement contractConnect the system decision, task boundary, claims, metrics, uncertainty, and stop condition.
2. Build a task portfolioRepresent frequency, risk, user segments, tools, environments, and changing failure modes.
3. Construct a testable worldVersion state, fixtures, tool behavior, resets, faults, and observability.
4. Grade outcomes and tracesSeparate deterministic checks, rubric judgments, trajectory evidence, and serious failures.
5. Measure stochastic reliabilityRepeat trials, show uncertainty, inspect slices, and avoid average-only release claims.
6. Connect preproduction to operationUse simulation, red teams, shadowing, canaries, and production gates as complementary evidence.

Evaluate the system that acts

An agent’s final answer can be acceptable even when its path was unsafe, wasteful, or irreproducible. Evaluation therefore needs the environment, allowed authority, state transitions, tool effects, budgets, and review decisions—not only a reference response.

Make every result decision-shaped

A result matters when it changes a release, rollback, routing, supervision, or investment decision. The book asks readers to state that decision before choosing a metric or building a dataset.

Use it with

Create one task card for a common workflow and one for a rare serious failure. If the environment cannot be reset or the effect cannot be observed, fix the test system before expanding the benchmark.

Evidence and method

The book treats evaluation as measurement under stated context. Standards, primary research, and official platform documentation support bounded claims; synthetic examples demonstrate mechanics rather than model superiority. Every score requires a denominator, environment, version, date, and decision meaning, and grader output remains an instrument subject to calibration and review.

Read a sample

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Resources

Errata and related guidance

Report or review an erratum.

• Why is a golden dataset not enough for agent evaluation?
• How do I build a task portfolio instead of a benchmark shrine?