How to Build Robust Evaluations for AI Agents: A Complete Roadmap

Evaluation Structure

An evaluation ("eval") is a test of an AI system: give the model an input and apply scoring logic to its output. This article focuses on automated evaluations that can run during development without real users.

Single‑round evaluation is simple: one prompt, one response, one score. Early LLMs relied on this method. As capabilities improve, multi‑round evaluation becomes common.

In multi‑round scenarios, an agent receives tools, tasks (e.g., building an MCP server), and an environment, executes a "agent loop" (tool calls and reasoning), updates the environment, and then unit tests verify the result.

Agent evaluation is more complex: agents use tools across rounds, modify state, and can propagate errors. Cutting‑edge models can find creative solutions that bypass static checks, such as Opus 4.5 discovering a policy loophole to book a flight.

Key components of an agent evaluation:

Task (also called problem or test case): a single test with defined input and success criteria.

Trial : an attempt at a task; multiple trials are run to reduce variance.

Scorer : logic that assigns a score to aspects of the agent’s behavior; a task may have several scorers, each containing multiple assertions (checks).

Record (also called trace): the complete log of a trial, including outputs, tool calls, reasoning steps, and any other interactions. For the Anthropic API this is the final messages array.

Result : the final state of the environment after a trial (e.g., whether a flight reservation exists in a SQL database).

Evaluation framework : infrastructure that runs evaluations end‑to‑end, providing instructions, parallel task execution, logging, scoring, and result aggregation.

Agent framework (or scaffold): the system that enables a model to act as an agent, handling input, orchestrating tool calls, and returning results. When we evaluate an "agent", we evaluate both the framework and the model (e.g., Claude Code via the Agent SDK).

Evaluation suite : a collection of tasks designed to measure a specific capability or behavior, often sharing a common goal (e.g., a customer‑support suite testing refunds, cancellations, and upgrades).

Why Build Evaluations?

Early manual testing and intuition can move a project forward quickly, but without systematic evaluation teams become reactive, waiting for user complaints and struggling to distinguish regressions from noise. Introducing evaluations early defines success criteria, automatically covers many scenarios, and accelerates iteration.

Practices from Claude Code, Descript, and Bolt AI show that evaluations guide research‑product collaboration, support A/B testing and cost baselines, and enable teams to ship upgrades in days rather than weeks, delivering long‑term ROI that outweighs the initial investment.

How to Evaluate AI Agents

Types of Scorers

Agent evaluations typically combine three scorer families: code‑based , model‑based , and human . Each scorer assesses a portion of the record or result. Choosing the right scorer for a task is essential for effective evaluation design.

Capability vs. Regression Evaluation

Capability evaluation asks "what can it do?" and starts with low pass rates to push the team toward harder problems. Regression evaluation asks "does it still do the old tasks?" and aims for near‑100 % pass rates to prevent backsliding. Once capability targets are met, they can be folded into regression suites for continuous drift monitoring.

Metrics for Non‑Deterministic Agents

Because agent behavior varies between runs, two metrics help capture success frequency:

pass@k measures the probability that an agent obtains at least one correct solution within k attempts. Higher k yields higher scores; pass@1 is often the most relevant for coding agents.

pass^k measures the probability that an agent succeeds on all k attempts. This metric emphasizes consistency, which is critical for customer‑facing agents where repeated success is required.

As k grows, pass@k and pass^k diverge: at k=10, pass@k may approach 100 % while pass^k can drop toward 0 %.

Roadmap: From Zero to One

Step 0 – Start Early : 20–50 real failure cases are enough to begin; the later you start, the harder it is to catch up.

Step 1 – Mine Existing Manual Tests and Tickets : prioritize by user impact and convert directly into tasks.

Step 2 – Write Clear Tasks Re‑reviewable by Two Experts : ambiguous specifications create noise; when pass rate is 0 %, investigate tasks/scorers before the model.

Step 3 – Balance Positive and Negative Samples : testing only "should do" cases leads to over‑optimization; include "should not do" scenarios.

Step 4 – Isolate a Clean Environment : remove leftover files, caches, and resource leaks to avoid false positives such as "git‑see" bugs.

Step 5 – Score Results, Not Paths : give partial credit, align LLM‑as‑judge with human judgments, and provide an "unknown" fallback to avoid threshold bugs.

Step 6 – Regularly Read Logs : verify failures are fair and prevent scorers from shifting blame.

Step 7 – Monitor Saturation : when scores exceed 80 %, switch to harder tasks to avoid superficial improvements.

Step 8 – Ongoing Contribution and Ownership : core infrastructure belongs to the evaluation team; product teams submit tasks like unit tests, and PR‑style contributions keep the evaluation pipeline ahead of development.

Combining Evaluations with Other Methods

Automated evaluations can run thousands of tasks without affecting production users, but they are only one view of agent performance. A complete picture includes production monitoring, user feedback, A/B testing, manual log review, and periodic human evaluation.

These methods map to different stages of agent development: automated evaluation is valuable pre‑release and in CI/CD; production monitoring detects distribution drift post‑release; A/B testing validates major changes when traffic permits; and continuous manual review fills gaps by classifying feedback, sampling logs, and deep‑diving when needed.

Just as the Swiss‑cheese model in security relies on multiple layers, combining several evaluation layers ensures that a failure missed by one layer is caught by another. The most effective teams blend fast automated checks, truth‑seeking production monitoring, and calibrated human review.

https://www.anthropic.com/engineering/demystifying-evals-for-ai-agents