Building Test Suites for Agents

Knowing what to measure is necessary but not sufficient. You also need infrastructure: a structured collection of test cases, a reproducible way to run the agent against them, and a system for tracking results over time so you can detect regressions as the agent changes. This is agent test suite engineering, and it is one of the disciplines that separates prototype agents from production agents. A prototype is tested manually, occasionally, by the people who built it. A production agent is tested automatically, continuously, by a system designed to catch problems before users encounter them.

The Eval Set: What It Is and How to Build One

An eval set is a curated collection of tasks — each with an input (the task description or initial user message), optional context (tool state, database contents, environment state), and a ground-truth specification of what success looks like. The eval set is the agent's permanent exam: you run the agent against it, score the results, and compare scores over time. Building a good eval set is a design challenge, not just a data collection task. A weak eval set — one composed only of easy, typical cases — gives inflated scores that feel encouraging but do not predict production behavior. A strong eval set deliberately includes: Typical cases: the common requests the agent will receive in production, representing the core of the task distribution. Edge cases: unusual inputs that stress-test the agent's assumptions — empty inputs, very long inputs, inputs with conflicting constraints, inputs in unexpected formats. Adversarial cases: inputs designed to expose failure modes — tasks that should trigger a refusal, tasks that look like goal drift opportunities, tasks that require the agent to recognize it lacks a necessary tool. Regression cases: tasks that a previous version of the agent failed, added permanently to the eval set once they are fixed, to ensure the fix holds across future changes. A practical starting eval set for a new agent deployment is typically 50 to 200 tasks. This is small enough to run frequently (even on every code change) but large enough to give meaningful coverage of the task space.

Regression Testing: Catching What You Break

A regression is a failure that did not exist in a previous version of the system but appears after a change — the opposite of a fix. Regression testing is the practice of automatically verifying that every change to an agent preserves the behavior that was previously correct. For software, regression testing is well-understood: you run the test suite on every code change and fail the build if any test breaks. For agents, regression testing is more complex because the agent's behavior is stochastic. A test case where the agent previously succeeded may fail on the current run simply due to random variation in model outputs — even if nothing actually regressed. The standard approach is to run each eval case multiple times (typically 5 to 20 runs) and compute a pass rate. A regression is flagged when the pass rate for a previously passing case drops significantly — for example, from above 80% to below 50% — rather than when a single run fails. Statistical thresholds for regression detection must be calibrated based on the inherent variability of the agent and the acceptable false-positive rate.

Continuous evaluation pipelines integrate regression testing into the development workflow. When a developer modifies the agent's system prompt, adds a new tool, or changes a parsing step, the eval pipeline runs automatically — typically in a CI/CD system — and reports whether performance improved, stayed stable, or regressed. This closes the feedback loop between development and evaluation, making quality visible in real time rather than discovered after deployment. A key engineering decision is which subset of the eval set to run on every change (the fast suite, typically under 5 minutes) versus which to run nightly or on major version bumps (the full suite, which may take hours). The fast suite should include all regression cases and a random sample of the full eval set. The full suite provides comprehensive coverage for deployment decisions.

Benchmarks: Comparing Against a Standard

A benchmark is a standardized eval set shared across multiple agents or agent frameworks, designed to allow fair comparison between different systems. Benchmarks serve a different purpose than private eval sets: where a private eval set is tuned to your specific deployment, a benchmark provides a common yardstick that lets the community measure progress. Prominent agent benchmarks include WebArena (web navigation and task completion), SWE-bench (software engineering tasks on real GitHub issues), GAIA (general assistant tasks requiring multi-step reasoning), and AgentBench (diverse agentic tasks across domains). Performance on these benchmarks is widely reported in AI research and can give a rough sense of a model's agentic capabilities relative to the state of the art. Benchmarks have significant limitations as a proxy for production performance. Benchmark tasks are often narrower and more structured than real deployments. Models and agents can be implicitly or explicitly optimized for benchmark performance without improving on real tasks. And benchmarks quickly become stale as the field advances and practitioners learn to game them. A responsible use of benchmarks is as a coarse filter and for tracking progress over time — not as the primary signal for deployment decisions.