How to test AI agents: A practical guide
Blog post from Braintrust
Testing AI agents differs from traditional software testing because model-driven behavior is non-deterministic, often involves multiple tool calls, and may change external state, making exact output matching inadequate. Effective evaluation assesses both final outcomes and decision trajectories through scored criteria, repeated trials, trace-level inspection, and controlled or stubbed dependencies that prevent real-world side effects such as refunds or customer emails. A comprehensive strategy combines deterministic unit tests with agent evaluations across single-step decisions, full trajectories, CI regression suites, and production monitoring, examining tool selection, argument validity, output schemas, response quality, execution order, final system state, and step counts. Test cases should draw heavily from real production traffic and incidents while covering routine, edge, adversarial, and out-of-scope requests, with datasets versioned alongside agent changes. Braintrust presents its platform as a system for defining datasets, tasks, and scorers; tracing tool interactions; enforcing score thresholds in CI; and scoring sampled production traces so reviewed failures can be incorporated into future regression tests.
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