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Evaluating AI agents for production requires a different approach than traditional software testing because agents produce non-deterministic outputs. Strands Evals is a framework built for the Strands Agents SDK that addresses this through three core concepts: Cases (test scenarios), Experiments (test suites), and Evaluators (LLM-based judges). The framework ships with 10 built-in evaluators covering helpfulness, faithfulness, harmfulness, tool selection accuracy, tool parameter accuracy, and goal success rate. It supports both online evaluation (live agent invocation) and offline evaluation (historical trace analysis). An ActorSimulator enables realistic multi-turn conversation testing by generating AI-powered simulated users. Evaluation operates at three levels: session, trace, and tool. An ExperimentGenerator can auto-create test cases from high-level descriptions. Best practices include starting small, writing specific rubrics, combining online and offline evaluation, setting meaningful thresholds, and tracking trends over time.

Evaluating AI agents for production: A practical guide to Strands Evals

The task function: connecting agents to evaluation

Built-in evaluators for comprehensive assessment

Evaluation levels: understanding the hierarchy

Integrating evaluation into your workflow