Simulate realistic users to evaluate multi-turn AI agents in Strands Evals

This article explains how to evaluate multi-turn AI agents using ActorSimulator in Strands Evals, which simulates realistic users for comprehensive testing beyond single-turn interactions.

• Multi-turn evaluation is harder than single-turn because each message depends on prior context and conversation paths are unpredictable
• ActorSimulator creates consistent personas with explicit goals that adapt naturally to agent responses
• Profile generation automatically creates distinct actor profiles from test cases for realistic conversations
• Goal tracking determines when simulated user objectives are met or agent cannot complete requests
• Structured reasoning shows why simulated users choose specific responses for transparency
• Integration with OpenTelemetry captures complete traces across conversation turns for evaluation
• Custom actor profiles enable targeted testing across different user segments and expertise levels
• Best practices include setting appropriate max_turns, writing specific task descriptions, and analyzing patterns across test suites

ActorSimulator enables systematic multi-turn evaluation of conversational AI agents through realistic, goal-driven user simulation integrated into existing evaluation pipelines.