Evaluate Multi-Turn AI Agents with ActorSimulator

Evaluating single-turn agent interactions is a well-understood process for most teams: you provide input, collect output, and assess the result. However, real conversations rarely stop at one turn. Users ask follow-up questions, change direction, and express frustration when their needs are unmet. For instance, a travel assistant that handles 'Book me a flight to Paris' well might struggle when the same user follows up with questions about trains or hotels.

Testing these dynamic interactions requires more than static test cases. The core difficulty lies in scale, as manually conducting hundreds of multi-turn conversations every time your agent changes is impractical. Evaluation teams need a way to programmatically generate realistic users who can engage naturally with an agent over multiple turns.

ActorSimulator in Strands Evaluations SDK addresses this challenge by offering structured user simulation that integrates into your evaluation pipeline. Multi-turn evaluation is fundamentally harder than single-turn because each message depends on previous ones. The user's second question is shaped by the agent's first response, and an incomplete answer may lead to follow-up questions.

Simulation-based testing, backed by clear persona definitions and goal tracking, allows for the creation of a controlled environment where realistic actors interact with the system. A useful simulated user must maintain a consistent persona and exhibit goal-driven behavior to reflect real interactions. ActorSimulator is designed around these principles, ensuring realistic and repeatable outcomes.

The process begins with profile generation, where ActorSimulator uses a language model to create a comprehensive actor profile based on the input query and task description. This approach enhances the quality of AI agent evaluation by addressing the complexities of multi-turn interactions.