Show HN: Understudy: Scenario Testing for AI Agents (github.com)

Understudy has launched a new scenario-driven testing framework designed to enhance the evaluation of AI agents through realistic user simulations. This framework allows developers to create multi-turn conversation tests in which agents interact with a simulated user, while recording an execution trace of their actions—such as tool calls and decisions. The testing process involves wrapping the agent, mocking tools, writing YAML scene files to define interactions, and running simulations to validate the agent's behavior without relying on the output prose alone. This innovative approach is significant for the AI/ML community as it introduces a structured method for evaluating dialogue systems, such as customer service bots and task automation agents. By focusing on traces of actions taken rather than just verbal outputs, Understudy promotes a more rigorous assessment of agent effectiveness. Key features include the ability to simulate multiple scenarios, validate actual tool usage, and generate detailed reports that include metrics and evaluation results—making it a powerful tool for developers aiming to refine AI behavior and ensure compliance with expected performance standards.