AI agent evaluation: How to test, debug, and improve agents in production

Testing and debugging AI agents, such as Alyx, require distinct approaches compared to traditional software due to the non-deterministic nature of AI outputs. When developing Alyx, the team discovered that small changes in prompts could lead to unpredictable failures, necessitating a robust testing framework. They moved from inefficient manual testing to using real production traces as test cases, capturing actual user interactions to ensure comprehensive evaluation of agent behavior. By employing LLM-based evaluators, they could assess whether outputs met expectations without relying on exact matches that are prone to brittleness. This method allows for more flexible and accurate testing, as it focuses on understanding the intent behind outputs. Additionally, integrating these tests into CI/CD pipelines ensures continuous monitoring and quality control, preventing regressions. Experimentation over time, especially during model upgrades, helps track performance trends and catch anomalies early. Ultimately, the testing framework fosters better communication within teams by providing a shared language for evaluating AI behavior, thus enhancing collaboration and reducing ambiguity.