A growing challenge in AI engineering is ensuring that fixes to agent behavior don't introduce new regressions. This article introduces a technique that turns production traces from agent incidents into replayable regression test suites. By capturing the exact sequence of events, inputs, and outputs during an incident, teams can replay these scenarios in a test environment to verify that fixes work as intended. This approach not only automates the verification process but also builds a library of realistic test cases that reflect real-world usage patterns. It's particularly valuable for teams operating AI agents in production, where edge cases and unexpected interactions are common. The method aligns with broader reliability engineering practices, bringing the discipline of regression testing to AI systems. For platform teams and SREs, this offers a concrete way to improve system robustness and reduce the time spent on manual debugging after incidents.
This article presents a method to convert production agent incident traces into replayable regression test sets. This approach enables teams to automatically verify fixes and prevent regressions in AI agent behavior. It's a practical pattern for improving AI system reliability and reducing debugging time.