Your AI agent is fixing the wrong service

A benchmark of 100 injected bugs in a private 240-service, 65,000-line multilingual codebase found that an AI coding agent using monitoring alerts alone fixed 51% of cases, while providing captured request and response traffic increased its success rate to 77%. Traffic context sharply reduced wrong-service investigations from 34% to 4% and roughly halved resolution time for bugs solved under both conditions by revealing concrete endpoint, field, header, and payload mismatches. The largest benefits appeared for wire-level failures such as schema drift, missing headers, SSE framing, and URL encoding problems, where captured data directed the agent to relevant code quickly. However, traffic captures harmed performance on 11 cases involving internal logic, including exception hierarchies, race conditions, and transformations not visible at the HTTP boundary, because they encouraged fast but incorrect fixes focused on symptoms. The findings suggest that production traffic can substantially improve agent debugging when failures are expressed in request-response behavior, but should be supplemented with broader code exploration for faults rooted in internal layers.

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