Use AI and traffic replay to test AI-generated code

Traffic replay can help AI coding agents validate changes against recorded behavior rather than relying solely on tests whose expectations may reflect the same assumptions used to create the code. Using Cursor, proxymock, and a local Go example from the mock-lab repository, the workflow records requests to a working application and its downstream dependencies before changes are made, then replays those requests against an updated app with recorded dependency responses. Agents can compare status codes and response bodies, identify mismatches, distinguish configuration issues from regressions, and rerun the unchanged baseline after fixes. In the demonstration, two of eight requests initially failed because recorded authentication and order identifiers were stale; after replay configuration propagated fresh values, all requests matched. Developers can inspect detailed replay results in proxymock’s web viewer rather than relying only on an agent’s report, while retaining unit tests and adding separate tests for new or intentionally changed behavior.

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