Refactor Safely with AI: Using MCP and Traffic Replay to Validate Code Changes

AI coding assistants can create “hallucinated success” when they generate both implementation and tests, allowing misunderstood requirements to appear correct despite production failures. The proposed “Ralph Wiggum Loop” addresses this by repeatedly having an agent modify code, replay immutable recordings of sanitized production traffic, inspect precise response differences, and retry until it meets a defined fidelity score. The architecture uses Speedscale’s eBPF-based traffic recording and DLP filtering, cloud storage, local proxymock snapshots, and an MCP bridge that lets AI agents pull recordings, replay requests against local services, and analyze expected-versus-actual response diffs. In a legacy endpoint refactoring example, the agent initially causes failures, then uses replay data to identify incompatibilities such as JSON serialization and null-handling differences before reaching full compatibility across 1,958 recorded requests. This approach positions recorded production behavior, rather than agent-authored tests, as an external and objective validation source for AI-assisted software changes.

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