Flamegraphs Find It. Replay Proves It.

A walkthrough demonstrates how combining Grafana Pyroscope profiling with proxymock traffic recording and replay can provide independent performance and behavioral evidence for AI-assisted code changes. Using a Go catalog API whose quadratic deduplication logic processes 15,120 records, the lab records real request and downstream-response traffic, replays it against a mock dependency, profiles CPU usage under repeatable load, and directs an AI agent to inspect both the hotspot and recorded input before proposing a minimal fix. The reference optimization replaces nested duplicate scanning with constant-time membership checks while preserving validation and first-valid-record semantics, reducing a focused benchmark from 83.9 ms/op to 0.69 ms/op and, on one Apple arm64 machine, improving API latency from 168.2 ms to 13.0 ms and throughput from 47.3 to 591.3 requests per second without failed requests or stable response differences. The process emphasizes that status codes and schemas alone cannot establish correctness, so functional replay and response diffs must verify stable output fields, while a second CPU profile confirms that the original hotspot has been materially reduced. It also recommends read-only, scoped observability access and careful handling of potentially sensitive profile and recorded-traffic data.

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