Performance regression review with coding agents

Performance reviews for coding-agent-generated changes should rely on measurable baselines, representative workloads, and explicit performance budgets rather than visual inspection of diffs alone. Comparable tests must control variables such as datasets, concurrency, cache state, runtime, hardware, and sample size, while metrics should match the affected area, including bundle size and interaction timing for frontend changes or query counts, execution plans, latency, throughput, and memory for backend work. Reviewers should account for normal measurement variance, retain raw benchmark and profiling artifacts, and trace changed code to risks such as repeated requests, unbounded loops, eager loading, lock contention, or cache invalidation. Tools including profilers, bundle analyzers, query planners, load tests, and browser measurement systems can provide reproducible evidence when paired with repository-specific instructions, named workloads, thresholds, and commands. Findings should identify the affected input, metric, comparison commit, environment, and explanatory code path, while optimizations must still undergo correctness testing to avoid altering freshness, failure behavior, or other acceptance criteria. Stable, decision-relevant measurements can become required CI checks, whereas noisy benchmarks should remain advisory until their variance is understood.

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