AI Coding Agents
Performance
Performance regression review with coding agents
September 22, 2026 - 2 minute read
Coding agent performance review needs a measurable baseline. A diff may suggest an extra query, repeated allocation, or larger browser bundle, but the impact depends on the workload and environment. Reviewers need evidence that compares equivalent runs and connects a change to a user-facing or operational limit.
A performance budget turns that limit into a merge criterion. Google’s performance budget guidance describes budgets for quantities such as resource size, timing milestones, and rule-based scores. Backend services can apply the same principle to latency, throughput, memory, database calls, or queue depth.
Start from a comparable baseline
Choose a representative workload before reviewing the patch. Fix the dataset, concurrency, cache state, runtime version, hardware class, and number of samples. Compare the branch with its merge base under the same conditions. A result from a warm local cache cannot support a claim about a cold production path.
Select metrics tied to the change. A frontend dependency update may need bundle size and interaction measurements. A database change may need query count, execution plans, and tail latency. A background worker may need throughput, memory growth, and retry volume. Keep existing service-level objectives and budgets as the source of truth.
Record natural variance. If repeated baseline runs vary more than the observed difference, the test cannot support a regression finding. Improve the harness or report the result as inconclusive instead of assigning false precision.
Preserve raw measurements and profiler output with the review. Aggregated scores alone can hide one expensive route, query, or interaction.
Review the changed execution path
Trace inputs from the changed code to expensive operations. Look for unbounded loops, repeated network requests, missing pagination, extra serialization, eager loading, lock contention, and cache invalidation. Static inspection can identify a credible risk, while profiling or benchmarks show whether that risk appears in the chosen workload.
Factory’s local code review supports custom instructions for performance regressions and unnecessary re-renders. Give the review concrete repository context, including the hot path, budget, benchmark command, and known exceptions. A generic request to “check performance” produces weaker evidence than a named workload and threshold.
Keep deterministic tools in the loop. Bundle analyzers, profilers, query planners, load tests, and browser measurement tools produce artifacts that another reviewer can inspect. The agent can connect those results to the diff and propose a focused fix.
Make the finding reproducible
A useful performance finding names the affected input, changed metric, comparison commit, environment, and command. It also points to the code path that explains the result. Screenshots without configuration or a single timing from a developer laptop do not provide enough evidence for a merge decision.
Run correctness tests after any optimization. Caching, batching, and deferred work can improve a benchmark while changing freshness or failure behavior. Preserve the original acceptance criteria and add a regression check where the repository can run it consistently.
Automated review in CI can apply the same instructions to every pull request through Factory’s code review workflow. Keep noisy benchmarks advisory until their variance is understood. Promote stable, decision-relevant measurements to required checks.
