Two confident fixes missed this production bug
A production bug survived two confident fixes and green tests. Replaying the captured request exposed the missing state and proved the real fix.
Latest insights on Agentic AI workflows, cloud native architectures, and performance optimization best practices from the Speedscale team.
A production bug survived two confident fixes and green tests. Replaying the captured request exposed the missing state and proved the real fix.
Testing AI applications with invented traffic looks fine until real users arrive. Then come the retries, the fallback models, and the token bill.
A rebuilt 2026 guide to Postman alternatives. Which API clients are still maintained, what they cost, and which one fits local-first, gRPC, or team workflows.
AI pushed throughput up 59%, yet median delivery got worse. The bottleneck moved to validation. How replaying production traffic in CI closes the gap.
Our v2 release looked clean in HTTP tests until we diffed the SQL workload — an N+1 loop, a startup migration, and 70 ms of extra DB time hiding in plain sight. Here's how to compare two releases without database access.
The trace was sampled out. I found the bug anyway — by filtering recorded traffic on the customer's email instead of a trace ID. Here's how to follow one request across four services with no trace IDs and no OpenTelemetry.
Metrics, logs, and traces were built for humans and cheap storage. AI inverts both assumptions, and the next maturity level is a deterministic replay sandbox.
A second run of our AI bug-fixing benchmark shows where captured traffic lifts agents toward 90%, why service maps barely help, and which bugs still fail.
Using 'production-similar' data in pre-production is a major security risk. Learn why traditional masking fails, where hidden PII hides, and how to fix it.
Skip hand-writing WireMock stubs. Speedscale records your real request and response traffic and exports it straight to WireMock mappings.