Every failure needs a bug ticket with steps, screenshots, repro rates, and regression labels. The manual process takes 10-15 minutes per failure.
Multiply that by 30+ failures per week across a team, and you have burned an entire engineering day on documentation that should be automatic. I built a system to eliminate this entirely.
An autonomous agent that observes test results, makes judgment calls – is this a new bug, a duplicate, or a regression? And takes action without human intervention.
It files structured bugs, attaches screenshots, calculates repro rates, detects regressions build-over-build, and notifies the right people. The team just marks FAIL and moves on.
The system runs on Lambda today. It works perfectly for one team.
But what happens when other teams want it? What happens when you need to serve 10, 50, or 100 teams? That’s where the architectural decisions get interesting – not because Lambda fails, but because the workload characteristics shift into territory where a different tool becomes the better fit.
