AI coding agents burn tokens when they silently break code and keep building on top of broken changes. The solution isn't better prompt engineering — it's giving agents a feedback loop via E2E tests running against real cluster dependencies. mirrord enables this by letting a local process connect to a Kubernetes cluster without deploying, so agents can run existing E2E tests against real databases, queues, and services and get pass/fail feedback in seconds. When multiple agents or developers share a staging cluster, mirrord's traffic filtering, queue splitting, and database branching keep sessions isolated. The result is agents that self-correct in one or two iterations instead of twenty, dramatically reducing token spend.
Table of contents
E2E tests as agent guardrails #What this looks like in practice #Scaling this across a team #Prompt optimization matters less than you think #Sort: