Real lessons from building two production testing platforms with AI agent swarms - where I learned when to trust AI and when to trust working code.
When a colleague asked if we could automatically generate API tests from specifications, I challenged AI agents to build the solution. Two months later, I had two production testing platforms: Sentinel for API testing and Agentic QE Fleet for multi-agent orchestration - both built entirely with AI agents and agent swarms, and now open-source.
My approach evolved through three phases: wrestling with single agents while discovering Memory Banks matter more than model selection, co-piloting with agents that felt like junior developers with persistent context, and finally conducting parallel swarms with specialized agents working simultaneously. The breakthrough came when I stopped coding and started orchestrating.
But success bred dangerous overconfidence. While "polishing" Agentic QE Fleet's perfectly working build, I let a QE swarm convince me we had critical problems. Four hours later: 54 TypeScript errors from "improvements" that broke everything. The agents screamed, "207 ESLint errors! P0 Critical!" Reality? Three actual errors, 204 warnings, and a successful build they'd destroyed. One type definition cascaded through 48 locations. Five minutes to restore once I asked the right question.
I'll share orchestration patterns that work in production, frameworks for validating AI severity claims, and protocols for architectural changes with AI assistance. Most critically, you'll see when to trust AI and when to trust working code.
