There's a moment in every ambitious AI engineering project where you convince yourself that more agents means more power. I hit that moment early in building my Python orchestration framework — and I spent several painful weeks learning exactly why that intuition is wrong.
The seductive pitch: decompose complex tasks into specialized sub-agents, run them in parallel, let them coordinate. What actually happened was a reliability nightmare that taught me more about agentic architecture than any framework documentation ever could.
The Problem I Actually Built
My Python orchestration system was designed to automate complex, multi-step workflows — the kind that require planning, research, code generation, and validation to happen in a coherent sequence. Early on, I structured it as a web of parallel agents: a planner, several workers, a validator, and a synthesizer, all exchanging structured messages.
On paper it was elegant. In practice, it had three failure modes I couldn't engineer away:
