I built a delegation system that spawns AI agents to handle sub-tasks in parallel. Quality sweeps. Code audits. Checking every SDK directory for dead links. The idea: spin up cheap local agents, let them work, collect results. They kept failing. Not crashing — just stopping. No output. No error. 600 seconds of silence, then a timeout. I assumed the tasks were too complex. I assumed parallel delegation was unreliable. I never checked what model I was actually giving them. The Root Cause My delegation system was configured to use a small local model. Fine for single-turn questions. Useless for multi-step tool loops. A quality sweep isn't one tool call. It's: find the directory, list the files, search each one, flag issues, report results. That's five sequential steps, each dependent on the last. The small model lost coherence after the second call. The first step worked. By the third, it was hallucinating or hanging. Meanwhile, the main agent handled the exact same tasks in minutes. Same instructions.…