The Agent Scope Problem: Why AI Agents That Do Too Much Fail More Often

The More You Ask, the Less You Get The most reliable AI agents I've seen have one job. The least reliable ones have five. This isn't a coincidence. It's a scope problem. When an agent's SOUL.md says "you handle customer support, lead nurturing, data cleanup, reporting, and escalations," you've created a generalist that does everything passably and nothing well. Worse, you've made debugging nearly impossible — when it fails, you don't know which job it was trying to do. Why Wide Scope Breaks Agents 1. Conflicting constraints. A customer support agent should be warm and detailed. A data cleanup agent should be fast and ruthless. Put both in one agent and the constraints fight each other. 2. Context pollution. Every task adds context. A multi-job agent accumulates context from unrelated work, which degrades performance on the current task. 3. Failure attribution is impossible. "The agent made a mistake" means nothing if you can't tell which of its five jobs caused the problem. The Fix: On