The Bounded Task Principle: Why Constrained AI Agents Outperform Open-Ended Ones

The Bounded Task Principle: Why Constrained AI Agents Outperform Open-Ended Ones Claude just found 22 vulnerabilities in Firefox in two weeks — including 14 high-severity ones. People are talking about model capability. They're missing the real lesson. It worked because the task was bounded. Defined scope. Defined output format. Defined success criteria. The agent wasn't asked to "improve Firefox security" — it was given specific parameters, a specific surface area, and a clear definition of what a finding looks like. That's the principle. And it applies to every agent you build. Why Vague Tasks Break Agents Most agents fail not because of model quality — but because the task spec is under-defined. When a task can mean five different things, an agent picks one interpretation. Often the wrong one. Then it optimizes hard for that interpretation while the user wanted something else entirely. Symptoms of a vague task spec: Agent loops longer than expected Output looks "reasonable" but miss