The Myth of the Single AI Agent

When we started experimenting with AI automation, the intuitive idea was simple: one powerful agent that could handle everything.

In practice this failed quickly.

Large generalist agents became unpredictable. They jumped between responsibilities—planning in one moment, coding in the next, then evaluating their own work. Results were inconsistent. Context slipped. The same input could produce wildly different outputs depending on what the agent had just been doing.

What worked far better was a network of small, specialized agents, each responsible for a narrow task. A planner that plans. A researcher that researches. A coder that codes. A reviewer that reviews. Each agent does one thing well and hands off a structured artifact to the next.

Instead of one super-agent, we now rely on clear agent roles and structured handoffs between them. The system is more predictable because each piece has a bounded responsibility.

The lesson: intelligence scales through collaboration, not size.