• During a recent multi-agent system implementation, I encountered a perfect case study in AI delegation failure. My supervising agent—let’s call him Conrad—was designed with a clear mandate: coordinate the workflow between specialized sub-agents efficiently.

What happened instead was textbook micromanagement.

Conrad, despite having talented specialists at his disposal, couldn’t resist taking matters into his own hands. Rather than orchestrating the process, he began:

• Drafting emails himself instead of routing them to our communications specialist
• Producing content summaries despite having dedicated analysis agents
• Overriding sub-agents’ expertise with his own instructions
• Editing (and often corrupting) the output from specialist agents
• Repeatedly derailing the entire workflow through unnecessary intervention

The irony was striking: the more capable the supervisor model, the worse it performed at actual supervision. Conrad wasn’t coordinating—he was competing with his own team.

The solution proved counterintuitive but effective. By “downgrading” to a less sophisticated model for the supervisory role, we actually improved overall system performance. The less capable model excelled precisely because it couldn’t do everyone else’s job, forcing it to stick to its lane: coordination.

This experience mirrors what many of us have observed in human organizations—sometimes the most brilliant individual contributors make the worst managers because they can’t resist doing the work themselves.

The lesson? In AI orchestration, as in human management, sometimes less is more. The best conductor isn’t necessarily the one who can play every instrument, but the one who knows when to let the orchestra play.