The leader that AI agents need

Most agentic systems don't fail because of the model or because AI doesn't work. They fail because nobody designed the coordination properly.

They're like employees: a junior needs detailed instructions, a senior acts with little context. If you throw a junior into the jungle, don't complain later when they do what you didn't expect.

Multi-agent systems are teams

Multi-agent systems work like teams: they need clear roles, success criteria, mechanisms to escalate blockers, and an orchestrator that synchronizes them and keeps the focus on the overall objective.

They need leaders. The seat-warmers will be the next token-burners.

The difference between agent systems and human teams is that there are no meetings, no standups, and no waiting.

Can you imagine having to wait for the standup to use your next Claude Code session? Or waiting until Thursday's meeting with the client to give them the requirements? Or waiting two weeks to define what goes into the next sprint?

Agents don't wait. They receive instructions, execute, report back, and take on the next task. Like a newly hired employee at their dream company who gets paid well to work on an exciting project.

Leadership principles that transfer

If you're a good leader of humans, your agents will thank you. The same principles apply:

• Clear context: Agents need to understand why, not just what. The more context you provide, the better their decisions.
• Defined boundaries: What can they decide on their own? When should they escalate? Without this, agents either block on everything or go rogue.
• Success criteria: How do you know the output is good? If you can't define it for a human, you can't define it for an agent.
• Feedback loops: Agents need to know when they got it right and when they didn't. This is how you iterate on prompts and configurations.

The companies that will get the most out of agentic AI are the ones that already know how to lead well. The tool is new, but the skill is ancient.