I run a company with 89 AI agents across 22 departments. Here is what I have learned about multi-agent coordination.

Not hypothetical. Not a research paper. This is what my company actually runs on, right now. ​ Some things that surprised me: ​ 1. DELEGATION IS THE BOTTLENECK, NOT INTELLIGENCE ​ The agents are smart enough. The hard part is knowing which agent to invoke for which task and how to coordinate their outputs. We built a "conductor" agent whose only job is orchestration -- it never does specialist work itself. ​ 2. AGENTS NEED EXPERIENCE TO GET GOOD ​ An agent invoked once is mediocre. An agent invoked 100 times with memory of past work is genuinely useful. The learning curve is real. ​ 3. DEPARTMENT STRUCTURE MATTERS ​ We tried flat coordination (any agent talks to any agent). It was chaos. Organizing into departments with manager agents who coordinate their team was the breakthrough. ​ 4. THE HUMAN IS STILL THE CEO ​ I am the CEO. The AI is the co-CEO. I set direction, it executes across the organization. The human-AI partnership IS the product. ​ 5. MOST "AI AGENT" PRODUCTS ARE JUST CHATBOTS ​ Real agents reason, delegate, fail, retry, and learn. If your "agent" is just an API call with a system prompt, it is not an agent. ​ Happy to answer questions about the architecture. What has your experience been with multi-agent systems? ​ ​