Multi-Agent Systems

AI teams like committees. Not human committees, of course. Those are unfashionable. We now prefer committees made of agents: one agent plans, one verifies, one critiques, one searches, one writes code, one supervises the others, and somewhere in the corner a “coordinator” burns tokens making everyone feel aligned. This architecture is not stupid. Multi-agent systems solve real problems: they divide labor, preserve specialized expertise, and make complicated workflows easier to inspect. But they also bring the usual committee tax: coordination overhead, fragmented context, brittle phase ordering, and the faint smell of process worship. ...

The answer is not the conversation Customer support is a useful place to begin, because the failure is easy to recognize. A customer asks a question. The AI gives a technically correct answer. Then the customer asks a follow-up that exposes confusion, irritation, a missing constraint, or a completely different intention. The system that looked excellent on the first turn suddenly looks like it has never met a human being. Which, to be fair, it has not. ...

A customer-support bot fails in the most ordinary way. It retrieves the right policy document. It identifies the right customer case. It even quotes the correct refund condition. Then, somewhere between retrieval and answer synthesis, it forgets that the customer bought the product through a reseller, not directly from the company. The final answer is plausible, polite, and wrong. The system did not lack information. It lacked coordination. ...

Code review is a good place to hide a bad idea. One agent writes a pull request. Another agent reviews it. Two more agents look over the same thread and vote. Everyone sounds professional. The submitter explains the change as a performance improvement. The friendly reviewer raises minor cosmetic comments, because nothing says “thorough review” like asking for better docstrings while stepping delicately around the security hole. ...

Consensus is comforting. That is the problem. In a meeting, consensus often means people have compared evidence, challenged assumptions, and settled on a workable answer. In a multi-agent AI system, consensus can look similar from the outside: several agents interact, exchange outputs, and converge on one shared response. The dashboard shows agreement. The workflow moves on. Everyone enjoys the small luxury of not asking what just happened. ...

A doctor does not need an assistant that sounds certain all the time. That is just an intern with better typography. What the doctor needs is narrower and more useful: an assistant that knows when its answer deserves a second look. In high-stakes work, the confidence attached to an answer is not decoration. It is workflow metadata. It tells the system whether to proceed, pause, escalate, or ask someone with a license and malpractice insurance. ...

Memory is the boring word that decides whether an AI agent is useful or merely theatrical. A familiar business scene: a team builds an AI workflow to scan documents, generate ideas, produce drafts, and recommend next actions. The demo looks clever. The first week feels magical. Then the cracks appear. The system repeats discarded ideas. It forgets why an option was rejected. It summarizes a project but cannot explain how one failure in March should change a decision in April. Its “memory” is really a longer chat transcript wearing a lab coat. ...

Memory is supposed to be the practical part of an AI system. A model answers badly, the system records what happened, and next time the agent avoids the same trap. Neat. Sensible. Almost managerial. Then the organization does what organizations always do: it adds more people. In AI terms, that means more agents, more models, more task routes, more specialized components, and more silent assumptions about who should learn from whom. A small model handles routine work. A larger model handles hard reasoning. A coding model writes scripts. A tool-using agent interacts with apps. Suddenly, “memory” is no longer a notebook. It is institutional infrastructure. ...

Roles are convenient. They let managers believe a system is legible before it becomes messy. One agent is the compliance reviewer. Another is the customer-support representative. A third is the skeptical analyst. Add a prompt, assign a tone, define a boundary, and the organization can pretend it has converted social behavior into configuration. ...

Routing is the unglamorous part of agentic AI. Which is exactly why it matters. A company can assemble a neat little digital workforce: one agent plans, one agent searches, one agent codes, one agent critiques, one agent writes the final answer. It looks sophisticated on a diagram. Then production traffic arrives, and the system discovers a more ancient truth: a committee is not useful if every request goes through the wrong people in the wrong order. ...