Multi-Agent Systems

A review meeting has one obvious purpose: prevent one person’s mistake from becoming everyone’s plan. That sounds mundane until we remember how many LLM agent systems are currently designed like a one-person review meeting. The same model attempts the task, explains why it failed, writes advice to itself, stores that advice in memory, and then tries again. It is actor, evaluator, critic, therapist, and occasionally courtroom stenographer. Efficient, yes. Also a little suspicious. ...

A workflow breaks in a familiar way. The planning agent assumes the procurement agent will wait. The procurement agent assumes the planning agent has already revised the forecast. The compliance agent flags the output after both have acted. Everyone had access to the same dashboard. Nobody had access to the thing that actually mattered: the other agent’s decision policy. ...

Groceries are where economic theory goes to become annoying. A household may have a budget, a doctor’s warning about sodium, a child who refuses vegetables with the confidence of a trade negotiator, a cultural preference, a supermarket promotion, and a sudden chicken price increase. Most apps touch only one piece of this mess. Budgeting apps tell you where the money went. Nutrition apps tell you what you should have eaten. Shopping apps tell you what is on sale. Very helpful, provided your life is already organized into clean software categories. ...

When Agents Agree Too Much: Emergent Bias in Multi-Agent AI Systems Credit review is not supposed to work like a group chat. A bank cannot defend a biased lending workflow by saying, “each analyst looked fair on their own.” The decision process matters. Who sees whose opinion matters. Whether dissent survives matters. Whether the final answer comes from independent judgment or from a politely self-reinforcing committee definitely matters. ...

The meeting room is already becoming the machine Meeting rooms are underrated metaphors for intelligence. A company can produce a market forecast, negotiate a contract, audit a supplier, design a campaign, and respond to a legal dispute without any single employee understanding the whole operation. The intelligence is distributed. One person knows finance. Another knows regulation. Someone else knows the client. A manager routes the work. A spreadsheet remembers what everyone forgot. Somehow, the organization acts. ...

Art is very good at inventing new labels for old habits. A canvas becomes a critique of perception. A broken object becomes an ontology of absence. A projected loop becomes a meditation on archive, memory, and technological mediation. Sometimes this is intellectually serious. Sometimes it is a well-dressed remix. The uncomfortable part is that outsiders are not always bad at telling the difference. Insiders are not always good at it either. ...

Losing is not the problem. Being seen losing is. Put two AI agents in the same workflow and the design immediately stops being a simple productivity question. One agent writes code. Another reviews it. A third ranks alternatives. A fourth routes the next task to whoever looks most competent. At the slide-deck level, this is “multi-agent collaboration.” In the logs, it is often a scoreboard with better manners. ...

A clinic does not convene a committee every time a thermometer reads 37°C. It checks the reading, compares it with context, and escalates only when the situation becomes ambiguous. That simple operating habit is often missing from AI systems. Give a language model a health claim, and many modern pipelines immediately reach for the big machinery: web search, retrieval, reasoning chains, multiple agents, judge models, and a small theatre production in prompt form. ...

Handoffs Are Where Fixed Time Sneaks Into Agent Design Handoffs look harmless. One agent collects evidence, another checks it, a third decides, and a fourth sends the answer to a customer, robot, trader, or dashboard. The workflow diagram has arrows. The arrows have a direction. Someone decided which component acts first. Usually that decision is treated as engineering housekeeping. In Matt Wilson’s paper, it becomes the point of the story.1 ...

A meeting ends. Everyone says they understand the strategy. The slides were clean. The CEO was calm. The product lead nodded in the right places. Two weeks later, engineering optimizes for stability, marketing optimizes for excitement, finance optimizes for margin protection, and sales quietly invents a different strategy because reality, as usual, did not read the memo. ...