Multi-Agent Systems

Deal flow is rarely scarce. Attention is. A venture-capital team may receive hundreds of startup introductions, each surrounded by founder biographies, investor histories, comparable companies, co-investment relationships, sector narratives, and enthusiastic claims about an inevitable Series A. The practical problem is not obtaining more evidence. It is deciding which fragments deserve serious attention before the partnership meeting begins. ...

Approval meetings exist for a reason. An analyst proposes an investment. Legal identifies a compliance problem. Operations notices that the promised delivery date is fictional. Someone with decision authority compares the evidence, resolves what can be resolved, and escalates what cannot. Now remove that final decision-maker. Give every participant access to APIs, databases, payment systems, and customer communications. Allow them to act autonomously. Then ask the same participant who proposed the decision to explain why it was sensible. ...

Handoffs are where promising projects quietly become expensive. A biologist identifies a plausible target. A chemistry team designs a molecule that appears to bind it. Weeks later, pharmacology discovers that the molecule is poorly absorbed, rapidly cleared, or inconveniently toxic. The result travels back upstream as a report, perhaps accompanied by a meeting, several caveats, and the medicinal-chemistry equivalent of “please try again.” ...

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. ...