Finding something in a large system is a messy activity, and the mess is the problem. When an agent goes looking for the one file that handles a particular flow, or the one paragraph in a long reference that answers a question, it does not arrive there in a straight line. It searches, reads candidates, follows a lead that goes nowhere, backs out, and…

A long-running agent will be interrupted. This is not an edge case to defend against at the margins; it is the ordinary weather of any task that runs long enough to matter. The process is killed by a deploy. A rate limit stalls a tool call past its timeout. A downstream service returns an error the agent cannot get past. A…

Every agent accumulates state as it runs: the plan it is working from, the outputs of the steps it has finished, the results of the tools it has called, the facts it retrieved to make a decision. That state has to live somewhere, and there are only two categories of somewhere. It can sit inside the context the model is already reading, or…

In a single-agent system, the risky moments are the tool calls: the points where the model reaches out and something in the world might not behave. In a multi-agent system, a second class of risk appears, and it is easy to miss because nothing about it looks like an error. It lives at the boundary between one agent and the next. Every…

Inside a single agent, coordination is almost free. Everything the model has read, decided, or produced sits in one context, and any later step can reach back and use it without anyone arranging for that to happen. The moment work is split across separate agents, that convenience is gone and most people do not notice it has left. Each agent runs in its…

Once a system holds more than one agent, the design question quietly shifts. It stops being about what any individual agent can do and becomes a question about the connective tissue between them: who hands work to whom, where authority sits, and what any single node can observe about the whole. Borrowed from distributed systems, the word for that arrangement is topology, and…

Every time an agent delegates a task, it creates a second actor with its own context, its own tools, and its own capacity to do the wrong thing. How much of the system that second actor can see, touch, and break is its blast radius, and the mistake most people make is treating that radius as a side effect of the task…

The defining mistake in multi-agent design is building an orchestrator that does work. It feels natural: the orchestrator is the smartest, most context-rich component in the system, so why not let it handle the hard parts itself and delegate only the scraps? Because the moment the coordinating agent starts executing, it stops being able to coordinate well. Its context fills with the…