You've become the switchboard between your AI agents

A developer building Memeri identifies a coordination problem when multiple AI coding agents work on the same project without shared context. The solution is a structured memory layer that provides a shared decision log, shared tasks, and quick grounding for new agents, enabling them to work from the same source of truth.

If you have started running more than one AI coding agent, you have probably hit a strange kind of friction. Each agent is capable on its own, but together they trip over each other. One makes a decision, another never sees it and quietly undoes it. Two of them solve the same problem in parallel. You end up as the human switchboard, carrying context from one to the next. It is tempting to blame the model. Usually that is not it. The models are fine. The problem is that they do not share anything. A single agent session is a closed world. It knows what is in its context window and nothing else. Open a second agent, in another tool or another tab, and it starts from zero. It cannot see what the first one decided, what is already done, or what you agreed an hour ago. There is no shared view, just several private ones. That is fine when you work with one agent at a time. It stops being fine the moment there are two or three, or a teammate and a couple of agents, all touching the same project. The cost of that fragmentation grows with every participant you add. Most people reach for the same workarounds, in roughly this order: Each of these helps a single agent remember more, when the real problem is that several agents cannot agree. This is the shift that matters. "My agent forgot" is a memory problem, and the labs are steadily solving it. "My agents do not know what each other did" is a coordination problem, and no model upgrade touches it. Coordination needs three things the private bubbles cannot provide on their own: In practice that looks like an append only decision log, so nothing is silently overwritten and you can see when something was superseded. Shared tasks that live outside any single chat. And a quick view of what was just worked on, so a fresh agent gets grounded in seconds rather than re explained from scratch. The interesting frontier right now is not a model that remembers more. It is the layer that lets many agents, and the humans alongside them, work from the same source of truth. That is the problem we are building Memeri around: structured memory and visible work, shared by every agent you connect over MCP, so opening a new agent means it already knows the project. If you run multiple agents day to day, the single most useful thing you can do, with or without a tool, is give them a shared and current record of what has been decided and what is done. It changes how they behave. If you want to try it, Memeri is in early access: https://memeri.ai https://memeri.ai