Count the windows. If you are building with AI coding agents seriously, you probably have more than three open right now: one running Claude Code on the backend, one running Cursor on the front end, maybe a third handling something in parallel. There is a notes file somewhere capturing the decisions you made two sessions ago that none of the agents know about. There is a scratch doc tracking what you told one agent that another one needs to know. You are moving fast, faster than you ever did before AI tools existed. But every time you switch contexts, you spend thirty seconds to a minute re-orienting yourself before you can give a useful instruction. You do that twenty, thirty, forty times a session.
That is not productivity. That is overhead wearing productivity’s clothes.
The agents are not the problem. The problem is what happens between them — in the switching, the re-orienting, the manual context passing, the conflict catching you do because nothing else will. That is where the hours go. And it gets worse, not better, as you add more agents to the mix.
The Terminal Graveyard
The first thing that happens when you start building seriously with AI tools is that the windows multiply. One terminal for the feature you are actively building. One monitoring what deployed. One running a long operation you started twenty minutes ago that you keep glancing at. Another for the part of the project you were mid-thought on before this became more urgent.
Individually, each one is doing exactly what it is supposed to do. Collectively, they create a workspace that demands constant triage. Before you can act, you have to assess: which window needs attention right now, which one finished, which one is waiting for input you forgot to give it. The agents are working. You are managing the management of the agents. These are not the same activity, and the second one does not show up in anyone’s before-and-after productivity story.
The terminal graveyard is the natural end state of scaling AI tool usage without changing the underlying workflow structure. It is what happens when you add more agents to a process that was not designed to hold them. The tools get more capable. The overhead of orchestrating them manually grows right alongside.
The Context Tax Nobody Measures
Here is something that does not show up in the benchmarks when AI coding tools publish their speed comparisons: the cost of the switch.
Every time you move from one agent to another, you pay a context tax. You need to re-establish where that agent is in the work. What it knows. What it does not know. What was decided in the other window that this agent needs to factor in before its next instruction. If the agents were working with shared context, this tax would not exist. But they are not. Every agent is stateless relative to every other agent. You are the only thing connecting them.
Here is what that looks like in a real session. You are directing the UI agent to build a form component. The data agent, running in another window, finishes the schema it was working on and the field names changed. The agents did not catch this conflict. You did, because you happened to glance at the right window at the right moment. Now you stop, digest the schema change, update your mental model of what the UI agent needs to know, switch windows, and re-inject that context manually before the next instruction. A three-minute detour for something that should have been handled automatically by the infrastructure underneath the agents, not by you.
Multiply that across every decision that ripples across agent boundaries in a session. The field naming change. The architectural choice made in one context that other agents are now building against without knowing it. The client requirement you established early that none of the agents will carry forward unless you state it again. A builder running three agents is not three times as productive as a builder running one. The context tax on the switching consumes a significant portion of whatever the additional agents provide, and nobody is talking about that part.
You Are the Shared Memory
The architectural reality of multi-agent AI development is simple and uncomfortable: every agent is stateless relative to every other agent. Claude Code does not know what Cursor just built. Cursor does not know what decision Claude Code made three prompts ago. The agents are powerful individually. Together, they share nothing.
Except you.
You are the shared memory. You are the one who knows the field naming convention was settled in session one and cannot change now without breaking three other things. You are the one tracking which decisions are locked and which are still open. You are the one who remembers that the client wanted the dashboard to load in under two seconds, a requirement established early that none of the agents will honor unless you re-inject it. When you switch from one agent to another, you carry all of that in your head and re-inject it manually, every time, every session.
This is sustainable for one agent. It is manageable for two. By three or more agents on a real project, the cognitive load of being the shared memory begins to visibly compete with the actual work of deciding what to build next. You are not blocked by the agents. You are blocked by the weight of connecting them. The more capable the tools get, the more clearly this becomes the bottleneck — because tool speed is no longer the constraint, and what remains is you.
More Agents, Same Bottleneck
The counterintuitive thing about this problem is that it compounds as you add more capacity. The instinct when you feel slow is to reach for more — another agent, a better model, a faster tool. But if the bottleneck is you — the human carrying context between stateless agents — adding more agents does not remove the bottleneck. It deepens it.
More agents means more windows to triage, more context to carry, more conflicts to catch before they become rework, more switching overhead between contexts that do not talk to each other. The output ceiling moves slightly upward as the models get more capable. The floor does not move, because the floor is set by how much coordination overhead you can absorb before it starts eating the real work.
This is the wall that serious AI builders hit and rarely see coming. The tools are genuinely good. The workflow surrounding them has not kept up. You move faster in any individual session. But the system as a whole — you plus multiple isolated agents — does not scale cleanly with the ambition of the project. At a certain point, the coordination cost of adding another agent outweighs what it contributes, and you have more terminals open but less clarity about what is actually moving forward.
The answer is not fewer agents. It is a different architecture for how agents relate to each other. Specifically: shared context that persists across sessions, roles that divide the work rather than duplicate it, and a layer that handles the integration overhead so you are directing the build instead of administrating it.
What the Workflow Actually Needs
The builders moving fastest right now are not running more agents than everyone else. They are using the same agents — Cursor, Claude Code, Codex — with better infrastructure underneath: shared context that persists between sessions, clear roles so work gets divided instead of duplicated, and a workspace where their job is to direct instead of coordinate.
Coordinating is the job you have right now. Moving information between isolated agents. Catching conflicts before they become rework. Re-establishing context every time a new session opens. Deciding what each agent needs to know before you can give it a useful instruction.
Directing is what the work should feel like. You describe the outcome. Agents take their roles. You review what ships.
That shift is not about the models getting smarter. It is about building a layer above them that handles the shared memory and parallel execution so you are not the one doing it manually every session. Once that layer exists, the terminal graveyard closes. The context tax disappears. Adding more agents actually makes you faster instead of more buried. For agencies managing multiple client builds simultaneously, this is where the real capacity change happens — and what that workflow looks like in practice is a different picture than what most people are running today.
Ozawa is that layer. The workspace where you direct multiple AI coding agents in parallel, shared context persists across sessions, and the integration overhead is handled at the infrastructure level instead of in your head. ozawa.app