Why your AI agents redo each other's work (and what fixes it)
25 September 2026
If you run more than one AI coding agent on the same project, you have felt this. Each agent is fine on its own. Together they collide. One makes a decision, another quietly undoes it. Two solve the same problem in parallel. You end up as the person carrying context from one window to the next.
It is tempting to blame the model. Usually the models are fine. The problem is that nothing they figure out reaches the others.
Your files are shared. Your understanding is not.
Here is the part that trips people up. The agents already share your files. Claude Code and Codex can both read the same repo. Sharing the code is solved.
What is not shared is the understanding around the code. The decision one agent made ten minutes ago about how to structure the auth flow. The constraint the other one discovered. The thing you ruled out an hour ago. That lives in one agent's context, and only there. The next agent reads the same files and reaches a different conclusion, because it never saw the reasoning.
Redoing each other's work is what it looks like when understanding does not transfer.
The fixes people try
Most people reach for the same things.
A CLAUDE.md or AGENTS.md. Great for stable rules. Useless for what got decided this week.
Pasting context between windows. Works, briefly, and makes you the bottleneck. You are now the switchboard.
A bigger context window. Still one agent's context. The other agent does not get it.
Each of these treats the symptom. None of them make the understanding belong to the project.
What actually fixes it
The fix is for understanding to live somewhere every agent reads from and writes to. Not a chat. The project.
When one agent commits a decision, grounded in the code it touched, the next agent inherits it. It does not have to be told. It opens the project and the decision is already part of what the project knows about itself. Two agents working the same codebase stop being two private worlds and start working from one picture.
That is what Naetive does. It is not a coordination layer that schedules your agents. It is the shared understanding they all inherit, so they stop redoing each other's work because they finally know what each other decided.