A shared coordination layer helped AI agents avoid repeat work
Running more than one AI agent at the same time can cause agents to undo each other’s work, ask for the same context again, or repeat work that was already done. A shared coordination layer was built so every agent could read and write the same working record. That record stored decisions, research, open issues, and modules, reaching about 850 notes in one month.
The tool’s own meter counted at least about 2 million tokens saved, and it did not count savings it could not measure, so the real number may be higher. Six agents worked in parallel while seeing each other’s work and staying inside their own roles. In one case, one agent was editing while another was deploying; both noticed the overlap and adjusted without human help.
The main workflow was simple: one sentence from the human was filed and tagged by an agent, then later agents picked up the topic without rebuilding the same context from scratch.
Key points
- Multiple can create waste when they repeat context or overwrite each other’s work.
- A shared coordination layer let agents read and write the same decisions, research, and open issues.
- About 850 notes were created in one month of real use.
- The tool counted at least about 2 million tokens saved.
- Six agents worked in parallel and handled at least one overlap without human input.