agent-coordination

You can coordinate multiple AI agents by using a shared markdown log that agents read and append to. This enforces a structured phase model—Research, Converge, Stabilize, Action-List, Approval, Execute, Closeout—ensuring agents follow predefined protocols for collaboration.

The multi-agent collaboration phase model includes Research, Converge, Stabilize, Action-List, Approval, Execute, and Closeout. Agents must follow these predefined phase rules when appending to the shared markdown log to ensure structured and efficient workflow progression.

Start a coordination session by triggering the workflow with a phrase like "Start a coordination session on the [topic]." This initializes a shared markdown log and prompts participating AI agents to follow the structured phase rules for project decision-making.

Yes, you can manage discussions between AI agents like Claude, Gemini, and Codex using structured collaboration. Agents interact by reading and appending to a shared markdown log, maintaining context across complex research and large-scale decision-making processes.

The multi-agent coordination workflow triggers on specific keywords or file mentions, such as "agent coordination", "coordination session", or specific mentions of coordination protocols. This activates the structured phase model and shared markdown logging.

Use a shared log for AI workflow coordination during complex projects requiring input from multiple AI agents. It is ideal for collaborative research or large-scale decision-making processes where maintaining continuous context across agents is required.