multi-agent-coordination

Coordinate multiple AI agents by establishing shared memory and defining ownership, dependency graphs, and task status. This prevents conflicts and streamlines handoffs across Git-based collaboration and Cortex-driven orchestration.

Manage task handoffs and dependencies for parallel AI agents by creating an AGENCY.md memory backbone to document tasks and dependencies, then start the optional real-time coordinator to manage claims with TTL-based reservations.

Yes, you can use Git-based workflows for multi-agent orchestration to manage dependencies and prevent conflicts. This approach provides centralized ownership and task handoffs for complex projects using Cortex-driven orchestration.

A real-time coordination server is optional but enables fast, parallel agent sessions and TTL-based reservations. Without it, you can still coordinate agents using shared memory and dependency graphs via the AGENCY.md file.

Shared memory works by using AGENCY.md as a centralized backbone to document tasks, dependencies, and ownership status. This allows multiple AI agents to coordinate claims and handoffs without overwriting each other's work.

Avoid using a centralized coordinator for multi-agent collaboration if your project lacks complex dependency graphs or parallel agent sessions. Simple sequential tasks may not require shared memory orchestration or TTL-based reservations.