agent-team-coordination

Multi-role AI team coordination in a single agent environment is achieved by orchestrating sequential role-switching through a shared blackboard and file-based handoffs. This simulates team dynamics using pre-built role templates like researcher, architect, and executor.

A shared blackboard approach for single-agent workflows uses a central file-based protocol to preserve context across different AI roles. It enables phase-driven handoffs so that each specialist role can access and build upon previous task outputs.

File-based handoffs for AI project management tasks require explicit role templates, persistent memory integration, and handoff artifacts. This setup enforces sequence, traceability, and recoverability when switching between roles like planner and executor.

Yes, this multi-agent handoff protocol works with Antigravity, Cursor, and Claude Code setups. It is designed specifically for single-agent environments that require structured task execution and coordinated multi-role workflows.

To enforce sequence and traceability when switching AI roles, you need persistent memory integration and file-based handoff artifacts. These components ensure that context is preserved and recoverable across phase-driven task transitions.

The best way to simulate team dynamics in a single LLM agent is by using sequential role-switching guided by pre-built role templates. A central blackboard and phase-driven handoffs manage the shared context and workflow progression.