paseo-committee

Multi-agent planning breaks stalled workflow loops by coordinating two high-reasoning AI agents to perform root-cause analysis and synthesize a unified plan. This approach enforces phase gating across planning, implementation, and review to catch drift and resolve looping cycles.

Root-cause analysis in multi-agent decision-making is a process where parallel AI agents perform in-depth reasoning to step back from a problem. They maintain a shared plan to generate a robust, consolidated strategy for hard planning problems.

You coordinate parallel AI agents for complex planning tasks by launching multiple high-reasoning models simultaneously with your prompt. The system enforces phase gating and synthesizes their distinct outputs into a merged plan requiring user confirmation before implementation.

You should use a committee approach instead of single-agent planning for hard planning problems, stuck work, or looping cycles where single-agent approaches stall. It provides parallel planning and consolidated review to catch drift and resolve complex issues.

Yes, you can apply multi-agent workflow planning to resolve stuck work by launching two committee members in parallel. They perform in-depth reasoning and drift checking to produce a robust, synthesized plan with user confirmation for hard planning problems.

A limitation of using a multi-agent committee for workflow planning is the requirement to wait for convergence between parallel agents. The enforced phase gating across plan, implement, and review stages demands user confirmation, which may add overhead to straightforward tasks.