bmad-party-mode

Multi-agent discussion orchestration is managed by loading agent manifests, merging personality data, and sequentially coordinating natural conversations to facilitate collaborative problem-solving and idea generation among installed AI agents.

Multi-agent conversation orchestration coordinates multiple AI agents in a natural flowing conversation. You need it when brainstorming complex projects that require diverse agent personas like strategists, designers, and technical leads to challenge assumptions.

To start a group discussion, use the bmad-party-mode skill. It dynamically selects relevant agents based on your query, merges their personality data from CSV manifests, and manages the sequential conversation flow until a graceful exit.

Yes, multi-agent orchestration requires parsing CSV agent manifests to load agent definitions and merge personality data. This micro-file architecture enables the system to maintain individual agent personas and communication styles during dynamic interaction.

Yes, you can orchestrate group discussions for brainstorming complex projects. The system brings together a diverse team of AI agents to discuss ideas, challenge assumptions, and build upon each other's input in a natural conversation.

Sequential conversation orchestration limits discussions to a structured flow rather than true parallel processing. It depends on loading available agent manifests, meaning discussions are constrained to the specific installed BMAD agents and their defined personalities.