supabuilder

You can orchestrate multiple AI agents for product development by using a sequential pipeline with file-based handoffs and persistent wiki knowledge, ensuring context accumulates in dedicated folders rather than chat history.

The best way to manage AI agent handoffs during the software design lifecycle is through file-based state management, using handoff.md and NEXT_PHASE.md files to enforce a two-phase Think-Deliver cycle and maintain traceable governance.

Yes, a multi-agent workflow can handle both new product features and legacy migrations, supporting mission types including new-module, new-feature, revamp, pivot, integrate, migrate, scale, enhancement, and quick-fix.

A multi-agent pipeline maintains persistent knowledge across product initiatives by storing artifacts in dedicated product-wiki and code-wiki folders, allowing agents to build on prior missions without relying on conversation history.

Yes, you need to run a workspace setup to initialize the directory scaffolding and mission templates at your project root before the six-agent pipeline can govern setup, provenance, and file-based handoffs.

The full product development lifecycle requires six specialized AI agents: Strategist, PM, Designer, Architect, TechPM, and QA, operating in a fixed sequence from strategy formulation to quality assurance.