bmad-create-architecture

To define architecture decisions for consistent AI agent implementation, establish explicit mappings from requirements to architecture, including patterns, structure, and boundaries. This provides traceable decisions and commitments to ensure deterministic implementation across future projects.

Architecture decision mapping for AI workflows works by guiding AI agents through a stepwise architecture lifecycle with versioned rationales. This provides explicit mappings from requirements to architecture, ensuring traceable decisions and commitments for consistent implementation.

To guide AI agents through stepwise architecture lifecycle decisions, provide a project context and steer the architecture workflow by responding to prompts. This facilitates parallel decision-making across teams while maintaining versioned rationales and traceable commitments.

Yes, you can use this architecture workflow for parallel team collaboration. It facilitates parallel architecture decision-making across teams by providing traceable decisions and commitments that ensure consistent implementation by AI agents throughout the project lifecycle.

AI agent implementation lacks consistency without architecture decisions because there are no explicit mappings from requirements to structure, patterns, and boundaries. Without versioned rationales and traceable commitments, agents cannot achieve deterministic implementation across future projects.

Relying on AI agents without traceable architecture decisions limits implementation consistency and deterministic outcomes. Without explicit requirement mappings, versioned rationales, and documented compatibility, agents cannot maintain traceable commitments across future projects and teams.