BMad (Build More Architect Dreams) is a structured approach to running projects with AI. Instead of chaotic coding with a chatbot, you get a process where each phase builds context for the next, and AI knows what, why, and how to build.
Most developers using AI just start coding. The agent generates something, changes something, adds something. But nobody knows what stage the project is at, what has been decided, what architectural decisions were made and why. BMad solves this problem. Each phase ends with documentation stored directly in the git repository, accessible to all AI agents and developers on the team. This isn’t “prompt clicking.” It’s systematic work where every decision is documented and every step has its place.
Before anyone starts coding, you launch the brainstorming module with AI agents. Together you break down the problem, check what competitors offer, and explore which technologies are viable. The agent guides you through the entire process and organizes findings into concrete documents: a product brief, market research, technical research, and domain research reports. This way the entire team starts with a shared understanding of the problem.
The Product Manager agent helps you specify requirements: who you’re building for, which features are critical, what the priorities and success criteria are. The result is a PRD document that becomes the single source of truth for the entire team. In parallel, the UX Designer agent guides you through interface design: user flows, key screens, interactions. All before anyone touches code, because changes on paper cost a fraction of changes in a finished product.
The Architect agent helps design the system architecture: tech stack, module structure, integrations, database. Every decision is documented with justification. Then you divide everything into epics and stories, each with a clear scope and acceptance criteria. Finally, the agent validates implementation readiness: whether all stories have sufficient context for the Developer agent to implement without guessing.
This is the most important phase and the one where BMad shows the greatest value. All the context gathered in previous phases means AI agents don’t guess. They know what to build, how to build it, and why.
Sprint planning breaks down epics into concrete sprints with prioritized stories. Then the cycle begins: the Developer agent picks up a story, reads the project context, and implements it. After each story, the Code Review agent verifies quality, security, and architectural compliance. The cycle repeats for all stories in the sprint. After the sprint ends, a retrospective allows the team to draw conclusions and adjust course for the next iteration.
Sprint cycle
BMad works with the most popular AI-assisted programming tools. All project documentation lives in the code repository, so agents have access to it at all times. Project management tools like Jira and GitHub connect through MCP servers, enabling task synchronization and progress tracking without leaving the terminal.
I run training for development teams where BMad forms the foundation of the entire workflow. Participants learn not just the methodology but also tool configuration, AI agent orchestration, and working on real projects. The training ends with a ready, working development environment, not notes.
The program covers the entire ecosystem: from AI basics, through MCP servers and code review, to advanced multi-agent coordination.
See the full training programLet’s talk about how to organize your team’s work with AI.
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