13 KiB
13 KiB
Decision Architecture Workflow - Technical Reference
Module: BMM (BMAD Method Module) Type: Solutioning Workflow
Overview
The Decision Architecture workflow is a complete reimagining of how architectural decisions are made in the BMAD Method. Instead of template-driven documentation, this workflow facilitates an intelligent conversation that produces a decision-focused architecture document optimized for preventing AI agent conflicts during implementation.
Core Philosophy
The Problem: When multiple AI agents implement different parts of a system, they make conflicting technical decisions leading to incompatible implementations.
The Solution: A "consistency contract" that documents all critical technical decisions upfront, ensuring every agent follows the same patterns and uses the same technologies.
Key Features
1. Starter Template Intelligence ⭐ NEW
- Discovers relevant starter templates (create-next-app, create-t3-app, etc.)
- Considers UX requirements when selecting templates (animations, accessibility, etc.)
- Searches for current CLI options and defaults
- Documents decisions made BY the starter template
- Makes remaining architectural decisions around the starter foundation
- First implementation story becomes "initialize with starter command"
2. Adaptive Facilitation
- Adjusts conversation style based on user skill level (beginner/intermediate/expert)
- Experts get rapid, technical discussions
- Beginners receive education and protection from complexity
- Everyone produces the same high-quality output
3. Dynamic Version Verification
- NEVER trusts hardcoded version numbers
- Uses WebSearch to find current stable versions
- Verifies versions during the conversation
- Documents only verified, current versions
4. Intelligent Discovery
- No rigid project type templates
- Analyzes PRD to identify which decisions matter for THIS project
- Uses knowledge base of decisions and patterns
- Scales to infinite project types
5. Collaborative Decision Making
- Facilitates discussion for each critical decision
- Presents options with trade-offs
- Integrates advanced elicitation for innovative approaches
- Ensures decisions are coherent and compatible
6. Consistent Output
- Structured decision collection during conversation
- Strict document generation from collected decisions
- Validated against hard requirements
- Optimized for AI agent consumption
Workflow Structure
Step 0: Validate workflow and extract project configuration
Step 0.5: Validate workflow sequencing
Step 1: Load PRD and understand project context
Step 2: Discover and evaluate starter templates ⭐ NEW
Step 3: Adapt facilitation style and identify remaining decisions
Step 4: Facilitate collaborative decision making (with version verification)
Step 5: Address cross-cutting concerns
Step 6: Define project structure and boundaries
Step 7: Design novel architectural patterns (when needed) ⭐ NEW
Step 8: Define implementation patterns to prevent agent conflicts
Step 9: Validate architectural coherence
Step 10: Generate decision architecture document (with initialization commands)
Step 11: Validate document completeness
Step 12: Final review and update workflow status
Files in This Workflow
- workflow.yaml - Configuration and metadata
- instructions.md - The adaptive facilitation flow
- decision-catalog.yaml - Knowledge base of all architectural decisions
- architecture-patterns.yaml - Common patterns identified from requirements
- pattern-categories.csv - Pattern principles that teach LLM what needs defining
- checklist.md - Validation requirements for the output document
- architecture-template.md - Strict format for the final document
How It's Different from Old architecture
| Aspect | Old Workflow | New Workflow |
|---|---|---|
| Approach | Template-driven | Conversation-driven |
| Project Types | 11 rigid types with 22+ files | Infinite flexibility with intelligent discovery |
| User Interaction | Output sections with "Continue?" | Collaborative decision facilitation |
| Skill Adaptation | One-size-fits-all | Adapts to beginner/intermediate/expert |
| Decision Making | Late in process (Step 5) | Upfront and central focus |
| Output | Multiple documents including faux tech-specs | Single decision-focused architecture |
| Time | Confusing and slow | 30-90 minutes depending on skill level |
| Elicitation | Never used | Integrated at decision points |
Expected Inputs
-
PRD (Product Requirements Document) with:
- Functional Requirements
- Non-Functional Requirements
- Performance and compliance needs
-
Epics file with:
- User stories
- Acceptance criteria
- Dependencies
-
UX Spec (Optional but valuable) with:
- Interface designs and interaction patterns
- Accessibility requirements (WCAG levels)
- Animation and transition needs
- Platform-specific UI requirements
- Performance expectations for interactions
Output Document
A single architecture.md file containing:
- Executive summary (2-3 sentences)
- Project initialization command (if using starter template)
- Decision summary table with verified versions and epic mapping
- Complete project structure
- Integration specifications
- Consistency rules for AI agents
How Novel Pattern Design Works
Step 7 handles unique or complex patterns that need to be INVENTED:
1. Detection
The workflow analyzes the PRD for concepts that don't have standard solutions:
- Novel interaction patterns (e.g., "swipe to match" when Tinder doesn't exist)
- Complex multi-epic workflows (e.g., "viral invitation system")
- Unique data relationships (e.g., "social graph" before Facebook)
- New paradigms (e.g., "ephemeral messages" before Snapchat)
2. Design Collaboration
Instead of just picking technologies, the workflow helps DESIGN the solution:
- Identifies the core problem to solve
- Explores different approaches with the user
- Documents how components interact
- Creates sequence diagrams for complex flows
- Uses elicitation to find innovative solutions
3. Documentation
Novel patterns become part of the architecture with:
- Pattern name and purpose
- Component interactions
- Data flow diagrams
- Which epics/stories are affected
- Implementation guidance for agents
4. Example
PRD: "Users can create 'circles' of friends with overlapping membership"
↓
Workflow detects: This is a novel social structure pattern
↓
Designs with user: Circle membership model, permission cascading, UI patterns
↓
Documents: "Circle Pattern" with component design and data flow
↓
All agents understand how to implement circle-related features consistently
How Implementation Patterns Work
Step 8 prevents agent conflicts by defining patterns for consistency:
1. The Core Principle
"Any time multiple agents might make the SAME decision DIFFERENTLY, that's a pattern to capture"
The LLM asks: "What could an agent encounter where they'd have to guess?"
2. Pattern Categories (principles, not prescriptions)
- Naming: How things are named (APIs, database fields, files)
- Structure: How things are organized (folders, modules, layers)
- Format: How data is formatted (JSON structures, responses)
- Communication: How components talk (events, messages, protocols)
- Lifecycle: How states change (workflows, transitions)
- Location: Where things go (URLs, paths, storage)
- Consistency: Cross-cutting concerns (dates, errors, logs)
3. LLM Intelligence
- Uses the principle to identify patterns beyond the 7 categories
- Figures out what specific patterns matter for chosen tech
- Only asks about patterns that could cause conflicts
- Skips obvious patterns that the tech choice determines
4. Example
Tech chosen: REST API + PostgreSQL + React
↓
LLM identifies needs:
- REST: URL structure, response format, status codes
- PostgreSQL: table naming, column naming, FK patterns
- React: component structure, state management, test location
↓
Facilitates each with user
↓
Documents as Implementation Patterns in architecture
How Starter Templates Work
When the workflow detects a project type that has a starter template:
- Discovery: Searches for relevant starter templates based on PRD
- Investigation: Looks up current CLI options and defaults
- Presentation: Shows user what the starter provides
- Integration: Documents starter decisions as "PROVIDED BY STARTER"
- Continuation: Only asks about decisions NOT made by starter
- Documentation: Includes exact initialization command in architecture
Example Flow
PRD says: "Next.js web application with authentication"
↓
Workflow finds: create-next-app and create-t3-app
↓
User chooses: create-t3-app (includes auth setup)
↓
Starter provides: Next.js, TypeScript, tRPC, Prisma, NextAuth, Tailwind
↓
Workflow only asks about: Database choice, deployment target, additional services
↓
First story becomes: "npx create t3-app@latest my-app --trpc --nextauth --prisma"
Usage
# In your BMAD-enabled project
workflow architecture
The AI agent will:
- Load your PRD and epics
- Identify critical decisions needed
- Facilitate discussion on each decision
- Generate a comprehensive architecture document
- Validate completeness
Design Principles
- Facilitation over Prescription - Guide users to good decisions rather than imposing templates
- Intelligence over Templates - Use AI understanding rather than rigid structures
- Decisions over Details - Focus on what prevents agent conflicts, not implementation minutiae
- Adaptation over Uniformity - Meet users where they are while ensuring quality output
- Collaboration over Output - The conversation matters as much as the document
For Developers
This workflow assumes:
- Single developer + AI agents (not teams)
- Speed matters (decisions in minutes, not days)
- AI agents need clear constraints to prevent conflicts
- The architecture document is for agents, not humans
Migration from architecture
Projects using the old architecture workflow should:
- Complete any in-progress architecture work
- Use
architecturefor new projects - The old workflow remains available but is deprecated
Version History
1.3.2 - UX specification integration and fuzzy file matching
- Added UX spec as optional input with fuzzy file matching
- Updated workflow.yaml with input file references
- Starter template selection now considers UX requirements
- Added UX alignment validation to checklist
- Instructions use variable references for flexible file names
1.3.1 - Workflow refinement and standardization
- Added workflow status checking at start (Steps 0 and 0.5)
- Added workflow status updating at end (Step 12)
- Reorganized step numbering for clarity (removed fractional steps)
- Enhanced with intent-based approach throughout
- Improved cohesiveness across all workflow components
1.3.0 - Novel pattern design for unique architectures
- Added novel pattern design (now Step 7, formerly Step 5.3)
- Detects novel concepts in PRD that need architectural invention
- Facilitates design collaboration with sequence diagrams
- Uses elicitation for innovative approaches
- Documents custom patterns for multi-epic consistency
1.2.0 - Implementation patterns for agent consistency
- Added implementation patterns (now Step 8, formerly Step 5.5)
- Created principle-based pattern-categories.csv (7 principles, not 118 prescriptions)
- Core principle: "What could agents decide differently?"
- LLM uses principle to identify patterns beyond the categories
- Prevents agent conflicts through intelligent pattern discovery
1.1.0 - Enhanced with starter template discovery and version verification
- Added intelligent starter template detection and integration (now Step 2)
- Added dynamic version verification via web search
- Starter decisions are documented as "PROVIDED BY STARTER"
- First implementation story uses starter initialization command
1.0.0 - Initial release replacing architecture workflow
Related Documentation: