Universal AI agent framework for agentic agile-driven development with progressive disclosure documentation optimized for AI agents
Comprehensive examples of BMad Method in action across different project types and team configurations.
Context: Building a SaaS MVP from scratch with tight timeline (5-day sprint)
Timeline: Day 1 planning, Days 2-5 development
# Morning: Project Brief and PRD
# Load team-fullstack bundle in ChatGPT/Claude
*agent analyst
*create-project-brief
# 30 minutes: Complete project brief
# Output: docs/project-brief.md
*agent pm
*create-prd
# 2 hours: Complete PRD with 3 epics, 12 stories
# Interactive elicitation creates comprehensive requirements
# Output: docs/prd.md
# Afternoon: Architecture
*agent architect
*create-architecture
# 1 hour: Full-stack architecture document
# Defines tech stack, database schema, API structure
# Output: docs/fullstack-architecture.md
# Evening: Validation and Sharding
*agent po
*shard-doc
# 15 minutes: Documents sharded into manageable chunks
# Output: docs/prd/ and docs/architecture/ directories# Load team-ide-minimal in Cursor/Claude Code
# Each day: 2-3 stories
*agent sm
*draft
# Creates story with full context:
# - Requirements from sharded PRD
# - Architecture sections (tech-stack, coding-standards, etc.)
# - Previous story learnings
# Output: docs/stories/1.1.user-authentication.md
*agent dev
*develop-story
# Implements story sequentially:
# - Reads story file (contains ALL context)
# - Implements tasks in order
# - Updates Dev Agent Record section
# - Never loads PRD/architecture (story has everything)
# Output: Implemented code + updated story file
*agent qa
*review
# Reviews implementation:
# - Checks against acceptance criteria
# - Validates code quality
# - Makes gate decision (PASS/CONCERNS/FAIL/WAIVED)
# Output: docs/qa/1.1-user-authentication-qa-gate.md
# If QA feedback exists:
*agent dev
*review-qa
# Dev addresses issues and updates story
# QA reviews again until PASSResult: MVP ready in 5 days with complete documentation, all requirements met, and quality gates passed.
Context: Adding new features to existing 10-year-old codebase with complex dependencies
Challenge: Minimize risk while extending functionality
# Flatten entire codebase to XML for context
npx bmad-method flatten -i . -o legacy-system.xml
# Creates 50MB XML file with complete codebase structure
# Includes all files, dependencies, and relationships*agent pm
*create-prd
# Uses brownfield-prd-tmpl.yaml
# References legacy-system.xml for context
# Creates enhancement PRD with:
# - Integration points identified
# - Risk assessment
# - Rollback strategies
# Output: docs/brownfield-prd.md
*agent architect
*create-architecture
# Documents integration points:
# - Existing system architecture
# - New feature architecture
# - Integration patterns
# - Technical debt identified
# Output: docs/brownfield-architecture.md*agent sm
*draft
# Uses create-brownfield-story task
# Gathers context from:
# - Brownfield PRD
# - Existing codebase patterns (from flattened XML)
# - Integration requirements
# - Safety checks and rollback plans
# Output: docs/stories/brownfield-feature.md
*agent dev
*develop-story
# Implements with safety checks:
# - Existing functionality protection
# - Rollback plan documented
# - Risk mitigation strategies
# - Integration testing approach
# Output: Safe implementation with minimal riskResult: Safe enhancement with minimal risk, complete documentation of changes, and rollback plan ready.
Context: Multiple developers working on different stories in parallel
Challenge: Maintain context without conflicts
*agent sm
*draft
# Creates 1.1.user-authentication.md
# Story includes complete context from:
# - Epic 1 requirements
# - Architecture sections needed
# - No dependencies on other stories
*agent dev
*develop-story
# Implements 1.1 independently
# Story file contains all needed context
# No need to read PRD or architecture*agent sm
*draft
# Creates 2.1.product-listing.md
# SM agent:
# - Detects 1.1 in progress (not done)
# - Creates 2.1 (different epic, can work in parallel)
# - Includes learnings from 1.1 if available
# - Loads appropriate architecture sections
*agent dev
*develop-story
# Implements 2.1 independently
# Story contains all needed context
# No conflicts with Developer A's workResult: Both developers work independently, stories contain all needed context, no conflicts or context loss.
Context: QA finds critical issues, dev must fix before proceeding
Challenge: Systematic issue resolution with quality assurance
*agent qa
*review
*gate
# Gate Decision: FAIL
# Issues identified:
# 1. Security: Password stored in plain text
# 2. Missing: Input validation
# 3. Test coverage: 45% (target: 80%)
# Output: docs/qa/1.1-user-authentication-qa-gate.md*agent dev
*review-qa
# Dev agent:
# 1. Reads QA feedback in story file
# 2. Prioritizes: Security first (critical)
# 3. Fixes password storage (implements bcrypt)
# 4. Adds input validation (all endpoints)
# 5. Increases test coverage to 85%
# 6. Updates Dev Agent Record with changes
# Output: Updated implementation + story file*agent qa
*review
*gate
# Gate Decision: PASS
# All issues resolved:
# - Password encryption implemented
# - Input validation added
# - Test coverage: 85% (exceeds target)
# Story complete and ready for productionResult: All critical issues resolved, quality gates passed, story ready for production.
Context: Very small change (< 4 hours) to existing system
Approach: Streamlined workflow for quick enhancements
# For very small changes (< 4 hours)
*agent pm
*create-brownfield-story
# Creates focused story for single-session completion:
# - Minimal PRD documentation
# - Integration context included
# - Safety checks embedded
# - Rollback plan documented
# Output: docs/stories/brownfield-quick-fix.md
*agent dev
*develop-story
# Implements with:
# - Focused scope
# - Integration safety checks
# - Quick validation
# Output: Quick enhancement completed safelyResult: Small enhancement completed in single session with proper documentation and safety checks.
Context: New team member learning BMad Method
Approach: Use Orchestrator for guided workflow execution
# Load Orchestrator
*help
# Shows available agents and workflows
*workflow-guidance
# Get help selecting appropriate workflow
# Orchestrator asks questions:
# - New or existing project?
# - Full-stack, service, or UI only?
# - Team size and experience?
# Recommendation: greenfield-service
*workflow greenfield-service
# Orchestrator guides through workflow:
# - Loads appropriate agents in sequence
# - Provides context at each step
# - Explains what each agent does
*plan
# Create detailed plan before starting
# Orchestrator outlines:
# - All steps in workflow
# - Expected outputs
# - Time estimates
# - Dependencies
*plan-status
# Check progress through workflow
# Shows:
# - Completed steps
# - Current step
# - Remaining steps
# - Blockers (if any)Result: New team member successfully completes workflow with guided assistance.
Context: Complex project requiring multiple agents in sequence
Approach: Orchestrator manages agent handoffs
# Orchestrator manages agent handoffs
*agent analyst
*create-project-brief
# Analyst creates project brief
# Output: docs/project-brief.md
*agent pm
# Orchestrator transforms into PM
*create-prd
# PM uses project brief to create PRD
# Output: docs/prd.md
*agent architect
# Orchestrator transforms into Architect
*create-architecture
# Architect uses PRD to create architecture
# Output: docs/architecture.md
*agent po
# Orchestrator transforms into PO
*shard-doc
# PO shards PRD and architecture
# Output: docs/prd/ and docs/architecture/Result: Seamless handoff between agents, each building on previous work, no context loss.
Context: Game development project using Godot expansion pack
Approach: Use domain-specific expansion pack
# Install expansion pack
npx bmad-method install --expansion-packs bmad-godot-game-dev
# Expansion pack provides:
# - Game-specific agents (Game Designer, Level Designer)
# - Game development tasks
# - Godot-specific templates
# - GDScript and C# patterns
# Use game-specific workflow
*workflow greenfield-game
# Workflow includes:
# - Game design document creation
# - Level design specifications
# - Godot project structure
# - GDScript coding standards
*agent game-designer
*create-game-design-doc
# Creates game design document with:
# - Core mechanics
# - Level progression
# - Asset requirements
# Output: docs/game-design.md
*agent dev
*develop-story
# Implements with Godot-specific context:
# - GDScript patterns
# - Scene structure
# - Node architecture
# - Game-specific best practicesResult: Domain-specific development with specialized agents and patterns for game development.
Install with Tessl CLI
npx tessl i tessl/npm-bmad-method