@cloudkinetix/bmad-enhanced
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Cloud-Kinetix enhanced fork of BMAD-METHOD - Breakthrough Method of Agile AI-driven Development with robust versioning and unified validation.
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YAML
name: multi-agent-orchestration
title: Multi-Agent System Orchestration Workflow
description: Comprehensive workflow for designing and implementing multi-agent AI systems with coordination and governance
type: orchestration
category: ai-development
estimated_time: 2-6 weeks depending on system complexity
agents:
- llm-architect
- llm-engineer
- llm-safety-governance
- architect
- dev
- qa
prerequisites:
- Individual agent designs completed
- Orchestration requirements defined
- Inter-agent communication patterns identified
- System boundaries established
startup_sequence:
- agent: llm-architect
task: orchestration-research
message: "Beginning multi-agent system design with coordination patterns research"
system_design_phase:
- id: 1.1
agent: llm-architect
task: coordination-pattern-research
outputs:
- Coordination patterns analysis
- Communication protocols
- State management strategies
- Conflict resolution approaches
decision_points:
- id: D1
name: Orchestration Pattern
description: Choose multi-agent coordination approach
- id: 1.2
agent: architect
task: system-architecture-design
inputs:
- Agent specifications
- Coordination patterns
outputs:
- System architecture diagram
- Communication infrastructure
- Data flow design
- Scalability plan
- id: 1.3
agent: llm-safety-governance
task: multi-agent-safety-design
outputs:
- Inter-agent safety protocols
- Collective behavior constraints
- Emergent behavior controls
- System-wide governance
agent_integration_phase:
- id: 2.1
agent: llm-engineer
task: agent-interface-definition
outputs:
- Agent API specifications
- Message formats
- Protocol definitions
- Error handling standards
- id: 2.2
agent: dev
task: orchestration-framework
inputs:
- Architecture design
- Interface definitions
outputs:
- Orchestration engine
- Message broker setup
- State management system
- Monitoring framework
- id: 2.3
agent: llm-engineer
task: agent-adaptation
repeats: per_agent
outputs:
- Adapted agent implementations
- Integration endpoints
- Communication handlers
- State synchronization
coordination_implementation:
- id: 3.1
agent: llm-engineer
task: implement-coordination-logic
outputs:
- Task routing engine
- Load balancing logic
- Conflict resolution
- Consensus mechanisms
decision_points:
- id: D2
name: Consensus Strategy
description: Choose decision-making approach
- id: 3.2
agent: dev
task: implement-orchestration-api
outputs:
- Orchestration API
- Client SDKs
- Admin interfaces
- Debugging tools
- id: 3.3
agent: llm-engineer
task: implement-learning-coordination
outputs:
- Collective learning framework
- Performance optimization
- Behavior adaptation
- Knowledge sharing
testing_phase:
- id: 4.1
agent: qa
task: integration-testing
outputs:
- Agent communication tests
- Coordination scenario tests
- Failure mode testing
- Performance benchmarks
- id: 4.2
agent: llm-engineer
task: emergent-behavior-testing
outputs:
- Behavior analysis
- Unexpected pattern detection
- Stability testing
- Edge case validation
- id: 4.3
agent: llm-safety-governance
task: system-safety-validation
outputs:
- Multi-agent safety tests
- Cascading failure analysis
- Governance compliance
- Risk assessment
optimization_phase:
- id: 5.1
agent: llm-architect
task: system-optimization-review
inputs:
- Test results
- Performance metrics
outputs:
- Optimization recommendations
- Architecture refinements
- Scaling strategies
- Cost optimization
- id: 5.2
agent: llm-engineer
task: coordination-optimization
outputs:
- Optimized routing algorithms
- Improved consensus mechanisms
- Enhanced error recovery
- Performance tuning
production_deployment:
- id: 6.1
agent: dev
task: deployment-orchestration
outputs:
- Kubernetes configurations
- Service mesh setup
- Auto-scaling policies
- Disaster recovery plan
- id: 6.2
agent: llm-engineer
task: production-monitoring
outputs:
- Agent health dashboards
- Coordination metrics
- System behavior tracking
- Anomaly detection
- id: 6.3
agent: llm-safety-governance
task: operational-governance
outputs:
- Operational procedures
- Incident response plan
- Audit requirements
- Compliance monitoring
decision_points:
- id: D1
step: 1.1
description: Select orchestration pattern
options:
- Centralized orchestrator
- Distributed coordination
- Hierarchical delegation
- Peer-to-peer negotiation
impacts:
- System complexity
- Failure resilience
- Scalability limits
- Latency characteristics
- id: D2
step: 3.1
description: Choose consensus mechanism
options:
- Voting-based consensus
- Leader election
- Quorum-based decisions
- Market-based coordination
impacts:
- Decision speed
- Fault tolerance
- Consistency guarantees
- Resource efficiency
- id: D3
step: 5.1
description: Optimization focus
options:
- Optimize for throughput
- Optimize for reliability
- Optimize for cost
- Balanced optimization
impacts:
- System performance
- Operating expenses
- User experience
- Maintenance burden
system_components:
- Agent registry and discovery
- Message broker/event bus
- State management service
- Coordination engine
- Monitoring and logging
- Admin dashboard
- Developer tools
- Security layer
success_criteria:
- All agents successfully integrated
- Coordination protocols working reliably
- System meets performance targets
- Safety controls validated
- Monitoring comprehensive
- Documentation complete
- Team trained on operations
outputs:
- Multi-agent system implementation
- Orchestration infrastructure
- Comprehensive test suite
- Safety validation reports
- Operational procedures
- Monitoring dashboards
- Deployment automation
- Training materials