@cloudkinetix/bmad-enhanced/expansion-packs/ck-llm-agent-dev/workflows/llm-agent-implementation.yaml

Cloud-Kinetix enhanced fork of BMAD-METHOD - Breakthrough Method of Agile AI-driven Development with robust versioning and unified validation.

name: llm-agent-implementation
title: LLM Agent Implementation Workflow
description: Structured workflow for implementing production-ready LLM agents with continuous testing and optimization
type: implementation
category: llm-development
estimated_time: 1-4 weeks depending on complexity

agents:
  - llm-engineer
  - llm-architect
  - llm-safety-governance
  - dev
  - qa

prerequisites:
  - Completed AI agent design specification
  - Development environment configured
  - AI model access secured
  - Testing infrastructure ready
  - Monitoring tools available

startup_sequence:
  - agent: llm-engineer
    task: implementation-kickoff
    message: "Initializing AI agent implementation with safety-first approach"

foundation_phase:
  - id: 1.1
    agent: llm-engineer
    task: setup-observability
    outputs:
      - Logging infrastructure
      - Metrics collection
      - Tracing configuration
      - Error tracking setup

  - id: 1.2
    agent: dev
    task: scaffold-implementation
    inputs:
      - Agent specification
      - Architecture design
    outputs:
      - Project structure
      - Core interfaces
      - Configuration framework
      - Dependency setup

  - id: 1.3
    agent: llm-engineer
    task: prompt-testing-setup
    outputs:
      - Prompt testing harness
      - Version control for prompts
      - A/B testing framework
      - Performance baselines

prompt_engineering_phase:
  - id: 2.1
    agent: llm-engineer
    task: initial-prompt-implementation
    inputs:
      - Prompt design templates
      - Use case scenarios
    outputs:
      - Base prompt implementations
      - Context management logic
      - Token optimization
      - Error handling
    decision_points:
      - id: D1
        name: Prompt Strategy
        description: Choose prompt optimization approach

  - id: 2.2
    agent: llm-engineer
    task: prompt-iteration-cycle
    repeats: until_satisfactory
    outputs:
      - Refined prompts
      - Performance metrics
      - Edge case handling
      - Optimization report

  - id: 2.3
    agent: qa
    task: prompt-quality-testing
    outputs:
      - Test results
      - Quality metrics
      - Failure analysis
      - Improvement recommendations

core_implementation_phase:
  - id: 3.1
    agent: llm-engineer
    task: implement-agent-logic
    inputs:
      - Validated prompts
      - System interfaces
    outputs:
      - Core agent implementation
      - Tool integrations
      - State management
      - Error recovery logic

  - id: 3.2
    agent: dev
    task: api-implementation
    outputs:
      - REST/GraphQL endpoints
      - Authentication layer
      - Rate limiting
      - Request validation

  - id: 3.3
    agent: llm-engineer
    task: implement-safety-controls
    inputs:
      - Safety requirements
      - Governance guidelines
    outputs:
      - Input validation
      - Output filtering
      - Audit logging
      - Circuit breakers

testing_phase:
  - id: 4.1
    agent: llm-engineer
    task: performance-benchmarking
    outputs:
      - Latency metrics
      - Throughput analysis
      - Resource utilization
      - Optimization opportunities

  - id: 4.2
    agent: qa
    task: comprehensive-testing
    outputs:
      - Unit test suite
      - Integration tests
      - End-to-end scenarios
      - Load test results

  - id: 4.3
    agent: llm-safety-governance
    task: safety-testing
    outputs:
      - Adversarial testing results
      - Bias detection report
      - Safety boundary validation
      - Compliance verification

optimization_phase:
  - id: 5.1
    agent: llm-engineer
    task: performance-optimization
    inputs:
      - Benchmark results
      - Resource constraints
    outputs:
      - Optimized implementations
      - Caching strategies
      - Batch processing
      - Resource efficiency

  - id: 5.2
    agent: llm-architect
    task: scalability-review
    outputs:
      - Scaling strategies
      - Architecture refinements
      - Deployment patterns
      - Capacity planning

production_readiness:
  - id: 6.1
    agent: llm-engineer
    task: monitoring-setup
    outputs:
      - Production dashboards
      - Alert configurations
      - SLA definitions
      - Runbook documentation

  - id: 6.2
    agent: llm-safety-governance
    task: final-safety-review
    outputs:
      - Production safety checklist
      - Incident response plan
      - Rollback procedures
      - Approval documentation

  - id: 6.3
    agent: dev
    task: deployment-preparation
    outputs:
      - CI/CD pipelines
      - Infrastructure as code
      - Environment configurations
      - Deployment scripts

decision_points:
  - id: D1
    step: 2.1
    description: Select prompt optimization strategy
    options:
      - Manual iteration with testing
      - Automated prompt optimization
      - Hybrid approach with human review
      - A/B testing in production
    impacts:
      - Development timeline
      - Quality assurance process
      - Resource requirements
      - Risk management

  - id: D2
    step: 3.3
    description: Safety control strictness
    options:
      - Minimal controls (fast, flexible)
      - Balanced controls (recommended)
      - Strict controls (slow, safe)
      - Custom per use case
    impacts:
      - User experience
      - Safety guarantees
      - Performance overhead
      - Maintenance burden

  - id: D3
    step: 5.1
    description: Optimization priorities
    options:
      - Optimize for latency
      - Optimize for cost
      - Optimize for quality
      - Balanced optimization
    impacts:
      - User satisfaction
      - Operating costs
      - System complexity
      - Scaling characteristics

outputs:
  - Production-ready AI agent implementation
  - Comprehensive test suite
  - Performance benchmarks
  - Safety validation reports
  - Monitoring infrastructure
  - Deployment pipelines
  - Operational documentation
  - Incident response procedures

success_criteria:
  - All functional requirements met
  - Performance SLAs achievable
  - Safety tests passing
  - Security review approved
  - Monitoring fully operational
  - Documentation complete
  - Team trained on operations

deployment_options:
  - Staged rollout with monitoring
  - Blue-green deployment
  - Canary release
  - Feature flag activation