@cloudkinetix/bmad-enhanced

# Create AI Agent Development Workflow Plan Task ## Purpose Guide users through AI agent development workflow selection and create a detailed plan that emphasizes research-driven design, safety governance, and production readiness with comprehensive testing and monitoring. ## Task Instructions ### 1. Understand AI Development Goals [[LLM: Start with discovery about AI agent requirements and constraints]] Ask the user: 1. **Agent Type & Complexity**: - Single-purpose agent or multi-agent system? - Autonomous or human-in-the-loop? - Real-time or batch processing? - Integration complexity? 2. **Development Scope**: - **New Agent Design**: Research and design from scratch - **Implementation**: Build designed agent - **Optimization**: Improve existing prompts/performance - **Multi-Agent**: Orchestrate multiple agents - **Production Deployment**: Full production readiness 3. **Constraints & Requirements**: - Performance requirements (latency, throughput) - Safety and compliance needs - Budget constraints - Timeline expectations - Team AI expertise level ### 2. Recommend AI Development Workflow Based on answers, recommend: **Design Workflows:** - `llm-agent-design` - Complete research-driven design - `llm-architecture-planning` - System architecture focus **Implementation Workflows:** - `llm-agent-implementation` - Single agent build - `prompt-optimization` - Prompt improvement cycle - `multi-agent-orchestration` - Multi-agent systems **Specialized Workflows:** - `voice-agent-development` - Voice interface agents - `safety-first-development` - High-risk domains ### 3. Create LLM Development Workflow Plan [[LLM: Generate plan with LLM-specific considerations]] ````markdown # LLM Agent Development Workflow Plan: {{Workflow Name}}  **Created Date**: {{current date}} **Agent Purpose**: {{agent-purpose}} **Safety Requirements**: {{safety-level}} **Performance Targets**: {{latency}}, {{throughput}} ## Development Objectives {{Clear description of what the LLM agent will accomplish}} ## Technical Requirements - [ ] Model selection criteria defined - [ ] Performance benchmarks established - [ ] Safety constraints documented - [ ] Integration points identified - [ ] Monitoring requirements specified ## Workflow Steps with Research Gates ### Phase 1: Research & Design  - [ ] Step 1: Domain Research  - **Research Focus**: Industry best practices, existing solutions - **Output**: Research report with recommendations - **Decision**: Architecture pattern selection  - [ ] Step 2: Safety Requirements  - **Governance Level**: {{safety-level}} - **Compliance Needs**: {{requirements}} - **Output**: Safety framework document ### Phase 2: Prompt Engineering  - [ ] Step 3: Initial Prompt Design  - **Approach**: Research-driven patterns - **Testing**: Comprehensive test scenarios - **Iteration**: Minimum 3 cycles recommended - [ ] Step 4: Optimization Cycle  - **Metrics**: Quality, latency, token usage - **Method**: A/B testing with statistical validation - **Exit Criteria**: Performance targets met ### Phase 3: Implementation  - [ ] Step 5: Core Implementation  - **Safety Controls**: Input validation, output filtering - **Observability**: Logging, metrics, tracing - **Error Handling**: Graceful degradation - [ ] Step 6: Integration Development  - **APIs**: REST/GraphQL with rate limiting - **Security**: Authentication, authorization - **Documentation**: OpenAPI/GraphQL schemas ### Phase 4: Testing & Validation  - [ ] Step 7: Safety Testing  - **Adversarial Testing**: Prompt injection, jailbreaks - **Bias Detection**: Fairness evaluation - **Boundary Testing**: Edge case validation - [ ] Step 8: Performance Testing  - **Load Testing**: Concurrent user simulation - **Latency Analysis**: P50, P95, P99 metrics - **Resource Profiling**: Memory, CPU, costs ### Phase 5: Production Readiness  - [ ] Step 9: Monitoring Setup  - **Dashboards**: Real-time agent health - **Alerts**: Performance degradation, errors - **Analytics**: Usage patterns, success rates - [ ] Step 10: Deployment Preparation  - **Strategy**: Canary, blue-green, feature flags - **Rollback**: Automated procedures - **Documentation**: Runbooks, incident response ## AI-Specific Decision Points 1. **Model Selection**  - GPT-4 class (high quality, high cost) - GPT-3.5 class (balanced) - Specialized models (domain-specific) - Fine-tuned models (custom) 2. **Prompt Strategy**  - Zero-shot with instructions - Few-shot with examples - Chain-of-thought reasoning - Multi-step orchestration 3. **Safety Level**  - Basic filtering (public facing) - Comprehensive governance (enterprise) - Mission-critical controls (healthcare, finance) ## Testing Strategy ### Prompt Testing - [ ] Functionality coverage: Core use cases - [ ] Edge cases: Boundary conditions - [ ] Adversarial: Security testing - [ ] Performance: Latency and throughput ### System Testing - [ ] Integration: End-to-end flows - [ ] Load: Concurrent usage - [ ] Failover: Resilience testing - [ ] Monitoring: Alert validation ## Risk Mitigation ### Technical Risks - Model API availability → Fallback strategies - Prompt drift → Version control - Performance degradation → Monitoring - Cost overruns → Budget alerts ### Safety Risks - Harmful outputs → Content filtering - Bias amplification → Regular audits - Privacy leaks → Data sanitization - Misuse → Usage monitoring ## Success Metrics - [ ] Response quality score > {{threshold}} - [ ] P95 latency < {{target}}ms - [ ] Safety incident rate < {{threshold}} - [ ] User satisfaction > {{target}}% - [ ] Cost per request < ${{target}} ## Monitoring Plan ```yaml dashboards: - agent_health: Response times, error rates, availability - usage_analytics: Request volume, user patterns, feature usage - safety_monitoring: Filter triggers, anomalies, incidents - cost_tracking: Token usage, API costs, resource consumption alerts: - performance: Latency spike, error rate increase - safety: Harmful content detected, unusual patterns - availability: Service degradation, API failures - budget: Cost threshold exceeded ``` ```` ## Next Steps 1. Review technical requirements with team 2. Validate safety requirements with stakeholders 3. Set up development environment 4. Begin with: `@ai-architect *task domain-research` --- _AI Development Plan Active: Follow research gates and safety checkpoints throughout_ ```` ### 4. AI Workflow Variations **For Rapid Prototypes**: - Simplified safety controls - Basic testing only - Fast iteration cycles - Minimal documentation **For Production Systems**: - Comprehensive safety framework - Full testing suite - Extensive monitoring - Complete documentation **For Regulated Industries**: - Enhanced governance - Audit trail requirements - Compliance validation - Formal approval gates ### 5. Provide AI-Specific Guidance ```text Your AI Agent Development workflow plan is ready! Key Considerations: - 🔬 Research-driven approach at each phase - 🛡️ Safety controls integrated throughout - 📊 Performance metrics tracked continuously - 🔄 Iterative optimization expected Before starting: 1. Confirm model access and API keys 2. Set up testing infrastructure 3. Review safety requirements with team 4. Establish success metrics Ready to begin development? ```` ## Success Criteria The AI workflow plan succeeds when: 1. Research gates clearly defined 2. Safety checkpoints integrated 3. Testing strategy comprehensive 4. Performance targets specified 5. Monitoring plan detailed 6. Risk mitigation addressed ## Integration with AI Agents AI agents should: 1. Check for workflow plans on startup 2. Validate against research gates 3. Enforce safety checkpoints 4. Track optimization iterations 5. Update progress metrics