@cloudkinetix/bmad-enhanced

# CK LLM Agent Development Expansion Pack A comprehensive expansion pack for the BMAD Method that provides specialized tools, workflows, and best practices for developing production-ready LLM agents. This pack emphasizes safety, testing, observability, and modern LLM development practices. ## Overview The LLM Agent Development expansion pack equips teams with everything needed to build, test, deploy, and monitor LLM agents at scale. It includes specialized agents, proven workflows, comprehensive templates, and industry best practices for LLM development. ### Key Features - **Research-Driven LLM Agents**: Five expert agents with dynamic research and reasoning capabilities - **Adaptive Tool Selection**: Context-aware technology selection based on current landscape and requirements - **Dynamic Testing Frameworks**: Research-driven approach to selecting and configuring testing tools - **Intelligent Safety Assessment**: Continuous research integration for latest compliance and safety standards - **Future-Proof Architecture**: LLM-powered decision making that adapts to evolving LLM landscape - **Context-Aware Templates**: Dynamic template generation based on specific project needs ## Installation 1. Ensure you have BMAD CLI installed: ```bash npm install -g @bmad/method-cli ``` 2. Install the expansion pack: ```bash bmad install expansion ck-llm-agent-dev ``` 3. The installer will add all components to your `.bmad-core` directory ## Components ### Agents **NEW! 🧙 LLM Development Wizard** (`llm-wizard`) - Your friendly guide to LLM development - Helps you navigate the LLM development framework - Recommends the right specialist agent for your needs - Provides quick access to common workflows - Makes LLM development approachable for all skill levels 1. **LLM Architect** (`llm-architect`) - Research-driven system design and architecture expert - Researches current LLM architectural patterns and recommends optimal approaches - Designs scalable LLM agent architectures based on latest industry developments - Provides technology selection guidance through systematic research and evaluation - Plans multi-agent orchestration systems using current best practices 2. **LLM Engineer** (`llm-engineer`) - Dynamic implementation and optimization specialist - Researches and selects appropriate tools and frameworks for specific needs - Implements LLM agents using current best practices and emerging patterns - Conducts research-driven prompt engineering and optimization - Sets up testing frameworks based on evaluation of current options 3. **LLM Orchestrator** (`llm-orchestrator`) - Multi-agent coordination and workflow specialist - Coordinates complex multi-agent implementations - Manages team workflows and agent interactions - Provides project-level orchestration and planning - Ensures smooth collaboration between all LLM agents 4. **LLM Safety & Governance** (`llm-safety-governance`) - Adaptive safety and compliance specialist - Researches evolving LLM regulations and compliance requirements - Conducts safety assessments using latest methodologies and standards - Monitors emerging LLM threats and implements current mitigation strategies - Ensures regulatory compliance through continuous research and validation ### Workflows 1. **LLM Agent Greenfield** - Complete workflow for new LLM agent development 2. **LLM Agent Enhancement** - Workflow for improving existing agents 3. **Prompt Optimization** - Focused workflow for prompt improvement 4. **Multi-Agent System** - Workflow for building agent orchestration ### Templates - **LLM Agent Specification** - Dynamic agent requirements document with research-driven approach - **Prompt Library** - Context-aware prompt patterns and frameworks - **Evaluation Suite** - Adaptive testing framework configuration - **AI Architecture** - System architecture documentation with current best practices - **Safety Report** - Compliance-aware safety assessment and reporting - **Monitoring Dashboard** - Research-based observability setup guide - **Voice Agent Config** - Current voice/multimodal agent configuration patterns ### Tasks - **Prompt Testing Setup** - Configure PromptFoo for systematic testing - **Create Agent Spec** - Design comprehensive agent specifications - **Design Evaluation Suite** - Create testing frameworks - **Setup Observability** - Configure monitoring and logging - **Multi-Agent Orchestration** - Design agent communication systems - **Voice Agent Setup** - Configure voice interactions - **Safety Testing** - Run comprehensive safety assessments - **Performance Benchmarking** - Measure and optimize performance ### Checklists - **LLM Agent Readiness** - Production deployment checklist - **Prompt Quality** - Prompt engineering standards - **Safety Review** - Safety and alignment verification - **Performance Optimization** - Performance tuning checklist - **Production Deployment** - Deployment readiness verification ### Data Files - **AI Development Framework** - Research-driven development guidelines and decision criteria - **Prompt Engineering Patterns** - Adaptive prompt patterns with current techniques - **Safety Assessment Framework** - Dynamic safety considerations and compliance guidelines - **Tool Selection Framework** - Research methodology for evaluating and selecting AI development tools ## Quick Start ### NEW! Start with the LLM Wizard ```bash # Activate the LLM Development Wizard for guidance bmad agent llm-wizard # The wizard will help you: # - Choose the right specialist agent # - Access common workflows # - Get quick starts for your specific needs ``` ### 1. Start a New LLM Agent Project ```bash # Option A: Use the wizard for guidance bmad agent llm-wizard *start # Guides you to the Architect # Option B: Go directly to the LLM Architect bmad agent llm-architect # Research and plan your agent architecture *research-tech *analyze-context ``` ### 2. Design Your Agent ```bash # Create research-driven agent specification bmad agent llm-architect *strategy # Research and design system architecture *architecture ``` ### 3. Develop and Test ```bash # Research and implement with current best practices bmad agent llm-engineer *research-tools *implement # Research and configure safety measures bmad agent llm-safety-governance *research-standards *assess ``` ### 4. Deploy to Production ```bash # Research and set up monitoring bmad agent llm-engineer *monitor # Validate safety and readiness bmad agent llm-safety-governance *audit *checklist ``` ## Research-Driven Best Practices ### Dynamic Safety Assessment - Research latest safety methodologies before conducting reviews - Stay updated on emerging AI threats and mitigation strategies - Implement defense layers based on current threat landscape - Monitor for drift using latest detection techniques ### Adaptive Testing Strategy - Research current testing frameworks and select based on project needs - Test edge cases informed by latest AI failure mode research - Benchmark against current industry standards - Optimize based on research into effective evaluation methods ### Intelligent Observability - Research observability best practices for current AI architectures - Implement monitoring based on latest operational insights - Configure alerts using research-informed thresholds - Track metrics that current research identifies as most predictive ### Context-Aware Cost Management - Research current cost optimization strategies and tools - Select models based on systematic analysis of cost vs. performance - Implement caching strategies informed by latest efficiency research - Regular cost reviews using current benchmarking methodologies ## Research-Driven Tool Selection ### Testing Framework Approach The expansion pack provides research methodologies for selecting testing tools: - Framework evaluation criteria and selection process - Current testing methodology research and analysis - Systematic tool comparison and validation approaches - Implementation guidance based on project-specific needs ### Observability Solution Selection Research-driven approach to monitoring and observability: - Tool landscape analysis and evaluation frameworks - Requirements-based selection criteria - Integration complexity assessment methodologies - Performance and cost optimization research ### Technology Stack Research Systematic approach to infrastructure and deployment decisions: - Current technology landscape analysis - Performance benchmarking methodologies - Cost-benefit analysis frameworks - Integration and maintenance consideration research ## Common Use Cases ### 1. Customer Support Agent ````bash bmad workflow ai-agent-greenfield --type conversational-ai ```text ### 2. Data Analysis Agent ```bash bmad workflow ai-agent-greenfield --type analytical-agent ```` ### 3. Multi-Agent Research System ````bash bmad workflow multi-agent-system ```text ### 4. Voice Assistant ```bash bmad agent llm-orchestrator *create voice-agent-config ```` ## Troubleshooting ### Common Issues 1. **High Latency** - Check prompt complexity - Review caching strategy - Optimize model selection 2. **Safety Failures** - Review safety filters - Update prompt constraints - Add additional validation 3. **Cost Overruns** - Analyze token usage - Implement request batching - Use smaller models where appropriate ## Contributing We welcome contributions! Please see the main BMAD repository for contribution guidelines. ### Areas for Contribution - Additional agent types - New workflow patterns - Template improvements - Best practices documentation - Tool integrations ## Support - **Documentation**: See the `docs/` folder for detailed guides - **Issues**: Report issues in the main BMAD repository - **Community**: Join the BMAD Discord server - **Updates**: Follow @bmad_method for updates ## License This expansion pack is part of the BMAD Method and follows the same license terms. --- Built with ❤️ by the BMAD Team for the AI development community