@cloudkinetix/bmad-enhanced
Version:
Cloud-Kinetix enhanced fork of BMAD-METHOD - Breakthrough Method of Agile AI-driven Development with robust versioning and unified validation.
39 lines (38 loc) • 1.86 kB
YAML
workflow:
id: llm-agent-enhancement
name: Research-Driven LLM Agent Enhancement
description: Streamlined workflow for improving existing AI agents using research-driven optimization and enhancement techniques.
type: enhancement
project_types:
- performance-optimization
- feature-enhancement
- safety-improvement
- integration-upgrade
- model-migration
- cost-optimization
approach: Research current enhancement techniques and apply evidence-based improvements with systematic validation
key_phases:
assess:
description: Research and analyze current agent performance
agents: [llm-engineer, llm-safety-governance]
actions:
- Research current optimization techniques and assessment methodologies
- Analyze existing agent performance and identify improvement opportunities
- Investigate enhancement patterns relevant to the specific use case
- Prioritize improvements based on impact and feasibility
enhance:
description: Apply research-backed enhancement techniques
agents: [llm-engineer, llm-architect]
actions:
- Research and implement optimization techniques based on current best practices
- Apply enhancement strategies appropriate for the identified opportunities
- Validate improvements using research-backed testing methodologies
- Document enhancement rationale and implementation approach
validate:
description: Test and verify enhancement effectiveness
agents: [qa, llm-safety-governance]
actions:
- Execute comprehensive testing using current validation standards
- Verify safety and compliance requirements are maintained
- Validate performance improvements against established benchmarks
- Document results and lessons learned for future enhancements