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