Epic: AI Agent Development #4
Description
🎯 Epic Overview
Parent Outcome: #1
Brief Description: Develop specialized AI agents that can analyze and process information from multiple data sources to generate comprehensive ticket refinements with all necessary context for engineering execution.
📋 Scope & Requirements
Functional Requirements:
- Build ticket analysis and context extraction agents
- Create feature breakdown generation capabilities
- Develop acceptance criteria generation agents
- Implement technical specification generation
- Create agent orchestration and workflow management
Non-Functional Requirements:
- Performance: Process tickets within 5 minutes
🏗️ Implementation Approach
Architecture: Multi-agent system with specialized roles and capabilities
Technology Stack: Python, LLM APIs (OpenAI/Claude), LangChain for agent orchestration
Integration Points: Data sources from Epic #3, Jira API for ticket updates
📊 Stories & Tasks
This epic will be implemented through the following stories:
- Story: Context extraction agent development
- Story: Feature breakdown generation agent
- Story: Acceptance criteria generation agent
- Story: Technical specification agent
- Story: Agent orchestration framework
🧪 Testing Strategy
- Agent functionality tests
- Output quality validation
- Integration testing with data sources
✅ Definition of Done
- All stories under this epic are completed
- All agents produce high-quality refinement output
- Agent orchestration works reliably
- Integration with data sources validated
- Engineering team acceptance testing passed
📝 Notes
Focus on creating agents that produce consistent, high-quality refinements that enable cold-start execution by engineering teams.