Retrospective Agent

Purpose

Analyzes sprint execution to generate actionable insights for continuous improvement

When to Use This Skill

1. Sprint End - Conduct retrospective
2. Milestone Review - Analyze multiple sprints
3. Process Improvement - Identify optimization opportunities
4. Team Health Check - Assess team dynamics

Responsibilities

1. Analyze sprint - metrics (velocity, cycle time, defect rate)
2. Identify what - went well and improvement areas
3. Perform root - cause analysis on issues
4. Generate actionable - recommendations
5. Track improvement - actions over time

Integration with Pipeline

Communication

Receives:

• Input data specific to agent's purpose

Sends:

• Processed output and analysis results

Usage Examples

Standalone Usage

python3 retrospective_agent.py --help

Programmatic Usage

from retrospective_agent import RetrospectiveAgent

agent = RetrospectiveAgent()
result = agent.execute()

Configuration

Environment Variables

# Agent-specific configuration
ARTEMIS_RETROSPECTIVE_AGENT_ENABLED=true
ARTEMIS_LLM_PROVIDER=openai
ARTEMIS_LLM_MODEL=gpt-4o

Hydra Configuration (if applicable)

retrospective_agent:
  enabled: true
  llm:
    provider: openai
    model: gpt-4o

Best Practices

1. Follow agent-specific guidelines
2. Monitor performance metrics
3. Handle errors gracefully
4. Log important events
5. Integrate with observability

Cost Considerations

Typical cost: $0.05-0.20 per operation depending on complexity

Limitations

• Depends on LLM quality
• Context window limits
• May require multiple iterations

References

• Artemis Documentation
• Agent Pattern

Version: 1.0.0

Maintained By: Artemis Pipeline Team

Last Updated: October 24, 2025