AI Optimization Flywheel
AI Optimization Flywheel
A continuous improvement cycle consisting of four phases: Train (strengthen knowledge), Test (validate changes), Deploy (roll out updates), and Analyze (measure performance). Each cycle compounds the next, driving systematic AI Agent enhancement over time.
Continuous Improvement Through Systematic Cycles
The AI Optimization Flywheel is a repeatable four-phase process for continuously improving AI Agent performance. Unlike one-time optimization efforts, the flywheel creates compound improvements where each cycle builds on the previous one, driving systematic enhancement over time.
The Four Phases
1. Train - Strengthen knowledge, expand coverage, refine behavior
- Add or update content sources
- Create new procedures for complex workflows
- Refine guidance for tone and policy
- Connect additional data sources
2. Test - Validate changes safely before customer impact
- Run simulations on real scenarios
- Preview responses across channels
- Test different customer segments
3. Deploy - Roll out updates strategically
- Stage changes to subsets of traffic
- Monitor performance metrics in real-time
4. Analyze - Measure performance and identify opportunities
- Review resolution rates by topic
- Analyze CX Scores for patterns
- Identify knowledge gaps and prioritize improvements