Reinforcement Learning
Machine learning technique where AI learns optimal behavior through trial and error, receiving feedback on actions. In customer service, enables AI to improve resolution strategies based on outcomes and human feedback.
Learning Through Feedback
Reinforcement learning (RL) teaches AI through reward signals rather than explicit programming. The system tries different approaches, receives feedback on results, and learns which strategies work best. Over time, it optimizes behavior to maximize positive outcomes—in customer service, this means higher resolution rates, better satisfaction, and more efficient interactions.
This mirrors how humans learn: we try actions, see results, and adjust behavior. When an AI Agent discovers that asking clarifying questions before suggesting solutions improves resolution rates, RL helps it learn to apply this pattern consistently. The system becomes smarter through experience, not just data.
RL in Customer Service AI
Customer service provides rich feedback signals for reinforcement learning:
- Resolution success: Whether issues were actually solved
- Customer satisfaction: CSAT and sentiment scores
- Human feedback: Agent corrections and quality reviews
- Efficiency metrics: Handle time and first-contact resolution
Continuous Improvement
Reinforcement learning enables AI systems that improve automatically over time. As they handle more interactions and receive more feedback, they refine their strategies for routing queries, phrasing responses, and selecting resolution paths. This continuous learning means AI performance strengthens with use, delivering compounding returns. Modern implementations like Fin use RL alongside human oversight to ensure improvements align with business goals and quality standards.