Ensure AI Agents Work Evaluation Frameworks for Scaling Success — Aparna Dhinkaran, CEO Arize
AI Summary
Summary of AI Agents and Evaluation
Introduction
- Speaker discusses the importance of evaluating AI agents, especially in production settings.
Overview of AI Agents
- AI agents have evolved from text-based to multimodal and voice AI implementations.
- Example: PriceLine’s PennyBot for booking vacations hands-free.
Components of an AI Agent
- Router: Decides the next step for the agent by routing user queries to the appropriate skill.
- Skills: Logical chains that perform the specific tasks requested by users.
- Memory: Stores previous interactions for coherent multi-turn conversations.
Evaluating AI Agents
- Router Evaluation: Ensures the router correctly calls the right skill based on user input.
- Skill Evaluation: Focuses on the relevance and correctness of the responses generated by skills.
- Convergence Evaluation: Measures the efficiency of the paths taken by agents in processing requests.
Voice Applications
- Voice applications require additional evaluation metrics like audio quality, sentiment analysis, and speech-to-text accuracy.
Conclusion
- Evaluations should be integrated throughout the agent’s workflow to pinpoint where issues arise, whether in router decisions or skill execution.