How to Evaluate Agents Galileo’s Agentic Evaluations in Action
AI Summary
- Introduction to AI Agents
- Definition: AI agents vary from fully autonomous systems to predefined workflows.
- Key distinction: Workflows have predetermined paths; agents have non-deterministic paths decided by LLMs.
- Challenges in Evaluating AI Agents
- Complexity: Non-deterministic nature complicates evaluation.
- Key challenges: Understanding paths taken, evaluating correctness, and managing cost vs. performance trade-offs.
- Galileo’s Evaluation Platform
- Provides end-to-end visibility into agent performance.
- Offers metrics to measure success and identify failure points.
- New Agent-Specific Metrics
- Tool Selection Quality: Checks if the correct tools and arguments were used.
- Tool Errors: Detects if any individual tool failed.
- Action Advancement: Measures if the user progressed towards their goals.
- Action Completion: Assesses if the user achieved their goal.
- Demonstration of Metrics in Action:
- Evaluated a chatbot for a food ordering application.
- Used various queries to run systematic evaluations and log results.
- Observed failures in order placement due to unavailability, lacking cancellation tools, and incorrect argument usage.
- Conclusion
- Emphasized importance of metrics for improving agent performance and readiness for production.