Streamlining Agent Evaluation: A Comprehensive Approach to Improving Application Performance

As one of the founders of Arise, I'm excited to share our approach to agent evaluation, from evaluating tool calls to tracing conversations and identifying bottlenecks in our co-pilot agent's performance.

1. Aperna is a founder of Arise, which builds development tools for teams to build and take agents to production.
2. Arise focuses on agent evaluation, observability, monitoring, and tracing.
3. Building agents is difficult due to constant iteration at various levels (prompt, model, tool call definitions).
4. Evaluating agents systematically can be challenging, making it hard to track the performance of new prompts or models.
5. Including product managers and other team members in the iterative evaluation process can improve applications but is not always easy.
6. Identifying bottlenecks in deployed agents and addressing them is another challenge.
7. Arise looks at agent evaluations from different components, including tool call level, trajectory, trace, multi-turn conversations, and self-improvement.
8. Tool calling evaluation is crucial as it ensures the right tools are called with the correct arguments based on context.
9. Arise's product offers an insights tool for teams to analyze their application's performance and suggest improvements.
10. Analyzing different trajectories or paths that an agent can take helps identify bottlenecks and areas requiring improvement.
11. High-level views of an agent's architecture allow for a quick understanding of its capabilities and potential issues.
12. Arise's co-pilot is evaluated using their own product to identify strengths and weaknesses.
13. Evaluating Q&A correctness, specifically search Q&A correctness, can help pinpoint areas needing improvement.
14. Drilling down into specific traces allows for a more detailed analysis of issues, such as incorrect arguments passed to tool calls.
15. Ensuring the correct order of tools called is important for an agent to efficiently complete tasks.
16. Trajectory evaluation helps identify inconsistencies in tool call order and potential issues caused by them.
17. Multi-turn interactions require additional evaluation, such as checking consistency in tone and ensuring context is maintained.
18. Session evaluations are essential for analyzing multi-turn conversations with agents.
19. Evaluating the evaluation (eval) prompts used to identify failure cases is important for improving the existing prompt and annotating outputs.
20. Improving eval prompts alongside agent evaluations and prompts creates a better product experience.
21. Arise Phoenix is an open-source product for learning about and testing agent evaluation techniques.
22. Riseex assists in running evaluations on your own data.
23. Iterating on both agent evaluations and eval prompts helps create a comprehensive, high-quality product experience.
24. Continuously refining the golden dataset for eval prompts is crucial for accurate evaluations.

Source: AI Engineer via YouTube

❓ What do you think? What are your thoughts on the ideas shared in this video? Feel free to share your thoughts in the comments!