Digital Event Horizon
Unlock the full potential of your intelligent agents with our comprehensive guide to tracing and evaluating. Discover how to refine and optimize your agent's performance using Arize Phoenix and LLM-as-a-judge.
Tracing and evaluation are crucial tools for refining and optimizing agent performance. Arize Phoenix is a centralized platform for tracing, evaluating, and debugging agent decisions in real-time. Tracing allows developers to track each step an agent takes, providing a clear understanding of how the agent operates. Evaluation helps determine how well an agent retrieves, processes, and presents information using LLM-as-a-judge.
In the rapidly evolving landscape of artificial intelligence, building an intelligent agent that can perform complex tasks is a significant milestone. However, this achievement is only the first step; understanding how effectively the agent performs is equally crucial. This is where tracing and evaluation come into play – essential tools for refining and optimizing agent performance.
The context data provided outlines the use of Arize Phoenix as a centralized platform for tracing, evaluating, and debugging agent decisions in real-time. By leveraging this tool, developers can gain valuable insights into their agent's internal workflow, enabling them to identify areas of improvement, optimize performance, and ensure that the agent behaves as expected.
Moreover, the article highlights the importance of making sense of an agent's internal workflow, which is where tracing comes into play. Tracing allows developers to track each step the agent takes – from invoking tools to processing inputs and generating responses – providing a clear understanding of how the agent operates.
To enable tracing, Arize Phoenix can be used in conjunction with OpenTelemetry and OpenInference. This enables the automatic capture of agent calls as traces, which are then sent to the Phoenix instance for analysis. The article demonstrates this setup by registering a tracer provider and instrumenting smolagents using the SmolagentsInstrumentor.
Furthermore, evaluation is an essential component of agent development. Evaluations help determine how well an agent retrieves, processes, and presents information. In this context, the article focuses on evaluating the DuckDuckGo search tool used by the agent, measuring the relevance of its search results using a Large Language Model (LLM) as a judge – specifically, OpenAI's GPT-4o.
By leveraging LLM-as-a-judge, developers can classify and score responses, providing valuable insights into the effectiveness of their agent. The article outlines the steps involved in this process, including installing OpenAI, retrieving tool execution spans, importing the RAG Relevancy Prompt Template, and running the evaluation.
In conclusion, tracing and evaluation are critical components of building intelligent agents that perform complex tasks effectively. By leveraging tools like Arize Phoenix and LLM-as-a-judge, developers can gain valuable insights into their agent's internal workflow and performance, enabling them to refine and optimize their creations for optimal results.
Related Information:
https://www.digitaleventhorizon.com/articles/Unlocking-the-Secrets-of-Intelligent-Agents-A-Comprehensive-Guide-to-Tracing-and-Evaluating-deh.shtml
https://huggingface.co/blog/smolagents-phoenix
Published: Fri Feb 28 11:46:02 2025 by llama3.2 3B Q4_K_M
