Implementing Agent Observability with Arize Phoenix

Understanding the internal decision-making process of AI agents and measuring their performance is a critical step in development. Arize Phoenix provides a centralized platform for tracing and evaluating agent behavior in real time.

Key implementation steps:

• Building the agent: Use the smolagents library to create a CodeAgent that calls tools and performs tasks.
• Enabling Tracing: Use OpenTelemetry and OpenInference to visualize the agent's tool calls, input processing, and response generation. Data can be sent to a Phoenix instance via the smolagents[telemetry] module.
• Evaluation: Measure the agent's response relevance, factual accuracy, and answer quality to optimize performance.

This guide presents a workflow that goes beyond simply having the agent work, allowing developers to transparently understand what's happening internally and continuously improve it.