How to Build, Trace and Evaluate AI Agents: A Python Guide with Smolagents and Phoenix

Learn step-by-step how to develop AI agents in Python with Smolagents, implement observability using OpenTelemetry & Phoenix, and perform robust LLM Agent evaluations.

Why Evaluate LLM Agents?

The frontier of Artificial Intelligence is rapidly moving beyond simple text generation. We are now building sophisticated LLM agents capable of reasoning, planning and interacting with external tools like databases, APIs or search engines to accomplish complex tasks. Frameworks like Smolagents make developing these powerful Python agents more accessible than ever.

However this power comes with significant complexity. When your agent can autonomously decide to call a function, retrieve information or query a search engine how do you really know if it’s making the right decisions? Is it selecting the optimal tool for the task? Is the information it retrieves actually relevant and helpful? When things go wrong how do you pinpoint the failure within the agent’s multi-step execution flow?

Traditional LLM benchmarks often fall short when assessing the nuanced multi-step behavior of these autonomous systems. We…