Debugging tool-calling agents in Python requires more than just the final answer; it necessitates inspecting the entire execution flow. This article details building a minimal agent that calls real APIs, validates arguments using JSON Schema, and logs each step for review.

The agent demonstrates a loop where the model requests tools, Python executes them, and results are validated and returned. This process is crucial for understanding failures, whether they stem from malformed arguments, API errors, or model request issues. The tutorial uses public APIs for geocoding and weather data, requiring only an OpenAI key.

The core takeaway is to evaluate an agent not solely on its output, but on the entire process: model request, schema validation, Python execution, compact tool results, error handling, and the final answer. Building this inspectable loop directly before adopting frameworks like Weights & Biases (W&B) Weave or MCP provides a foundational understanding of message flow and agent behavior.