Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
Data Science
Towards Data Science·Aug 6·5 min read
I Built a Tool-Calling Agent in Python. Here’s How I Debugged It
TL;DR
A Python-based tool-calling agent was developed, focusing on real API interactions and debugging techniques. The key outcome was a streamlined process for validating outputs and tracing errors before implementing a more complex agent framework.
✦ Why It Matters
Start implementing systematic debugging techniques in your projects to enhance reliability and reduce errors.
Key Takeaways
- Define tools with JSON Schema for validation.
- Capture detailed logs of each tool interaction.
- Inspect the entire message flow for better debugging.
- Use the provided script to implement a reliable agent.
How It Works
The agent uses a loop driven by a large language model (LLM) to request and execute functions, validating inputs against predefined schemas. Each interaction is logged, allowing developers to trace the flow of data and identify issues effectively.