Langchain Agent with Structured Tools Schema & Langfuse using AWS Bedrock Nova 🤖

AI agents are becoming the brains of modern apps, they make decisions, use tools, and give smart responses in real time. But to build them properly at scale, you need a solid agent framework (e.g. LangChain/LangGraph, AWS Strands Agents, etc.). In this post we’ll look at how LangChain agents use structured tool calling to do that. Also, we'll configure Langfuse to follow toolcalls, flow, input/output. What is Tool Calling? Tool calling is when an AI agent invokes external functions or APIs to perform actions or retrieve data. The agent decides to call a tool based on the user’s request and the available tool descriptions. It generates a structured request (following a schema) with the required parameters for that tool. Why Structured Tool Calling Important? Tool calling with a structured schema ensures that inputs and outputs are predictable, validated, and machine-readable, reducing ambiguity, hallucination and runtime errors. It also enables reliable automation and easier integration