Question

You are building a customer-support agent on Databricks using LangChain. The agent must answer product-availability questions by querying a live inventory system exposed as a Python function, and it must also handle general product FAQs from a knowledge base. You want the LLM to decide autonomously when to call the inventory function versus when to answer directly. Which approach correctly enables the agent to invoke the inventory function only when needed?

Accepted Answer

Wrap the inventory function as a LangChain tool with a clear name and description, bind the tools to a function-calling-capable chat model, and let the agent's reasoning loop decide when to call it.

Answer

Concatenate the full inventory table into the system prompt so the model can read stock levels directly without any tool calls.

Answer

Create a fixed LCEL chain that always calls the inventory function first, then passes the result to the LLM for every user query.

Answer

Fine-tune the foundation model on inventory snapshots so it memorizes current stock levels and can respond without external calls.