Question

You are building a Mosaic AI agent that calls a Unity Catalog function to look up a customer's order by an order ID. During testing, the LLM occasionally passes malformed arguments — sometimes an empty string, sometimes a full sentence like 'the order from yesterday' — causing the tool to fail. You want to reduce these failures by making the tool's expected input explicit to the LLM so it produces correctly formatted arguments more reliably. Which approach most directly addresses this?

Accepted Answer

Define a strict argument schema (e.g., a Pydantic model or typed function signature with descriptions) for the tool so the LLM receives a clear structured specification of the required input format.

Answer

Increase the model temperature so the agent explores a wider variety of argument formats until one succeeds.

Answer

Wrap the tool in a retry loop that re-invokes it with the same arguments until the call returns successfully.

Answer

Raise the agent's maximum iteration limit so it has more attempts to eventually call the tool correctly.