Question

You are building a Mosaic AI agent on Databricks that answers customer questions by calling multiple tools (a product catalog lookup, an inventory checker, and a shipping estimator). During testing, some queries cause the agent to enter long reasoning loops, repeatedly invoking tools without converging on a final answer. This drives up latency and token costs. Which configuration change most directly prevents these runaway loops while still allowing legitimate multi-step reasoning?

Accepted Answer

Set a max_iterations (maximum reasoning steps) limit on the agent executor so it terminates and returns after a bounded number of tool-calling cycles.

Answer

Switch the underlying foundation model from a pay-per-token endpoint to a provisioned throughput endpoint.

Answer

Increase the model's max_tokens output parameter so it can produce a complete answer in a single response.

Answer

Reduce the temperature parameter to 0 so the model produces fully deterministic tool calls.