A retailer's Foundry-hosted support agent answers customer questions about product availability. Inventory levels change by the minute, and the model's training data has no knowledge of current stock, so a plain prompt-engineered response would either guess or refuse to answer. The team wants the agent to retrieve the live stock count from the retailer's existing inventory API before it replies. Which Foundry capability should they configure to accomplish this?
Select an answer to reveal the explanation.
Short Explanation
You need a live lookup, not a guess. Define a function or tool that calls the inventory API, so the agent pulls the real stock count before answering. That's how you get current data instead of stale model knowledge.
Full Explanation
The capability to configure is defining a function or tool the agent can call, connecting it to the inventory API so it retrieves the live count at answer time. The agent needs a fact that changes constantly and cannot be baked into the model at training or fine-tuning time, so it must reach out to a live data source at the moment of the question. Foundry supports defining functions or tools that an agent can invoke mid-conversation; connecting one to the retailer's inventory API lets the agent fetch the actual current count and ground its answer in real data rather than a prediction. Increasing the model's temperature setting so it produces more varied guesses about likely stock levels is incorrect because raising temperature increases response variety and creativity, which makes an already-uninformed guess less consistent, not more accurate. Fine-tuning the model on last year's sales history so it can estimate current stock from historical patterns is incorrect because fine-tuning on historical sales data teaches the model patterns from the past, but stock levels an hour from now are not derivable from last year's trends, and fine-tuning does not create a live data connection. Adding more example question-and-answer pairs to the system message describing typical stock levels is incorrect because adding illustrative examples to the system message only shapes response style and format, it does not give the model access to information it was never trained on and cannot retrieve on its own. Tool calling is the mechanism designed specifically for pulling in real-time external data.