Question

You are building an Azure AI Foundry agent for a travel-booking assistant. The agent has three registered tools: get_user_preferences (reads a profile store), search_flights (queries an airline API using the retrieved preferences), and check_weather (queries a public weather API for the destination). During a single turn the agent must produce a recommendation that uses flight results filtered by user preferences, while weather information is independent. You want to minimize end-to-end latency without producing incorrect results. How should you design the tool-calling behavior for this turn?

Accepted Answer

Invoke get_user_preferences first, then invoke search_flights and check_weather in parallel once preferences are available.

Answer

Invoke all three tools in parallel immediately, then discard any results that depend on data that arrived late.

Answer

Invoke all three tools strictly sequentially in registration order to guarantee deterministic output.

Answer

Invoke check_weather first because it has no dependencies, then run get_user_preferences and search_flights in parallel.