Microsoft Azure AI Engineer Associate AI-102 Practice Question
You are designing a conversational solution in Azure AI Studio. The requirement is to let users ask for a travel itinerary, after which the solution must (1) obtain flights, (2) book a hotel, and (3) create a calendar event-calling three different REST-based internal APIs in the correct sequence without any further user prompts. Why is creating a custom agent the most appropriate approach?
A single chat-completion request with OpenAI function calling can perform all three API calls in one response, so adding an agent adds unnecessary overhead.
Logic Apps Standard workflows already include AI model steps and native HTTP actions, so an agent is only needed when you have non-HTTP services.
Agents provide built-in planning and tool orchestration, enabling autonomous calls to multiple APIs in sequence for a single user goal.
Durable Functions would be preferable because agents cannot reliably persist conversational state between tool calls.
A custom agent can hold the conversation thread, break the user's goal into sub-steps, and invoke the registered API tools in the right order until the itinerary is finished. If you relied only on a chat-completion endpoint with function calling, you would have to write your own control loop to examine each function-call response, maintain state, and decide when to call the next API. Other Azure orchestration options (Durable Functions or Logic Apps) can manage workflows but cannot reason over natural-language goals, so they still need an agent in front of them.
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What does an Azure custom agent do in conversations?
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Why not use Durable Functions instead of a custom agent?
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How do OpenAI function calls differ from agents in Azure?
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Microsoft Azure AI Engineer Associate AI-102
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