Question

You are building an Azure AI Foundry agent for a call-center analytics team. Agents must accept uploaded recorded customer support calls (audio files), produce accurate transcripts, then reason over the transcript to summarize the customer's issue and detect sentiment. The team wants the least custom orchestration code and prefers using a Foundry-managed capability to handle the speech-to-text step before passing text to the LLM. Which approach should you implement?

Accepted Answer

Register a custom function tool that calls Azure AI Speech to transcribe the audio, then let the agent's chat model summarize and analyze the returned transcript.

Answer

Attach the audio files to the agent's file search vector store so the model can retrieve and transcribe them at query time.

Answer

Send the raw audio bytes directly in the chat completion prompt to a text-only GPT deployment and instruct it to transcribe and summarize.

Answer

Use the code interpreter tool to write Python that plays the audio and manually types out the transcript for the model.