Question

Your team deploys a customer-support agent in Azure AI Foundry that answers questions using an Azure AI Search index built from a product knowledge base. Users report that the agent frequently returns answers based on outdated product documentation, even though the underlying source documents in Blob Storage were updated two weeks ago. The index was configured with an indexer that ran once during initial setup. What is the most appropriate action to ensure the agent grounds its answers on current content while minimizing manual effort?

Accepted Answer

Configure a schedule on the Azure AI Search indexer so it periodically re-crawls the Blob Storage data source and refreshes the index.

Answer

Increase the agent model's temperature so it generates more up-to-date responses from its training data.

Answer

Switch the agent's retrieval method from vector search to keyword-only search to improve accuracy.

Answer

Recreate the entire index manually each time a document changes and redeploy the agent.