Question

You are building a customer support agent with the Azure AI Foundry Agent Service. Each customer has an ongoing support case that may span several separate sign-in sessions over multiple days. Within a single session, the agent must remember earlier turns, and when a customer returns days later you must resume the exact same case context without re-uploading history. You also need to inspect the individual tool calls and model steps that occurred during one specific request for debugging. Which combination of Agent Service constructs should you use to model this?

Accepted Answer

Persist one thread per customer case for cross-session continuity, add messages to that thread each session, and inspect a specific run's steps to debug the tool and model activity of a single request.

Answer

Create a new thread for every sign-in session and store the full transcript in the agent's system instructions so it is reloaded each time the agent is invoked.

Answer

Use a single run that stays open for the entire multi-day case and read run-level metadata to recover per-session conversation history.

Answer

Store all conversation history in a vector store attached via file search, creating a fresh thread and run for each message to keep state stateless.