A useful AI system is often judged by the first response, but the harder problem is everything after it.
A draft gets produced, a person reviews it, a record needs approval, or the workflow pauses because another system is unavailable. If the context disappears at each pause, the software becomes brittle. OpenAI’s newer Agents API is built around durable sessions: the work can continue across multiple steps, and if there is a disconnect, the session and saved items can be retrieved so the run can be resumed.
That shifts the design question from “Can the model answer?” to “Can the process survive interruption?” In practice, that matters for any work where human review is part of the path, not a rare exception. For example, an agent can prepare a case summary, wait for approval, then continue without rebuilding the whole context from scratch.
The implementation choice is less about adding AI to a task and more about deciding where the run should pause, who can review it, and how recovery should work if the handoff fails. That is the difference between a clever demo and software people can depend on.