The common failure in agent pilots is not the first answer. It is work that keeps moving after nobody is watching.
OpenAI’s Agents API is useful because it treats long-running work as a session, not a single prompt. That means an agent can keep context across steps, call tools, pause for approval, and resume after a human review. It also adds tracing, so the run is easier to inspect after the fact.
That combination changes how you design the workflow. If an agent is reconciling records, drafting a vendor response, or preparing a case summary, the goal is not to stop and approve every action. The goal is to place a checkpoint where the work becomes hard to undo or easy to misroute.
A simple rule helps: if the next step can create a mistake that would be expensive to detect later, do not let the agent pass through silently. Log the step, surface the draft state, and require approval before the risky handoff. Let routine preparation continue automatically.
That is a better pattern than wrapping a brittle script around a model call. Durable sessions and human-in-the-loop pauses make agent workflows easier to support, because the process is visible where judgment actually changes the outcome.