Keeping the run when a worker fails
Temporal supplies durable execution: application work runs as workflows whose progress is recorded so execution can recover after interruption. In an agent application, the important state includes model results and completed operations, not just a final answer. Temporal's OpenAI Agents SDK integration runs model calls as Activities, the units of external work tracked by its workflow engine. [1]
That changes the lifecycle of a task. A process can restart and use recorded progress instead of treating the entire agent run as lost. External tool operations need appropriate Activity-backed execution; the documentation distinguishes those from deterministic functions running inside a workflow. Recovery depends on those application boundaries, rather than automatically making every side effect safe to repeat. [1]
Durable execution for agent workflows
On July 30, 2025, Temporal and OpenAI introduced a public preview connecting the Agents SDK to Temporal's execution model. The announcement described recovering agent work after a crash and retrying downstream requests when capacity or connectivity returned. This let a long-running agent task recover recorded progress after a worker interruption. [2]
The service boundary remains important: continuing execution is different from proving that an agent's decisions are correct. The original release was a Python integration in public preview, as its dated changelog confirms. A later note on the launch article records general availability on March 23, 2026, which is a separate milestone. [2][3]
Sources
- OpenAI Agents SDK integration - Python SDK
Temporal · retrieved Oct 23, 2026
- Production-ready agents with the OpenAI Agents SDK + Temporal
Temporal · Jul 30, 2025
- Announcement
Temporal · retrieved Oct 23, 2026