OpenAI + @Temporalio : Building Durable, Production Ready Agents - Cornelia Davis, Temporal

Agentic loops drive autonomous behavior

The SDK enables LLMs to control application flow through runner.run loops that continuously invoke the LLM, execute tools, and route outputs until the task completes.

Simple configuration with powerful defaults

Agents require only a name and instructions to start, but support advanced features like handoffs, guardrails, and tool integration in both Python and TypeScript.

Durable execution as a backing service

Temporal provides distributed systems durability as a service, allowing developers to program only the 'happy path' while automatically handling crashes, retries, and state recovery.

Workflows and activities architecture

Activities wrap external calls or heavy computation, while workflows orchestrate them with built-in retries, exponential backoff, and event-sourced state management.

Proven at massive scale

Every Snapchat, Airbnb booking, and OpenAI CodeX/image generation runs on Temporal, which originally forked from Uber's Cadence workflow engine.

Token-preserving crash recovery

When Temporal powers agents, applications resume exactly where they left off after crashes without re-executing previous LLM calls, preventing token reburn even on the 1,350th turn.

Automatic resilience for AI workflows

The integration transparently handles rate limiting, downstream API failures, and infrastructure crashes, eliminating manual retry logic and queue management like Kafka.

Native streaming not yet available

Temporal currently does not natively support streaming data for agents, though workarounds exist at scale and native support is a top priority.

Large payload storage in development

The team is actively building large payload storage to efficiently handle big LLM context windows by passing data by reference rather than value.