OpenAI +

Everyone is building AI Agents, and everyone is looking for ways to build them more easily. Earlier this year, OpenAI released the OpenAI Agents SDK to bring the patterns they have found to work for building agents to the developer community. With the SDK you can define AI agents by supplying them instructions (prompts), specifying which model to use (OpenAI or not), listing tools it uses (including MCP), and much more. The OpenAI Agents SDK encourages a paradigm of orchestrated micro-agents, which themselves may have micro-orchestrations within them with the use of handoffs. It’s an elegant and powerful model.

But a good AI Agents programming model is not enough. These agents are ultimately wildly distributed systems and are plagued with all of the problems such systems bring.

How can they persevere through flakey networks?
How can they function when LLMs are rate limited?
How can they run for long periods of time (hours, days, weeks, months) when infrastructure is rarely stable that long?

In this workshop, we’ll show you how. Temporal is an open source (MIT license) durable execution framework that brings resilience to AI agents, and in this workshop we’ll show you how it’s done with the OpenAI Agents SDK. Spoiler: OpenAI and Temporal have done all of the heaving lifting for you with an integration announced earlier this year.

Oh, and OpenAI themselves use Temporal to help make several of their products production ready (image gen and Codex, for example).

Not using the OpenAI Agents SDK? Do come anyway; the foundational concepts carry over to different agent frameworks (and more integrations are coming all the time).

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