Beyond the Model: A Practitioner's Guide to Harness Engineering

Shipping an AI agent to production is a different engineering problem than building one.

Most teams discover this the hard way. The prototype works. The demo impresses and then it hits real users, real failures, and real complexity. The infrastructure that was never designed for production starts showing its cracks.

The engineers who are getting this right aren't just choosing better models or frameworks. They're treating the harness as a first-class engineering problem and are thinking through the execution layer, the context, tool orchestration, observability, and the durability that keeps long-running tasks running when things go wrong.

Get the harness right and the model can do its job. Get it wrong and no model will save you.

In this session, Temporal and Grid Dynamics bring together infrastructure expertise and hands-on delivery experience for a conversation about what production-grade agent harnesses actually look like, where teams get stuck, and what it takes to build one without starting from scratch.

Key takeaways
- A clear diagnosis of why agent projects stall between prototype and production, and what the failure patterns actually have in common
- A practical breakdown of what a production harness is made of and where teams consistently underinvest
- A live demo showing durable execution in action, including what happens when things go wrong mid-run
- Honest perspective from practitioners who have built and implemented agent harnesses at scale
- Concrete takeaways you can bring back to your team right away

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