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.
What you can expect:
Can't make the live session? Register anyway and we'll send the recording to your inbox.
Principal Developer Advocate
Temporal Technologies
Cornelia has spent a career at the forefront of technological innovation, starting with image processing algorithm development, moving to web-centric computing in the late 1990s, and then more than a decade working in cloud-native software and DevOps platforms. As Senior Staff Developer Advocate for Temporal, she is now helping to drive the expansion of the “durable execution” distributed systems paradigm.
She is the author of Cloud Native Patterns: Designing change tolerant software.
AI Architect, AI Advisor (CTO Org), Distinguished Engineer
Grid Dynamics
Naresh has over 12 years of development experience in Deep Learning. He has worked with different types of solutions in the Deep Learning space from the days prior to Tensorflow and Pytorch where he used to build Deep Learning models on top of Theano/Caffe as frameworks. He is currently focused on nimble, agile solutions to accelerate value recognition of Generative AI solutions as part of the CTO Organisation at Grid Dynamics. He has a Bachelor's in Electrical Engineering, BITS-Hyderabad, and a Master's in Business Analytics and Big Data, IE Business School. He has previously worked with different companies across fields like P&G, GE Healthcare, and his own startup, Untangle AI in the Explainable AI Space.
