THE INFERENCE HUB x AWS - Are You Wasting Your Time With Harness Engineering?

GPU or CPU: Are You Wasting Your Time On Harness Engineering?

As AI models turn into full-blown operating systems (Software 3.0 territory), a real worry has been forming: are explicit prompts, guardrails, and agent loops just patches for gaps in what the base model already knows? Organizations keep pouring effort into "harnesses" that run mostly on CPU to shape what agentic systems spit out. That work matters, sure. But how much of it is necessary, and how much disappears once models get trained to handle these problems on their own?

Fine-tuning is a different story. It needs specialized techniques and hardware, and it's nowhere near as intuitive as writing a prompt. You need real expertise on data modeling and model training before you can do it properly.

This talk covers where harness engineering is genuinely worth the effort, and where you're probably reaching for the wrong tool. We'll show cases where fine-tuning beats months of prompt engineering and guardrail-building outright, then get into the messier gray areas: product requirements, unit economics, and how those factors end up deciding the question.

Hosted by The Inference Hub, AWS, and TensorOps.

An AWS Cloud Architect opens up the event with a session on using AWS for both harnesses and tuning.

Gad Benram, CTO of TensorOps, follows with a look at harness engineering itself: what it actually is, and what you can build with it.

Then Shuki Cohen, VP of Applied AI at AI21, digs into the foundations of post-training, before a live panel Q&A and an audience vote settle the debate.

August 4, 2026, 6:00 PM, at AWS Floor 28 in Israel.

Registration is required for building access.