AI Engineer Summit 2026

How to build a unit-economics model for an AI product before the invoice teaches you: per-request cost decomposition, the caching wins that matter, routing between model tiers, and how to talk about all of it with a finance team.

Ten minutes on getting reliable JSON out of a language model: schema design that models can actually follow, constrained decoding, repair strategies, and when to stop fighting and use two calls.

<p>A talk created by the verify script.</p>

A look at what changed in the last twelve months — from demos to systems, from prompts to products — and what the discipline needs to figure out next.

Agents fail in ways that traditional services do not: partially, plausibly, and expensively. This talk is the playbook we wish we had — bounded tool surfaces, idempotent actions, replayable traces, checkpointing, and the escalation paths that keep a human in the loop without keeping them in the way.

A working tour of a real evaluation harness: dataset curation, judge prompts you can defend, statistical significance on small samples, and wiring the whole thing into CI so a regression blocks a deploy rather than surprising a customer.

Multi-tenant inference from the operator's seat: scheduling, memory packing, cold-start mitigation, noisy-neighbour isolation, and the observability you need before you can safely oversubscribe anything.