Agentic AI in production: what actually works in 2026
The gap between an agent demo and an agent in production is enormous. After a year of shipping agentic features, these are the patterns that actually hold up.
Field notes from practitioners — how teams ship AI products, what AIOps looks like in production, and the career playbooks that actually work in 2026.
The gap between an agent demo and an agent in production is enormous. After a year of shipping agentic features, these are the patterns that actually hold up.
The interesting question in 2026 isn't 'which model is smartest?' — it's 'how little model can I get away with?' For most tasks, the answer is surprisingly little.
EngineeringFor years, 'make it smarter' meant 'train a bigger model.' Now there's a second dial: let the model think longer at inference. It changes the economics of hard problems.
AI PMEvery few months someone declares RAG dead because context windows got bigger. They're missing the point. The work just moved up a level.
AI PMIf you can't tell me how you'll know the model is doing its job, you can't ship it. Evals are how AI teams move fast without breaking things.
EngineeringEvery AI product decision is also a cost decision. The teams that treat inference spend as a design constraint from day one are the ones with a business at scale.
EngineeringThe honest answer is 'it depends' — but it depends on a small number of things you can actually reason about. Here's the framework we use.
EngineeringThe loudest AI story is the biggest models. The quietest — and maybe more consequential for products — is that good models now fit on the device in your pocket.
CareerIf you're studying AI right now and wondering what's actually worth learning, this is the honest map — the skills hiring teams test for, in the order that compounds.
AI PMYou don't need to become a lawyer. But if you ship AI in 2026, 'we'll deal with compliance later' is no longer a strategy. Here's the builder's version.