Andrew Gordieiev

A demo is a shape. Production is a contract. Most agent failures aren't model failures. They're the missing tests, the unhandled edge cases, the rollback path nobody wrote, the step that quietly gets skipped. So I treat an agent system like any other distributed system: typed contracts, validated boundaries, real error handling, observability at every step. The question that matters isn't whether the model produced something plausible. It's whether the system stays correct when the model doesn't.

When you bring AI into a big engineering org, the model and the tooling are rarely the bottleneck. Adoption is. Engineers give up on a new flow fast unless they feel it save them time early, and managers need numbers that cut through the hype. So I roll it out in small, low-risk steps that each feed a shared foundation (policies, references, eval setups), and every next project lands faster than the last.

Let agents run free on anything reversible. Put a human gate on anything that isn't. Every irreversible action goes through a review step (a diff, a reason, a way to undo it) that's built into the architecture, not bolted on as a UI afterthought. That part is non-negotiable for code, for infrastructure, for anything that touches customer data.

Reaching for the newest framework before the team understands the patterns underneath just builds something fragile. I get people fluent in the primitives first (message protocols, tool use, eval design, prompting as an actual skill) before we lock into any vendor stack. Vendors change. The patterns don't.