How Architecture as Code Became Context Engineering [ukr]

A few years ago, we started building Architecture as Code with a fairly simple goal: make architectural knowledge accessible, up to date, and understandable for engineers. C4 diagrams, ADRs, OpenAPI specifications, ERDs, ownership, and other documentation gradually moved closer to the codebase, becoming version-controlled and machine-readable.
We built it for humans.
Then AI agents arrived.
And unexpectedly, we discovered that one of the hardest parts of working with AI — transferring enough system context to an agent — had already been largely solved.

In this talk, we’ll explore why repository access alone is not enough, how code context differs from system context, and how Architecture as Code naturally evolves into Context Engineering.

Using practical examples, we’ll look at the context an AI agent needs to do more than just write code: understanding domains, architectural decisions, API contracts, ownership, and system constraints. We’ll also discuss the next challenge: ensuring that the context AI relies on actually reflects the real state of the system.