Your AI assistant just wrote 500 lines of flawless code. Unfortunately, it had no idea it just broke three services, invalidated two SDKs, failed a deployment pipeline, confused your API consumers, and made your on-call engineer question every life decision that led them here.

The problem isn't that AI can't write code. The problem is that most AI agents are brilliant interns on their first day: fast, enthusiastic, and completely unaware of how your systems actually work.

Modern software is a giant web of dependencies. APIs connect to services. Services run on infrastructure. Infrastructure feeds telemetry. Gateways enforce security. SDKs and documentation power developers. Tests prove everything still works. Change one thing, and suddenly everything has an opinion.

This session explores what happens when we give AI agents the missing ingredient: context. We'll dive into the architecture behind truly context-aware engineering systems, including how API knowledge graphs, catalogs, source control, Kubernetes environments, observability platforms, and runtime metadata create a living map of an organization's technology ecosystem.

From there, we'll explore how AI agents use that context to move beyond autocomplete and become actual engineering collaborators. Agents can understand the blast radius of a change, coordinate updates across repositories, generate SDKs and documentation, execute browser-based validation, trigger automated workflows, securely access enterprise systems, communicate through AI-native gateways, and run production-grade agent workloads.

Because the future of AI engineering isn't an AI that writes more code. It's an AI that finally understands what that code is connected to.