AI is a force multiplier that turns weak standards into architectural chaos. As code review becomes the ultimate bottleneck, engineering teams must bridge the gap between human intuition and machine output. This talk introduces a holistic framework for designing systems around AI coding. We explore how to codify architectural intent, from module boundaries to failure awareness, into machine-readable guardrails. Learn how to leverage context engineering to ensure your AI code tools respect your system’s design, preserving long-term maintainability without sacrificing the speed of the AI era.

1. Attendees will learn how to apply design, maintainability, quality, and chaos prevention to build a holistic verification layer for AI-generated output.
2. Learn how to build infrastructure that automatically feeds architecture diagrams, API contracts, and memory layers into AI agents to ensure alignment with senior intuition
3. Gain a practical methodology for encoding architectural reasoning into durable systems, allowing senior developers to shift from manual "line-coders" to "Policy Guardians" of the codebase.