AI coding agents can easily amplify loose engineering standards into chaos, unless context is engineered as infrastructure. Research shows 8x code duplication spikes and 154% larger PRs when you don't implement guardrails steering AI across the SDLC.

This talk explains the benefits of using "centralized context planes" as portable infra: machine-readable artifacts (architecture diagrams, agent coding rules, knowledge systems) distributed to your CLI/IDE/Git for AI agents. Build engines that systematize software quality as multi-dimensional gates, embedded where developers already work.

Based off of a code governance white paper I published, we'll codify code quality "dimensions" into constraints AI agents can lean on. For distributed teams, this will make context consistent, visible, and maintainable. Ultimately, it will enable your agentic workflows while preserving software craftsmanship.

You'll get a checklist for context artifacts, checkpoints of where to embed that context in your workflow, and patterns for verification at scale.