Engineering in the agentic era.

Design Continuity is a formula — Rails plus Change Control plus Conformance — architectural intent that is known, measured, and audited automatically at the full pace of change.

The silent, incremental divergence between agreed architectural intent and what actually ships — now accelerating as AI coding tools multiply PR volume beyond review capacity.

AI coding tools have multiplied engineering velocity but not architectural judgment — what agentic engineering means, and why design conformance can no longer be an afterthought.

CDA catches architectural violations at the PR; MCP prevents them before the first line is written — how governed agentic development closes the loop on both ends.

Every CI/CD pipeline has build, test, and code-quality gates but none check design intent — why the missing gate matters most now that AI agents write the code.

SAST and dependency scanning miss design-level security exposure. Why enforcing architectural intent at every PR is the only way to do SecDevOps when AI agents write the code.

Code review asks whether code passes; design review asks whether a decision conforms to architectural intent — two distinct mechanisms, and why one cannot substitute for the other.

As AI multiplies the rate of architectural decisions, no architect's cognitive capacity can keep pace — a structural human limit, not a discipline problem, that scales with every AI tool adopted.

Architectural debt is two things — the drift that crept in silently and the risk your team approved knowingly — and today only one of them has an owner or a cost.

The automated, per-PR process that checks every code change against approved architectural intent at CI/CD speed — how it works, and why 'continuous' is the key word.

Every Continuous Design Audit verdict is fully traceable — which lenses ran, what triggered them, and why — so developers can contest blocks and AI agents can self-correct.

LLMs are not naturally deterministic. How rkito achieves reliable governance verdicts through rule-based lens selection, narrow AI context, and full call logging — an open research problem.