For a decade, no-code automation democratized integration — but it never eliminated engineering thinking. Mapping JSON pathways, handling webhooks, and managing error branches still required non-developers to reason like software architects. AI-Native No-Code platforms represent a categorical break from that model. Instead of specifying how a process must run step by step, the human operator defines the intent — the target end-state — and an autonomous agentic system plans, selects tools, executes tasks, self-corrects errors, and coordinates across systems without traditional software engineering. Organizations deploying AI-Native automation are reporting 70–85% reductions in workflow build time and — more significantly — the ability to automate the exception-heavy 30% of processes that deterministic no-code tools could never reliably handle. The governance challenge is proportional to the capability gain: when non-engineers can deploy autonomous agents in minutes, traceability, least-privilege scoping, and determinism hybridization become the primary enterprise risk controls.