AI-generated automation has one obvious risk: the AI can be confidently wrong. It might reference a field that doesn't exist, wire two nodes together incorrectly, or leave a required credential blank — and still present it as a finished, working workflow.
It's validating what it produces against real, deterministic rules before anyone acts on it: checking that every node is actually reachable from a trigger, that credentials are structured correctly, that connections reference real field names. This kind of check doesn't guess — it either passes or it doesn't, the same way a compiler either accepts your code or tells you exactly what's wrong with it.
Gydmation AI validates every generated workflow against real n8n node schemas before showing it to you, and our self-healing loop re-checks a workflow after every fix attempt rather than assuming the fix worked. Reliability in AI automation comes from checking the output, not from trusting the model more.
