What “grounded AI” actually means for your helpdesk
The difference between an AI that answers from your runbooks and one that answers from the internet is the difference between an employee and a stranger.
Ask a general-purpose chatbot how to reset a VPN token and you will get a perfectly fluent answer — for someone else’s VPN, at someone else’s company, with someone else’s policy.
Fluency is not the hard part anymore. Relevance is. An IT answer is only useful if it matches your environment: your VPN vendor, your naming conventions, your approval policy, the fix that worked the last three times this happened.
Grounding, concretely
Grounded means the model’s answer is constrained to knowledge you control. In Resolv, that is three layers: your runbooks (step-by-step procedures the agent follows exactly), your documents (policies, guides, contracts of behaviour), and your past resolutions (what actually fixed things before).
Every answer cites where it came from. And when the knowledge base has no answer, the agent’s job is to say so and escalate — not to improvise something plausible.
The honest failure mode
That last behaviour is the one to test in any evaluation: what does the system do when it does not know? An ungrounded model fills the silence with confidence. A grounded one exposes the gap — which tells you exactly which runbook to write next.
Over time, that loop is the real compounding asset: every gap found becomes knowledge written, and every answer gets more precisely yours.