Grounding an AI agent means giving it your own documents to read before it answers, so its response reflects your policies rather than a model’s general training. In Insulin, the mechanism is a knowledge base you create and attach.
The failure everyone worries about with AI at work is the confident wrong answer. The failure that actually costs teams time is quieter: an answer that is plausible, that nobody can check, and that turns out three weeks later to have described how the business used to work.
Both have the same cause. The model is answering from what it learned generally, not from what your company decided specifically. No amount of prompt-writing fixes that, because the information simply is not there.
Grounding puts it there. Here is the shape of the work.
What grounding actually changes
An ungrounded agent is reasoning from general knowledge. Ask it what discount needs approval and it will produce something that sounds like a policy — because it has read thousands of them — without having read yours.
A grounded agent searches your documents first, then answers from what it found, and returns the documents it used. The difference is not that the second one is smarter. It is that the second one is checkable. You can open the cited page and see whether the answer is right, which means a wrong answer is a five-second discovery rather than a three-week one.
That checkability is the whole point, and it is worth being blunt about the implication: grounding does not make an agent correct. It makes an agent auditable, and auditable is the property you can actually build a process on.
The four steps
1. Create a base for one domain. Not one base for the company. A knowledge base works best scoped the way your policies are scoped — finance runbooks in one, approval policy in another. Broad bases retrieve loosely, and loose retrieval is how you get a confident answer built from the wrong document.
2. Load material that is already authoritative. You do not write anything new for this. The documents that should go in are the ones a new hire would otherwise have to ask someone about — approval rules, pricing and packaging policy, contract templates, internal runbooks, support procedures. Material that already governs the work.
3. Attach it to the agent that owns the domain. In Insulin an agent only reaches the knowledge bases you attach to it, in Read mode for search or Edit mode if it should also maintain the files. That scoping is a feature, not a limitation: the finance agent holding finance material and nothing else is what makes its behaviour predictable.
4. Test with a question you already know the answer to. This is the step teams skip, and it is the only one that tells you anything. Ask something you can grade. Then read the citations — not the answer, the citations. If the answer is right but the source is wrong, your documents are the problem, and you have found it before anyone relied on it.
What to load, and what to leave out
The instinct is to load everything. Resist it. A knowledge base is not an archive; every document in it is a document that can be retrieved and cited, including the superseded one.
Load the material that is current and authoritative:
- Policy that has a decision in it — approval thresholds, what requires escalation, who signs off.
- Procedures somebody would otherwise explain verbally — reconciliation steps, onboarding sequences, support runbooks.
- Templates and their constraints — the clauses a team may agree to without escalation.
- Reference material with a stable answer — packaging definitions, naming conventions, the meaning of internal terms.
Leave out drafts, superseded versions, and anything whose status is ambiguous. A retired policy in a knowledge base is worse than no knowledge base, because it will be retrieved and cited with exactly the same confidence as a current one — and the citation makes it look verified.
The maintenance rule follows from that: when the policy changes, update the document. The answers update with it, everywhere the agent runs, without anybody re-training anything.
Where grounding follows the agent
A knowledge base is not a place you visit. Once attached, its grounding travels with the agent into every surface it works in — an interactive chat session, a shared channel with several teammates, and an unattended scheduled job that runs at seven in the morning with nobody watching.
That last one is the reason to get grounding right before you automate anything. An interactive answer gets read by a person who might notice it is wrong. An unattended run at month-end produces output that goes straight into someone’s inbox with the authority of a completed process. The grounding is what makes that output worth the same as the interactive kind.
Frequently asked questions
What does it mean to ground an AI agent? Grounding means the agent searches your own documents before answering and returns the sources it used, so the response reflects your policies rather than the model’s general training.
How do I attach documents to an Insulin agent? Create a knowledge base for one domain, add your documents, then attach the base to the agent that owns that domain. An agent only reaches the bases you attach to it.
Should I put all company documents in one knowledge base? No. Scope a base the way your policies are scoped — one domain per base. Broad bases retrieve loosely, which produces confident answers assembled from the wrong document.
How do I test whether grounding is working? Ask a question you already know the answer to, then read the citations rather than the answer. A right answer with a wrong source means the documents need fixing.
Does grounding guarantee the agent is correct? No. It makes the agent auditable. You can open the cited document and check the claim, which turns a wrong answer into something you catch immediately rather than weeks later.
Takeaways
- Grounding does not make an agent correct — it makes it auditable, which is the property you can build a process on.
- Scope one knowledge base per domain. Broad bases retrieve loosely.
- Load material that already governs the work; leave out drafts and superseded versions.
- Test with a question you can grade, and read the citations rather than the answer.
- Grounding follows the agent into chat, channels, and unattended runs — get it right before you automate.
Insulin knowledge bases ground agents in your own documents and return cited sources on every answer. Explore Insulin knowledge bases, see how agents are scoped, or get a demo.
Sources
Primary sources for the platform rules cited above. Last verified August 14, 2026. Cloud providers change fees, eligibility, and program terms without notice — check the source before relying on a figure.
- Suger Insulin docs: Knowledge Base — How a knowledge base is created, how documents are indexed, and how an agent searches it before answering.
- Suger Insulin docs: Agents — Knowledge bases attach to an agent in Read mode (search only) or Edit mode (create, update, deprecate files), and an agent reaches only the bases attached to it.
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