Grounding AI agents so they stop making things up

Retrieval: answer from your data, not from memory

An ungrounded model answers from its training data, which does not contain your refund policy, your delivery timelines, or this customer's order. Retrieval means the agent looks up the relevant material at question time and answers from that text specifically.

The quality of a retrieval system is mostly the quality of what you put into it. Contradictory documents produce contradictory answers, and an outdated policy page will be quoted with total confidence. Curating the source material is the work; the vector database is the easy part.

• One authoritative source per topic — retire the old version rather than keeping both
• Chunk by meaning, not by character count, so a policy is not split mid-clause
• Store the source URL with every chunk so answers can cite where they came from
• Re-index when the source changes, on a schedule you can actually verify ran

Tools: let it look things up rather than guess

Retrieval handles documents. Live facts need tools — a function the agent can call to fetch this customer's order, check stock, or read a ticket. Without tool access, an agent asked “where is my order” has only one option, which is to invent something plausible.

Keep tools narrow and read-only until you have evidence the agent behaves. A tool that fetches order status is safe. A tool that issues refunds is a different risk category and should require a human confirmation step long after the rest is live.

Every question your agent cannot answer with a document or a tool call is a question it will answer by guessing.

Escalation: the most important feature

The agent needs an explicit, well-trodden path to a human, and it needs to take it readily. Low retrieval confidence, an angry customer, anything touching money or a complaint — these should escalate rather than resolve.

Escalation has to carry context. Handing a human an empty ticket that says “customer needs help” wastes the entire interaction. Pass the transcript, what the agent found, and what it was unsure about, so the human starts where the agent stopped.

What to measure once it is live

The third number is the one to watch in the first month. A high deflection rate with a high correction rate is not a working agent; it is a fast way to give wrong answers at scale.

• Deflection rate — resolved without a human, the headline number
• Escalation quality — how often a human immediately agrees the escalation was right
• Correction rate — how often a human has to undo something the agent said
• Unanswered topics — the questions retrieval had nothing for, which is your content backlog

A sensible rollout order

Start internal. Point the agent at your own team's questions first, where a wrong answer costs a laugh rather than a customer. Then move to customer-facing but suggest-only, where a human approves each reply and you can measure how often they edit it.

Only then go autonomous, and only for the question types where the correction rate is already near zero. This sequence takes a few weeks longer and removes nearly all of the risk that makes AI projects get switched off after a bad week.