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2 min read

What happens when an AI agent does not know the answer?

Silviu Major·Founder, Fiveleaf·

Ask a vendor this in your first call. The answer tells you more than any case study will.

A good answer describes a confidence threshold and a clean escalation. A bad answer is some version of "our AI handles everything", which means either they have not run one in production or they are hoping you will not check.

No agent handles everything. The ones claiming to are the ones that strand people in loops.

The two ways it goes wrong

It guesses. This is the failure everyone worries about, and it is genuinely fixable. An agent answering from a model's general knowledge hallucinates somewhere in the 15 to 27% range. One that retrieves from a verified knowledge base before responding drops to under 1.5%.

That difference is not a better model. It is whether the thing is grounded in your actual content, and it is the single most important technical requirement in the whole category.

It hedges. Less discussed and more common. The agent answers correctly, then adds "please confirm with a member of our team before proceeding". The customer reads that as uncertainty, asks for a human, and you have an escalation on a question the agent answered perfectly well.

We hit this ourselves. Resolution rate sat stuck for weeks while the transcripts looked fine, because the agent was being politely unsure at the end of correct answers.

What good looks like instead

Three things, and they are all architectural rather than conversational.

An explicit threshold. Above it, the agent answers directly and without hedging. Below it, it stops trying and hands over. The mushy middle, where it half-answers and half-defers, is where trust goes.

Routing that means something. Billing goes to billing, faults go to faults. Not one undifferentiated queue where a specialist question waits behind twenty password resets.

Context carried across. The human picks up with the customer verified, the issue captured and the transcript attached. If the customer has to start again, the handover failed no matter how good the first half was.

Done properly, most people do not notice the conversation changed hands.

The bit worth being blunt about

A bad handover is worse than no agent. "I'll connect you to someone who can help", then five minutes of silence, then a person asking for the account number again, is precisely the experience that taught a generation of customers to type "agent" the moment a chat window opens.

You are not just failing that conversation. You are confirming a suspicion they already had, and it carries into every future interaction.

What to ask before you sign

Ask to see the threshold. Ask what percentage of conversations cross it. Ask what the human sees when a handover lands, and ask to look at that screen.

A partner who runs agents in production can show you all three. One who only has a demo will start talking about the technology instead, and that pivot is your answer.

Frequently asked

How do I stop an AI agent making things up?
Ground it. An agent answering from a model's general knowledge hallucinates somewhere in the range of 15 to 27% of the time. One retrieving answers from a verified knowledge base before responding drops to under 1.5%. That single architectural choice moves the risk by more than an order of magnitude.
Should the agent always offer a human?
The route to a human should always be available and obvious, but the agent should not offer it reflexively. An agent that hedges every answer with 'please confirm with our team' trains customers to skip it entirely, which defeats the point.
What does a good handover actually include?
The customer already verified, the issue captured, and the full transcript attached, routed to the team that owns that problem rather than a general queue. If the customer has to repeat themselves, the handover failed regardless of how good the conversation was beforehand.

If you want help building this

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About the author

Silviu Major, Founder, Fiveleaf

Silviu Major

Founder, Fiveleaf

10+ years building automation systems inside enterprise SaaS, now applying that same operational rigour to AI implementation for mid-market businesses. Writes about what works (and what doesn’t) from inside live deployments, not from the outside looking in.

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