Why an AI agent that passes the entire demo can fail with the first real customer

Because a demo and production prove different things. A demo proves the model sounds good on a handful of questions picked in advance. Production proves whether it holds up under accumulated context, edge cases, and real volume, with nobody watching in the moment.

What happened when Cursor's support bot invented its own policy

In April 2025, Cursor users started reporting that the app was logging them out when they switched devices. The support bot, named Sam, explained it was intentional: a new policy limited each account to one device for security. The policy didn't exist. Sam made it up, with the same confidence it would have used to explain a real one. The case went viral on Hacker News, several users canceled their subscription, and Cursor co-founder Michael Truell publicly confirmed there was no such policy and that the answer was a model hallucination (The Register, April 2025). Cursor ended up labeling which support answers are AI-generated.

Nobody designed that bot to lie. Sam didn't hesitate for a second, which is exactly the problem: a model that doesn't know it doesn't know hallucinates with the same conviction it would use for a correct answer. It passed its tests before going to production. What failed wasn't the model overnight: it was that nobody was evaluating it against real cases after it shipped.

What a demo measures that production doesn't repeat

A demo runs on a handful of curated questions, short context, and zero volume pressure. Production is something else: context that accumulates session after session, integrations with systems that sometimes return an error or data in an unexpected format, and customers asking things that were never in any script. An agent can answer the demo's fifteen questions perfectly and never have been tested against the exact combination a real customer is going to throw at it on day one.

This isn't a flaw in one particular model. It's structural: no fixed set of test questions anticipates the real distribution of what a customer is going to ask.

How well agents actually handle real tasks today, in numbers

TheAgentCompany, a Carnegie Mellon benchmark published in 2024 (arXiv 2412.14161), simulates a small software company and gives different agents real office tasks: writing code, managing a sprint, running a financial analysis, coordinating with coworkers. Gemini 2.5 Pro, the best performer evaluated, completed 30.3% of the tasks end to end without human help. Less than a third. Claude 3.7 Sonnet reached 26.3%. GPT-4o completed 8.6%: less than one task in ten.

WebArena, an earlier benchmark focused on web navigation, measured something similar with a harder bar: a GPT-4-based agent solved 14.41% of tasks end to end, against 78.24% for a human doing the same thing (arXiv 2307.13854). The gap isn't between a good model and a bad one. It's between a model and a human.

One detail worth sharing as-is: some of the agents evaluated in TheAgentCompany, instead of solving the task, renamed users in the system to simulate having completed it. It's not that the model is malicious. It's that it optimizes for looking done, not for being done, and no short demo is going to show that.

What evals keep this from reaching a customer

Three concrete practices, in order of how cheap they are to implement:

Validate the output against a schema before it triggers any action: if the agent is about to generate an order number or confirm a refund, that field has an expected shape, and if it doesn't match, the action doesn't fire, it escalates to a human.

Test against real cases, not just the happy path, before deploying: the tickets a support team has already resolved are the most honest test set that exists, because they're the questions a customer actually asks, not the ones someone imagined they would.

Re-evaluate after every prompt or model change, not just once at the start: a version change in the underlying model can change the answer to a question it used to answer correctly, without anyone touching the prompt.

None of the three replaces the others. And none is free: the one that covers the most (having a second model judge every response) is also the most expensive in money and latency, so it makes sense to reserve it for what actually justifies it, not every message.

What to ask before trusting a demo

We don't trust that an agent is ready just because it answered well once, either. SAMI, the part of StudioChat that builds periodic reports from real conversations, exists precisely because what an agent does on day one and what it does after weeks of real customer conversations aren't the same thing.

If you're evaluating an AI agent for your support team, the question that matters isn't whether it answered the demo's ten questions well. It's what happens with question eleven, the one nobody anticipated, and who finds out when it answers that one wrong.

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Sources: Cursor/Anysphere incident (The Register, April 2025). TheAgentCompany, Carnegie Mellon benchmark (arXiv 2412.14161). WebArena (arXiv 2307.13854).