Why AI support agents give wrong answers

AI support agents can give wrong answers when their source material is outdated, they lack the user's screen or account context, or they guess without enough evidence. That's the problem we built the Frigade Assistant around. It lives inside your product, learns it by using it, and keeps that knowledge current as you ship.

Upgrading the model won't update an article that points to the wrong screen.

The docs are behind the product

When an agent answers from a help article, its instructions depend on when that article was last updated. That's fine for billing policy and refund rules. It breaks for "how do I do X," because X moved in last Tuesday's release.

Suppose a team ships a settings redesign. The integration toggle moves from Settings to a new Connections page. The help article still says Settings, so the agent says Settings, with full confidence, because that's what its source says. The user goes to Settings, doesn't find it, and has to ask support where the toggle went.

Nothing here is actually a model failure, because the model summarized its source correctly and the source was wrong. We wrote more about this cost in the freshness tax.

Frigade Assistant learns workflows directly from the product alongside the documentation you connect. When it learns the revised workflow, it can update the instructions it gives users.

The agent can't see what the user sees

A few things are going on when a user asks "why can't I export this?"

1. The answer depends on their plan. Exports might be a paid feature.
2. It depends on their role. Maybe only admins can export.
3. It depends on the screen they're on. The export button might live one level up.
4. It depends on state. Maybe the report is still processing.

If your chatbot only receives the question and a help article, it cannot check the user's plan or whether the report is still processing. It has to ask for that context or retrieve it through an integration.

An agent that runs inside the product can read the screen and use the account context your app provides. Then "why can't I export this" gets an answer about this report, on this plan, for this role.

Seeing the screen is only part of it, though. The agent also needs to know how the product works around the spot the user is in. Which setting upstream controls this button, what has to happen before the export unlocks, and where the user goes next. A screenshot of the reports page tells you there's a greyed-out button. The user may need to connect a data source first, so the agent needs to know that prerequisite and where to configure it.

The Frigade product agent learns those dependencies by using your product. The Assistant combines that knowledge with the current screen to explain what the user needs to do next.

The agent isn't allowed to say "I don't know"

If you measure success only by avoided handoffs, an unsupported answer can look like a resolved question. An agent without a good source may still produce a plausible answer.

A plausible wrong answer can send someone through unnecessary steps and leave support with a problem to untangle.

We think an agent should refuse when it would have to invent the answer, say what it doesn't know, and hand off to a human with the conversation attached. A handoff may lower deflection, but it gives support a chance to resolve the question before the customer acts on a guess.

How to check your own agent

Pull twenty conversations where users said the answer was wrong. For each one, save the question, the source the agent used, and the relevant account or screen context. Was the source outdated, was needed context missing, or did the agent answer without enough evidence? Fix the cause, then rerun the original question. Keep these cases in your release checks and review new failures for causes the checklist misses.

Help centers will keep falling behind products that ship every week, so an agent that learns from the product itself will have fewer of these failures to catch.