Stale articles are why your AI support agent answers wrong.

The agent is only as current as the articles

An AI support agent doesn’t know your product. It knows your help center. Each release that changes a flow without changing its article teaches the agent something false.

Support and knowledge teams running these agents say so:

“70% [of them] at this point isn’t (it’s causing user confusion and degrading the chatbot and AI summary output) because no one has been treating them like living docs. So many are way out of date.”

A knowledge manager, on their help articles, r/technicalwriting, July 2026

“All of this.. Just for Fin not to know how to answer a question such as “How to log in”..”

An Intercom Fin admin, r/CustomerSuccess, February 2026. Later in the thread: “Final decision did land on deleting past 3 years of Docs (inside Intercom) and will start over from scratch because it totally got out of the control..” (reply)

“Auto-flag Help Center articles that go stale when we ship product updates, and suggest the edits needed.”

An Intercom customer’s feature request, which says it “improves Fin resolution rates” and “scales KB maintenance as release velocity increases”, Intercom Community, April 2026. A reply: “This update would be super impactful and reduce our time manually locating existing articles that require edits due to a product release.”

Customers see it from the other side. An engineer on an AI assistant built into a cloud console: “it sent me to an out of date documentation complete with old screenshots.” (Hacker News, January 2025)

The numbers we’d stand behind

Gartner surveyed 5,728 customers and published the results in August 2024: only 14% of customer service issues were fully resolved in self-service, and 43% of self-service failures happened because the customer couldn’t find relevant content (Gartner). That survey isn’t about AI agents or about stale content, but an agent answering from out-of-date articles is self-service with the same missing ingredient: relevant, correct content.

You may have seen a figure that about 40% of help articles are stale. We couldn’t find how it was measured, so we don’t use it.

Fix the articles, not the bot

A better model won’t fix an article that’s wrong. Three things will:

1. Know which articles are wrong. Not “old”, wrong: an article whose steps no longer match the product.
2. Hear about it when the product changes, not when the agent has already given a customer the old answer.
3. Update the whole article: the steps, the labels, the screenshots and any video, with a person approving what goes live.

Intercom’s Fin Operator helps with the text: give it a brief, such as release notes, and it proposes updates to every affected article, snippet and internal article (Intercom). It works from what you tell it changed.

Stagewright works from the product. It reads your Intercom, Zendesk or Productlane articles, works out which feature each one explains, and checks it against walkthroughs that run on every pull request: ▲ failing when that feature’s walkthrough fails, ◐ stale when the UI changed after the article was last edited or it names a label no screen has any more, ● fresh when it was checked against a current walkthrough. When a change is approved, the linked article is updated as a draft with new steps, screenshots and the video. The two fit together: Stagewright with Intercom Fin.

What it doesn’t do: build another chatbot, or check articles about flows that have no walkthrough (those are marked not checked). The help-center integrations are built against each vendor’s documented API and are being proven on live accounts with the first teams in the private beta.

More for support leads: for support and success. The wider problem: Your help center is behind your last release.