How do you keep AI agent training current when the product changes?

Version the training to the product, and make currency expire. Every lesson and credential should say which product version it covers. When the product changes, people take a short update module rather than the whole course again, and their certificate lapses unless they pass a renewal check. Agents' own certificates lapse after 90 days for the same reason.

Why does agent training go stale faster than other training?

Two things move at once. The product changes: new capabilities appear, the workspace shows new information, and the platform's guardrails are revised. And the agents themselves change: a new capability is certified, depth rises or falls, a source is added, the business's own limits are adjusted. A reviewer trained on how the workspace looked six months ago may misread a held item today.

The second kind of change is the one teams forget. Training is not only about the product's features; it is about what this agent, in this team, is now allowed to do.

What keeps training current?

1. Stamp everything with a version. Lessons, tests and certificates should name the product version they reflect. A credential that does not say what it covers cannot tell you whether it is out of date.
2. Write update modules, not new courses. When something changes, teach the change: what is new, what behaves differently, what to watch for. Ten or twenty minutes is usually enough.
3. Tie renewal to a short check. A brief quiz after the update module confirms people absorbed it. Certificates lapse if it is not passed.
4. Brief teams on agent changes locally. When an agent's depth, bounds or sources change, the team that reviews it needs to know before the change goes live. The product owner should own that briefing; see what a product owner needs to know about AI agents.
5. Keep sources current, too. Agents should cite the current version of each policy, which is covered in how to let an AI agent cite the current version of a policy. Training that refers to old policy undermines both.

What does a change look like in practice?

Take a claims team whose workspace adds a new verdict view showing which of the five bounds decided each hold. The release notes go to IT. Two weeks later, a team lead notices reviewers still opening each held claim's history to work out why it was held, as they always had.

The fix is an update module: a fifteen-minute lesson on reading the new view, followed by a five-question check. Reviewers complete it within a week. The team lead adds a line to the team's onboarding checklist, an approach set out in the reviewer onboarding checklist for claims team. When the agent's settlement limit rises a month later, the same pattern repeats: a short local briefing, a check, and only then the change.

Which controls stop people working on stale knowledge?

• Certificates with an expiry date, visible to managers.
• Approval rights that depend on a current certificate, a practice a business can adopt; see whether approval rights can depend on a current certificate.
• A change process that includes a training step before an agent's depth or bounds change.
• An admin view showing who has completed each update.

The same logic applies to customer-facing knowledge. An agent that answers from the help centre is only as current as its articles, as discussed in how to answer product questions from the help centre with an AI agent.

Where the Academy fits

Pryme Intelligence Academy stamps the Foundations badge with the product version, so anyone can see what it covered. Certified Pryme Operator is valid for 12 months, and renewal is an update module and a short quiz. A lapsed certificate shows as lapsed on its VerifyDoc.ai page, so nobody relies on an out-of-date credential. The topic guide on training teams for AI agents covers renewal planning.