A training impact report for AI agents shows leadership what changed in decisions, not how many people attended. It compares measures taken from the agent's record before and after training: overturned approvals, missed red lines, held-queue times, calibration agreement and certificate coverage. The template gives each section, the measure, its source and a sample filled in.
Who reads the report, and what do they need?
Executives and boards approve the risk appetite for agents and the money for training. They want to know two things: are the people overseeing the agents competent, and is that competence turning into safer, faster decisions? Attendance figures answer neither. The report should use the record, where every verdict and every approval is filed with who decided and why.
It should also show coverage, because competence decays. Each person's certificate has an expiry date; each agent's certificate lapses after 90 days. A report that says "everyone was trained" without saying whose certificate is current next month is out of date the day it is read. How to report agent risk upward more broadly is covered in how to report on AI agent risk to the board.
How do you fill it in?
Take a baseline before training starts, ideally from a shadow period where the agent drafts and people decide. If there is no baseline, use the first month after go-live and say so. Pull each measure from the record for the same length of period after training. Use counts, not ratios, where the numbers are small. Keep it to two pages.
The starting point is often the AI agent readiness assessment report template, which records where the team stood before training. The method for choosing measures is set out in how to measure the impact of AI agent training.
The report template
| Section | Measure | Source | Before | After |
|---|---|---|---|---|
| 1. Scope | Agents, teams, period, number of people trained | Training register | ||
| 2. Coverage | People on the approval rota with a current certificate | Certificate register | ||
| 2 | Certificates expiring in the next 90 days | Certificate register | ||
| 3. Decision quality | Held actions decided | Record | ||
| 3 | Approvals later overturned | Record | ||
| 3 | Missed red lines | Record and incident log | ||
| 4. Flow | Median time from hold to decision | Record | ||
| 4 | Held items waiting more than one working day | Record | ||
| 5. Consistency | Calibration agreement on shared cases | Calibration logs | ||
| 6. Incidents | Incidents involving a human approval | Incident reports | ||
| 7. Agent depth | Capabilities moved up or down in depth, and why | Depth decisions | ||
| 8. Gaps | Teams or roles where measures did not improve | Analysis | ||
| 9. Decisions sought | What leadership is asked to approve | Report author |
Section 6 should draw on incident reports written in a standard format, such as the AI agent incident report template, so incidents can be compared across periods.
Worked example
An illustrative manufacturer reports on a quarter after training its finance team, which reviews an agent covering invoice matching and the record-to-report close.
- Scope: one agent, 18 people trained, comparing the three-month shadow period with the following three months.
- Coverage: 12 of 12 people on the approval rota hold a current certificate; three expire within 90 days, renewals booked.
- Decision quality: held actions decided rose from 1,100 to 1,400 as the agent took more work. Overturned approvals fell from 46 to 15. Missed red lines fell from 3 to 0.
- Flow: median time from hold to decision fell from five hours to two.
- Consistency: reviewers agreed on 8 of 10 calibration cases, up from 5 of 10.
- Incidents: one, a lapsed agent certificate handled to procedure.
- Agent depth: invoice matching approved to move from depth 2 to depth 3, based on the agent's certification and the reviewers' record.
- Gaps: intercompany reconciliations showed no improvement; a targeted calibration series is proposed.
- Decisions sought: approve the same training for the treasury team before its agent's shadow period starts.
Where the Academy fits
Pryme Intelligence Academy supplies part of sections 1 and 2. For teams, the admin view shows progress, results, certificates and expiry dates, and every certificate can be checked at source through VerifyDoc.ai. Sections 3 to 7 come from the business's own record. The guide to training teams for AI agents explains how the two fit together.
Questions people ask
What if the numbers get worse after training?
Report it. Often the agent took on harder work at the same time, which raises held volumes and overturns. Say so, and show the measure per type of held action if you can.
Should the report name individuals?
No. Leadership needs team-level results. Individual results belong with team leads for coaching and with the certificate register.
How often should the report be produced?
Quarterly in the first year of a deployment, then twice a year. Produce an extra one after any serious incident involving a human approval.