AI agent post-launch review for the first 30 days

Go-live starts the evidence window. It does not finish the operating decision.

An agent can stay online and complete work while correction time rises, exceptions age, approvals time out, or permissions drift. A production review must show whether the approved workflow is producing the intended result without hiding the work and risk that moved to people.

This AI agent post-launch review is built for the first 30 days in production. Use one record for one workflow and one authorized boundary. Bring forward the baseline and decision from the AI agent pilot metrics scorecard and pilot decision template. If evidence is missing, write UNPROVEN.

First-30-days production review worksheet

Keep the review record readable. Link to logs, case samples, approvals, incident records, and change history instead of copying every detail into the worksheet.

AI agent first-30-days operating review

1. Production workflow and authorized boundary: Workflow ___; allowed users, cases, systems, data, actions, volume, and hours ___; prohibited actions ___.
2. Review window and operating volume: From ___ to ___; attempted runs ___; completed runs ___; representative case mix or limitation ___.
3. Pilot baseline and current business result: Pilot baseline ___; production result ___; same unit ___; changed condition or context ___.
4. Accepted without material correction: ___ of ___ reviewed outputs = ___%; acceptance rule ___; evidence/sample ___.
5. Human review and correction time: Review ___ minutes/case; correction ___; exception handling ___; total burden vs. baseline ___.
6. Exceptions: Count ___; rate ___; current backlog ___; oldest age ___; material types ___; owner ___.
7. Approvals: Requested ___; approved ___; denied ___; timed out/no response ___; failed approval boundary events ___.
8. Prohibited-action and control events: Attempts/events ___; blocked ___; not blocked ___; owner and disposition ___.
9. Duplicate, partial, and failed runs: Duplicates ___; partial completions ___; failed runs ___; recovery status ___.
10. Source and permission failures: Stale/unavailable source events ___; permission denials ___; improper access events ___; effect ___.
11. User and operator feedback: Useful patterns ___; friction ___; unsafe or confusing behavior ___; evidence link ___.
12. Incident, pause, and rollback events: Incident count/type ___; pauses ___; rollback invoked/tested ___; recovery result ___.
13. Permission, scope, and configuration changes: Change/date ___; approver ___; reason ___; new evidence window required ___.
14. Unresolved UNPROVEN items: Missing proof ___; why missing ___; consequence ___; evidence owner/date ___.
15. Accountable review owner and date: Owner ___; role/authority ___; review date ___.
16. Disposition: Continue / Change / Pause / Roll back; evidence-based rationale ___.
17. Next authorized boundary: Allowed workflow/cases/actions ___; volume ___; retained human approvals ___; still prohibited ___.
18. Actions with owner and date: Action ___ / owner ___ / due ___ / evidence expected ___; repeat for every action.
19. Next evidence review: Date ___; evidence window ___; required measures/events ___; attendees ___.

The worksheet is not complete because every blank contains text. The evidence must support the entries, and each named owner must understand the work and authority attached to the role.

Uptime is not operating success

Technical availability answers whether the system could run. It does not answer whether the work was accepted, whether people spent more time checking it, whether exceptions reached an owner, or whether the agent stayed inside the authorized boundary.

Review six views together:

1. Business result: Did the workflow change in the same unit used for the pilot baseline?
2. Quality: What share met the agreed standard without a material correction?
3. Human burden: How much review, correction, approval, and exception time did people spend?
4. Exceptions and failures: What left the routine path, how old is the backlog, and who owns it?
5. Controls: Did approvals, prohibited-action blocks, permission boundaries, and stop behavior work every time they were needed?
6. Change: Did scope, permissions, sources, tools, configuration, instructions, ownership, or operating context change?

A green uptime chart can coexist with a poor operating result. Keep uptime in the record, but do not let it stand in for business, quality, burden, exception, and control evidence.

Compare production with the pilot without mixing windows

Use the same business measure and unit recorded in the 30-day AI agent pilot plan. Label the pilot period, production period, case count, and case mix. Keep changes in volume, staffing, source data, workflow, permissions, model or instructions visible.

Do not merge pilot and production cases into one average. A production result should be compared with the preserved pilot baseline, not used to rewrite it. When conditions differ enough to break comparison, record the difference and mark the conclusion UNPROVEN until a valid evidence window exists.

A practical review cadence

Launch day: confirm the operating boundary

Confirm the deployed workflow, runtime identity, permissions, human approvals, prohibited actions, monitoring, exception owner, pause path, and rollback status. Record the first-week and day-30 review dates before volume grows.

First week: inspect the work behind the averages

Review representative completed cases, corrections, exceptions, approvals, failures, control events, feedback, and any change from the launch record. Fix an evidence gap early rather than carrying it to day 30.

Day 30: make the operating decision

Review the full evidence window and choose Continue, Change, Pause, or Roll back. Record the next boundary, every action owner and date, and the next evidence review.

Reopen after a material event

Do not wait for the calendar after a material change to scope, permission, data, connected tool, model or instruction, approval, ownership, control, configuration, or incident. Reopen the review, preserve the prior window, and decide what new evidence is required.

No universal numeric threshold fits every workflow. Set thresholds from the approved consequence, baseline, operating objective, and control boundary. Never invent a pass rate because the review needs an answer.

Compact decision rubric

Continue

Choose Continue when the evidence supports the business result, accepted quality, human burden, exception ownership, controls, and current authorized boundary. Record what continues and the next review date.

Change

Choose Change when a specific, testable operating or design change can address a source gap, instruction problem, recurring correction, approval friction, exception pattern, or workload issue. Preserve the old evidence window and define a new one.

Pause

Choose Pause when work should stop while the team restores required proof, ownership, control, permissions, recovery capability, or exception capacity. Name who can restart the workflow and what evidence they need.

Roll back

Choose Roll back when the current production version or boundary should be disabled, revoked, or returned to the last approved state. Record in-flight work handling, recovery evidence, and the authority required before another start.

Favorable averages do not erase a prohibited action, failed approval, missing owner, uncontrolled permission change, material exception backlog, or untested recovery. Missing required proof remains UNPROVEN.

Run the day-30 review in 30 minutes

Pre-read: one operating record

Send the completed worksheet, pilot baseline, production measures, representative corrections, exception and approval logs, control events, incidents, changes, and UNPROVEN items. The meeting should test the evidence and make a decision.

Minutes 0-8: result and human burden

Confirm the workflow and evidence window. Compare the current business result with the pilot baseline in the same unit. Review accepted-without-material-correction and total human review, correction, approval, and exception time.

Minutes 8-16: exceptions and controls

Review exception count, rate, backlog, age, and ownership. Inspect approval outcomes, prohibited-action events, source and permission failures, and duplicate, partial, or failed runs.

Minutes 16-22: change and incident review

Review feedback, incidents, pauses, rollback events, and every material scope, permission, source, tool, configuration, model, instruction, approval, ownership, or control change.

Minutes 22-30: disposition and next evidence date

Choose Continue, Change, Pause, or Roll back. Record the accountable owner and date, next authorized boundary, every action owner and due date, and the next evidence review. Human authority remains explicit at every approval, pause, restart, and rollback decision.

Keep the production boundary visible

The AI agent readiness checklist defines a candidate workflow. The pilot plan, metrics scorecard, and decision template create bounded evidence. The AI agent production readiness checklist sets the go-live gate. This review keeps that gate visible after launch.

Microsoft 365, Teams, Copilot Studio, Power Platform, Fabric, and Power BI can support delivery, evidence, and monitoring where they fit. They do not own the operating decision. A named business owner retains authority, supported by a technical owner who can observe, pause, and roll back the system.

That is how practical AI Agents connect with Executive Intelligence and AI strategy: the operating result, human burden, exceptions, controls, ownership, and next decision stay visible together.

Bring one production workflow and its first 30 days of evidence. Leave with a decision, owner, next boundary, and next evidence date.

Book an AI Operating Review

FAQ

What should an AI agent post-launch review include?

Review the production workflow and boundary, evidence window, operating volume, business result against the pilot baseline, accepted-without-material-correction rate, human review time, exceptions, approvals, failures, control events, feedback, incidents, scope changes, unresolved proof, accountable owner, disposition, next authorized boundary, action owners, and next evidence date.

When should an AI agent be reviewed after launch?

Confirm the production boundary on launch day, review early operating evidence during the first week, hold a day-30 operating review, and reopen the record after a material scope, permission, data, tool, model, instruction, approval, ownership, control, or incident change.

Is technical uptime enough to show an AI agent is successful?

No. Uptime shows availability. Operating success also requires evidence about the business result, output quality, human review and correction time, exceptions, approvals, failures, control events, and whether the authorized boundary held.

How should production results be compared with the pilot baseline?

Use the same business measure and unit, label the production evidence window and operating volume, and keep changed conditions visible. Do not blend pilot and production samples into one average or compare unlike periods without noting the difference.

What should trigger a Pause or Roll back decision?

A prohibited action, failed approval, missing accountable owner, uncontrolled permission change, material exception backlog, untested recovery, or another breached operating boundary should override favorable averages. Missing required proof remains UNPROVEN.