AI system go-live handoff checklist: 12 acceptance criteria you must verify before production
AI projects often fail at handoff, not because the model is weak but because rollback, monitoring, and cost controls are missing. Demo success does not ensure production adoption or readiness. This AI go-live handoff checklist covers 12 acceptance items teams typically skip, verifying that someone can address issues when they occur in production.
By
Tenten AI FDE 團隊
前線部署工程
Published
May 28, 2026
Read time
6 分鐘
When a system went live with no rollback mechanism, the PM asked: 'If the model modifies a customer's credit limit incorrectly, how does it get reversed?'
No one could answer. The system had been running for three weeks with no rollback mechanism, no audit logs, and no record of who held permission to modify that table. Demo had revealed none of these gaps because demo never touches production data. Once it did, the skipped verifications became obvious.
An AI go-live handoff checklist is a verification document completed before moving a system from PoC to production. It checks permissions, rollback, monitoring, cost controls, and ownership. Rather than confirming whether the system works, it identifies who will handle problems after deployment.
A PoC works through a controlled path. Production must handle actual permissions, actual traffic, actual billing errors, and actual budgets. This gap is where teams discover they've skipped essential verification, only after go-live.
What this AI go-live checklist verifies
These 12 items are drawn from field deployments. The first eight keep the system operational. The last four drive adoption. All 12 must pass before production launch.
| Item | Verification Question | Pass Criteria |
|---|---|---|
| 1. Permission Boundaries | What data can the model and agent read, and what writes can they execute? | Least privilege, isolated service accounts, write whitelists |
| 2. Data Permission Inheritance | Does RAG retrieval respect the user's original access permissions? | Document-level and row-level permission filtering; no unauthorized access |
| 3. Rollback Mechanism | Can every write the AI triggers be reversed? | Reversible operations or manual review gates for high-risk actions |
| 4. Version Control | Can prompts, models, and indexes roll back to specific versions? | All three are versioned; one-click rollback to previous version |
| 5. Monitoring and Alerts | Who knows about latency, errors, and tool invocation failures? | Metrics on a dashboard; actual people get paged when thresholds breach |
| 6. Observability | Can you trace which documents and prompts each response used? | Every response is traceable and reproducible |
| 7. Cost Caps | Can a runaway agent loop burn through the budget? | Daily/monthly limits, per-user rate limits, circuit breakers |
| 8. Evaluation Baseline | Do you have specific numbers for 'passing'? | Offline test set plus post-launch online sampling thresholds |
| 9. Graceful Degradation | What does the user see if the LLM or vector database goes down? | Fallback: escalate to human, use cache, or clear error message |
| 10. Security and Compliance | Are PII handling, audit trails, and vendor data terms documented? | PII masking, audit logs, and contract terms archived |
| 11. Runbook | Can the on-call engineer fix it at 3 a.m. following this guide? | Executable documentation, not oral handoff |
| 12. Adoption Owner | Who's tracking adoption rate, and what's the target? | Assigned owner, explicit metrics, weekly review |
The four items most frequently skipped
Permissions, rollback, monitoring, and cost caps are frequently skipped. Any single one jeopardizes production deployment.
Permissions. Teams often launch by giving the AI broad read access to speed deployment. Once RAG bypasses original data permissions, junior staff can retrieve documents they normally cannot access. Verification must ensure the model's data access respects user permission boundaries.
Rollback. Demo environments are read-only, which is why rollback often goes unverified. Production AI changes ticket status, sends email, and updates databases. The standard approach: high-risk writes require human approval until error rates stay below threshold for two consecutive weeks, then automation increases gradually.
Monitoring. One project failed to detect a hallucination rate stuck at 8% for three months after launch because no metrics review occurred. Monitoring must track latency, tool invocation failures, and sampled response quality. It must specify who receives alerts during operational hours. Without accountability, monitoring becomes performative.
Cost caps. An unbounded agent loop can exhaust a quarterly budget in a weekend. The solution requires daily limits, per-user rate limits, and circuit breakers. All three safeguards are necessary to prevent runaway costs.
Handoff requires more than documentation
Handoff requires documented procedures that enable on-call engineers to resolve issues. It requires designated owners accountable for adoption metrics and targets. Without adoption ownership, utilization typically stays in single digits.
These 12 items are launch gates. Teams remain deployed until every item passes verification. Demo performance is not sufficient. What matters: production operation, user adoption, and incident response capability.
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