Approved scope record
You know the result the Agent owns, the work included, the work excluded, the exception triggers, the success metrics, and the client owner.
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AI Team treats go-live as an approval gate, not a button after checkout. The gate confirms scope, access, cost controls, approval rules, QA evidence, dashboard metrics, pause rules, and human operator coverage.
Evidence pack
A managed Agent should not enter production because it worked once. AI Team needs evidence for repeatable operation, handoff, audit, and rollback.
You know the result the Agent owns, the work included, the work excluded, the exception triggers, the success metrics, and the client owner.
Every connected system, OAuth grant, service account, API key, role, data boundary, and revocation path is documented before live use.
You approve what the Agent may do automatically, what needs human review, and what must stop or escalate.
You get reviewed evidence for representative runs, blocked states, vendor failures, approval paths, dashboard metrics, rollback expectations, and known limitations.
Go-live checklist
This checklist protects you from uncontrolled automation and protects service quality from margin, support, security, and reliability failures.
QA signals
A strong deployment proves the Agent behaves well when inputs, vendors, permissions, cost limits, and approval paths are imperfect.
The Agent is tested against realistic examples, edge cases, missing fields, duplicate records, bad links, and malformed content before live work starts.
Actions stay inside client-approved rules, and the Agent pauses when instructions conflict with access, cost, privacy, or safety constraints.
You can review what ran, what was approved, why work paused, what it cost, which provider was involved, and what the dashboard reports.
Approvers and operators get enough context to accept, reject, edit, pause, or escalate without reconstructing the whole task.
Pause rules
The readiness gate blocks activation when the Agent would otherwise run with unclear access, costs, approvals, data quality, or risk boundaries.
Client asks the Agent to operate outside the confirmed scope.
Required provider access, OAuth grant, API key, or service account is missing or revoked.
Provider usage could exceed the approved spend cap, warning threshold, or billing responsibility.
The workflow touches regulated, legal, medical, financial-advice, employment, credit, or safety-critical decisions.
The Agent cannot produce reliable output from available source data.
A vendor outage, rate limit, webhook failure, suspicious access event, or incident path blocks safe operation.
Operator approval
AI Team can automate large parts of QA and monitoring, but production activation still needs accountable human review until there is enough evidence to widen automation safely.
Related controls
These pages explain the controls you should understand before approving a managed Agent.
FAQ
These answers clarify what must be true before you connect systems, approve pass-through costs, or depend on an Agent.
Yes. Setup gathers scope, access, rules, and operating context. Deployment readiness is the final evidence review that confirms the Agent can run safely under the approved scope.
Yes. If required access, cost approval, policy boundaries, reliable inputs, dashboard metrics, or human approval paths are missing, AI Team should pause, re-scope, or keep the Agent in setup.
The client approves scope, connected systems, credential ownership, pass-through cost responsibility, spend limits, approval rules, exception rules, and the business owner for escalation.
AI Team approves build quality, QA evidence, run visibility, pause rules, dashboard metrics, operator coverage, incident path, and rollback or revocation readiness.
Ready to scope an Agent
AI Team can keep standard Agents affordable only if setup and deployment QA prevent risky, unclear, or expensive deployments.