An AI agent approval workflow should help teams move from idea to controlled launch without guessing who needs to review what. The workflow should be proportional: low-risk agents should move quickly, while agents with sensitive data, external impact, or action-taking authority should receive deeper review.
The template below can be adapted into intake forms, ticket workflows, governance platforms, or deployment gates.
Step 1: intake
Collect agent name, owner, purpose, users, environment, model or vendor, data categories, retrieval sources, tools, autonomy level, expected outputs, and launch timeline. Ask whether the agent can send messages, update records, execute code, trigger transactions, or influence regulated decisions.
The intake should produce an initial risk signal, not a final approval.
Step 2: classification
Classify based on autonomy, data sensitivity, affected users, external exposure, regulated function, tool permissions, and reversibility of actions. Document the rationale and unknowns.
Unknowns should not disappear. They should route to the owner for clarification.
Step 3: reviewer routing
Route reviews based on risk. Security reviews tool access, prompt injection, permissions, logging, and incident response. Privacy reviews personal data, retention, and vendor processing. Legal and compliance review obligations, disclosures, and restricted uses. Business owners approve purpose and residual risk.
High-risk agents may require executive or risk committee approval.
Step 4: control conditions
Before launch, define required controls: least privilege, human approval for sensitive actions, monitoring, logging, output validation, rate limits, user notices, escalation, and change review. Conditions should be testable.
Step 5: launch and review
Record decision, reviewers, rationale, conditions, evidence location, launch date, and next review date. After launch, monitor blocked actions, approvals, incidents, overrides, and changes.
A good approval workflow does not create a bottleneck for every AI idea. It creates a dependable path for agents that can affect data, systems, customers, or compliance posture.