A pilot that automates one high-volume scenario pays back faster than a strategy deck that promises ten. The pattern with Agentic AI on Enterprise Plus is to start where the volume is real, the failure mode is contained, and the audit trail is mechanical. Below are the scenarios that consistently show return inside two quarters and the ones that look attractive in a deck but bleed in production.
Password Reset Automation
Highest-volume, lowest-risk scenario. Agent identifies user via SSO context, validates against a second factor, triggers reset through the IDP, notifies, and closes the ticket. Containment rate of 80% is realistic when the IDP is stable; the remaining 20% involve account lockouts, MFA registration drift, or shared accounts that need human triage. Payback typically lands inside 90 days because Tier-1 ticket displacement is immediate and the per-call cost (LLM tokens plus IDP API) is well under a dollar.
agent_scope:
- read user.email, user.employee_number
- call IDP /reset endpoint
- log to incident worklog with model + version
- exclude: privileged accounts, service accounts, recent-lockout usersAccess Request Routing
Complex approvals remain human; simple access grants for standard software, file shares, and low-risk SaaS auto-resolve through the agent. The decision boundary is explicit — approval risk score, requester history, asset sensitivity. Above the threshold, the agent constructs an approval task with the right approver and routes; below the threshold, the agent grants and logs. Provenance is the audit trail in sys_audit plus the model invocation record. The ops team handles exceptions instead of every request.
Incident Triage
The agent classifies inbound incidents, sets category and subcategory, picks the assignment group from a Decision Table fed by the classification, and escalates urgent cases (paging on-call when severity matches a paging rule). Humans handle resolution on complex incidents but skip the initial triage toil. The agent does not close incidents; it only sets up the human’s first action. Misclassification rate stabilizes around 8-12% with a feedback loop that retrains the classification weights from corrected tickets.
Knowledge Article Surfacing
Inside the agent workspace, the agent retrieves the top three knowledge articles most likely to help, ranked by historical resolution correlation rather than naive vector similarity. The fulfiller sees the suggestions in the sidebar and clicks through. Measurable lift in mean time to resolve when the knowledge base is well-curated; minimal lift when articles are stale or duplicated, which makes this scenario a forcing function for KB hygiene.
What to Avoid
High-stakes autonomous actions on financial systems, compliance-relevant workflows, or security incidents. The risk-reward does not pencil out — a single bad action costs more than a year of automation savings. Keep humans firmly in the loop, use agents to assist with summary, recommendation, and draft action, and require human confirmation before anything writes to a financial or compliance system. The same caution applies to anything customer-facing where wrong action is a public incident.
Common Failure Modes
Token cost runaway when a poorly-scoped agent retries on every transient error — set per-conversation token caps. Audit gaps when the agent action does not record which model and version were used — log the model version on every action. Containment metrics inflated by counting tickets the agent merely touched — measure containment as ticket closure without human edit, not first agent action.
What to do this week: pick the single highest-volume Tier-1 ticket category in your queue and scope an agent for exactly that scenario; everything else can wait until the first one ships.
