Govern Agentic RMM Auto-Remediation

Tier: Advanced · Workflow area: Identity, endpoint & patch · ~40 minutes

This playbook establishes the operating rules for autonomous device remediation — Atera-class agentic AI, Intune remediations on auto-run, self-healing RMM scripts — so autonomy is granted in tiers, observed in logs, and revocable in minutes when the agent starts confidently fixing the wrong thing. (UC-36)

When to use

• Before enabling any "auto-heal" or agentic remediation featurethe scope document precedes the toggle, not the first incident.
• When the ticket queue shows agent-shaped noiseusers reporting "my machine restarted itself" or fixes that un-fix each other means autonomy is already running ungoverned.
• MSP contextsautonomous actions on client endpoints without a written, client-visible scope is a contractual incident waiting for a name.

When NOT to use

• Servers and privileged infrastructuredomain controllers, hypervisors, database servers stay out of autonomous remediation scope entirely; this playbook's tiers are for end-user endpoints.
• As a substitute for fixing root causesan agent silently restarting a crashing service every four hours is suppression, not remediation. If the action log shows the same fix repeating on the same asset, open a real ticket.
• Where no one will do the weekly log reviewungoverned autonomy is worse than none; if the review will not happen, keep the feature in recommend-only mode.

The recipe

1. 1.

   Write the scope document — one page, three lists. May touch autonomously: reversible, low-blast-radius actions (clear temp files on disk-full, restart a named list of user-mode services, reset a hung print spooler, re-register a broken agent). May propose only: driver updates, registry edits, reboots during work hours, anything touching security tooling. Never: credential or identity changes, disabling security agents, anything on the server exclusion list, actions on devices tagged exec or regulated. Name an owner and a review date.

2. 2.

   Map the scope into the tool's actual controls. In Atera-class agentic AI: configure which action categories run autonomously versus queue for approval. In Intune: Devices > Scripts and remediations — each remediation package gets an assignment scope matching your lists, and anything in "propose only" runs detection-only (detection script assigned, remediation script withheld) so you see the would-have-fixed count without the fixes. Verify with a test: trigger an out-of-scope condition and confirm the agent proposes rather than acts.

3. 3.

   Stand up the action log as a queryable artifact. Every autonomous action must land somewhere you can filter: the RMM's activity feed exported weekly, or remediation output collected centrally. Minimum fields: timestamp, device, trigger condition, action taken, result, user-visible? If the tool cannot answer "what did agents do to which machines last Tuesday" in one query, autonomy stays at tier zero.

4. 4.

   Run the weekly review with AI as the summarizer.

   Copy

   Here is this week's auto-remediation action log: [paste export].
   Produce:
   1. Action counts by type and by result (fixed / failed / recurred)
   2. REPEAT OFFENDERS: any device+action pair occurring 3+ times —
      these are suppressed root causes, list them for ticketing
   3. SCOPE CHECKS: any action that appears outside this allowed list
      [paste "may touch" list] — flag every instance, zero tolerance
   4. WRONG-FIX CANDIDATES: actions followed within 24h by a user
      ticket or another remediation on the same device
   5. One-paragraph summary for the ops channel
   Flag, don't conclude: every item you list, I will verify in the RMM.
   

   Verify flags in the console before acting — the reviewer pattern applies to the review itself.

5. 5.

   Catch the wrong-fix before it spreads. The signature of a bad remediation is success at scale: a fix suddenly "working" on hundreds of devices means the detection is over-matching. Set a volume alert — any single remediation exceeding its normal weekly count by 5x pages a human — and rehearse the kill switch: know the exact clicks to pause a remediation assignment or the agent's autonomous mode, and time it. If pausing takes more than five minutes or requires a vendor ticket, your real scope is smaller than your document says.

6. 6.

   Promote and demote autonomy on evidence. An action class earns promotion from propose-only to autonomous after four clean weekly reviews (no scope breaches, no wrong-fix candidates, repeat-offender rate falling). Any confirmed wrong-fix demotes its class back to propose-only for a month. Write both rules into the scope document so promotion is a process, not a mood.

Make it yours

• Atera / agentic-AI RMM shopthe vendor's autonomy is broad by default; invert it. Start with everything in approval mode, promote action classes one at a time using the four-week rule.
• Intune-only shopthe built-in remediation library plus custom detection/remediation script pairs gives you the same capability with more visible seams; the detection-only trick in step 2 is your propose-only tier, and remediation run reports feed the weekly review.
• MSP running multiple tenantsone scope document per client, signed; the weekly review runs per-tenant with a cross-tenant rollup, because a wrong-fix that spreads across clients is an incident and a churn event.

Quality check

• The scope document is findable and current. If the on-call engineer cannot locate what agents may touch, the answer during an incident becomes "nobody knows".
• Zero unexplained out-of-scope actions. One scope breach is a config bug to fix same-day; a tolerated one is a policy change nobody approved.
• Kill switch timed quarterly. Measured minutes-to-pause, by a person who is not the one who set it up.
• Repeat offenders become tickets. The log review's output includes root-cause tickets opened, or the review is observation theater.