From alert to closed ticket.Here is exactly what happens.
Five stages. One loop. Every ticket the AI engineer closes runs through all of them.
Alert arrives
Something needs attention.
An alert fires from your RMM, a ticket lands in your PSA, a user reports an issue, or the AI engineer itself notices something abnormal on an endpoint. It learns what normal looks like in your environment and flags when something drifts.
- Bidirectional sync with ConnectWise, Autotask, HaloPSA, ServiceNow, Zendesk, Freshdesk, Freshservice, Jira SM
- Webhook ingestion from monitoring and alerting tools
- Proactive detection: the AI engineer baselines your environment and catches drift before users notice
- User reported issues via chat
AI engineer investigates
Real commands on real systems.
The AI engineer connects to the endpoint and runs diagnostic commands against the actual system. It checks service states, reads event logs, inspects disk and memory, tests connectivity. When relevant, it cross-references data from your ticketing system, monitoring tools, and cloud identity to build a complete picture. The investigation adapts based on findings and goes multiple rounds deep, up to a configured iteration limit.
- Lightweight agent on every endpoint across Windows, macOS, Linux, and FreeBSD. Full requirements
- Runs PowerShell on Windows, bash on macOS/Linux
- Cross-environment investigation: endpoint, PSA, monitoring, cloud identity
- Adapts the investigation based on what it finds, not a fixed script
- Goes multiple rounds deep until it reaches a root cause or exhausts its iteration budget. If the budget runs out without confidence, the ticket escalates to a human with the full investigation attached
AI engineer decides
Root cause, confidence, risk.
The AI engineer synthesizes what it found into a root cause with a confidence level and a risk classification. High confidence, low risk actions can proceed automatically. Anything uncertain or risky gets flagged.
- Root cause identification from diagnostic evidence
- Confidence scoring based on evidence quality
- Risk classification per action (not per ticket)
- Maps findings to specific remediation actions from the resolution playbook
Acts or asks
Governed authority, not blind execution.
Simple fixes execute directly. Complex remediation orchestrates multi-step workflows across systems, including custom procedures your team defines. If the action is risky or your policies require a human sign-off, the AI engineer sends an approval request. One click to approve or deny.
- Approval policies define which actions need sign-off and from whom
- Low risk actions execute automatically (you configure the threshold)
- Approval strategies: single approver, multi-approver consensus, or role-based
- Multi-step workflow orchestration for complex, cross-system remediation
- Custom remediation procedures your team defines and the AI engineer executes
- Every action, whether auto-approved or human-approved, is logged
Verifies and closes
Prove it worked. Or hand off with everything attached.
After executing the remediation, the AI engineer runs a post-action check to confirm the fix actually worked. If verification passes, the ticket closes with the full audit trail. If the fix did not work, or the issue is beyond the AI engineer's scope, it escalates to your team with the full investigation attached: what it checked, what it found, what it tried, and what it recommends next.
- Post-remediation diagnostic re-run to confirm the fix
- Ticket updated with full investigation and resolution notes
- Audit trail: every command, every finding, every approval decision
- Escalated tickets include all diagnostic data, root cause hypothesis, and recommended next steps
When the AI engineer can't close it.
Not every ticket auto-resolves. When the iteration budget runs out or no safe single-step remediation exists, the ticket escalates with the full investigation attached. Your tech starts from a lead, not an empty ticket.
The escalation hands off everything: every command, every output, the leading hypothesis, the iteration count, and the suggested next step.
What you control. What the AI engineer handles.
You set up
- •Which ticketing system to connect
- •Which endpoints get the agent
- •Approval policies: what runs automatically, what needs sign-off, who approves
- •Issue classes and escalation rules
The AI engineer handles
- •What commands to run on each endpoint
- •How to interpret the diagnostic results
- •Whether it has enough confidence to act or needs to escalate
- •Verifying the fix worked and documenting what happened
Common questions
How long does it take to set up?
Connecting a PSA takes about 15 minutes (OAuth or API token plus webhook handshake). Agent deployment depends on fleet size and method: 50 to 100 endpoints via Intune or RMM script push completes in an hour or two; thousands of endpoints via group policy or a staged RMM rollout typically takes one to two business days. First autonomous resolution can run within an hour of the first agent reporting in. Plan a half-day with one IT lead for the connector and the first 100 endpoints, plus a follow-up rollout window for the rest of the fleet.
Does it replace my existing tools?
No. Runs alongside your existing PSA, RMM, and monitoring stack. Reads tickets, runs diagnostics on endpoints, writes results back.
What types of issues can it handle?
Printer, Outlook, Windows Update, VPN, disk, slow performance, services, browser, network, software installs, file permissions, and more. Each issue class ships with a vetted catalogue of diagnostic and remediation actions, and the AI engineer chooses which actions to run, in what order, based on what the endpoint actually returns. The catalogue keeps the available actions safe and predictable; the AI handles the case-by-case investigation.
Does a technician need to approve every action?
Approval policies define what runs autonomously vs. requires sign-off. Low-risk actions (clearing temp files, restarting a print spooler) auto-execute. Higher-risk actions route to designated approvers. Configure per action type, risk level, client: single sign-off, multi-approver consensus, or specific roles.
What happens with issues the AI engineer cannot resolve?
Escalates with full investigation attached: diagnostics run, output, root cause hypothesis, remediation attempted. The technician picks up a lead.
Watch the loop run.
Pick a ticket from your queue and we'll run it end to end on a live endpoint.
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