OpenFrame

Using Mingo AI in a Ticket Chat

AI IN ITAI INTEGRATIONBEST PRACTICESOPENFRAMETICKETING

Phase 6 — Tickets & PSA Workflow · Step 4

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June 23, 2026

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Vladislav Marchenko

Vladislav Marchenko

Head Of Marketing

Using Mingo AI in a Ticket Chat

Phase 6 — Tickets & PSA Workflow · OpenFrame Onboarding

Every OpenFrame ticket has AI built in — and it shows up in two places. Fae handles the client side; Mingo is your assistant on the technician side. This guide is about working with Mingo (and how it sits next to Fae) so the AI actually saves you time instead of getting in the way.

Two assistants, two jobs

Open a ticket and you'll see two chat panels side by side:

  • Client Chat — Fae. The client-facing assistant. It talks with the end user in plain language, gathers symptoms, and proposes fixes. You can Start Direct Chat to take over and message the client yourself at any point.
  • Technician Chat — Mingo. Your technical assistant. You type into "Enter your Request…" and Mingo helps you investigate and act — check the device, pull the relevant facts, draft a fix. Mingo also lives in its own Mingo section in the left nav for fleet-wide work outside any single ticket.

Same idea, different audience: Fae speaks to clients, Mingo works with techs.

What Mingo can do

In the Technician Chat, Mingo is grounded in the ticket's linked device and your fleet, so you can ask it to actually do technical work, not just chat:

  • Investigate — "What's the disk usage on this machine?" "Is the Fleet agent healthy?" "Which Windows updates are pending?"
  • Explain — summarize what's wrong, interpret a log, lay out options.
  • Act — propose and (with your approval) run a command or script on the device to apply a fix.

Because Mingo is tied to the device on the ticket, "this machine" just works — you don't have to tell it which computer you mean.

How to work with it

  1. Open the ticket and read the Client Chat to see what Fae has already established with the user.
  2. In Technician Chat, ask Mingo a specific question or task. Specific beats vague — "check pending Windows updates on this device" gets you further than "what's wrong."
  3. Review what it comes back with. When Mingo proposes an action that touches the machine, it won't run silently — it asks for your approval first (see Approval Workflows — When Mingo Asks Permission).
  4. Step into the Client Chat with Start Direct Chat when the human touch is needed — a reassurance, a question Fae can't answer, a heads-up.

What it can't (and shouldn't) do on its own

  • It won't change a machine without sign-off. Any device-touching action is gated behind Approve / Reject — by design.
  • It's an assistant, not an autopilot. Mingo is great at gathering facts and drafting fixes; you're still the one who decides and confirms.
  • It works best with context. A ticket with no linked device gives Mingo little to stand on — link the device (see Create a Ticket Manually).

Quick checklist

  • Know which lane is which: Client Chat = Fae, Technician Chat = Mingo
  • Asked Mingo a specific investigative question on a device-linked ticket
  • Used Start Direct Chat to message the client directly when needed
  • Let Mingo propose actions, and reviewed them before approving
  • Remembered Mingo also lives in its own Mingo section for fleet-wide tasks

What's next

The one rule that keeps all of this safe is the approval step. Approval Workflows — When Mingo Asks Permission covers exactly what triggers it, what you're approving, and how to approve or reject.

Based on OpenFrame v0.9.19. The AI assistants are actively evolving — capabilities expand between releases, so check the console for what's available in your tenant.

Vladislav Marchenko

Vladislav Marchenko

Head Of Marketing

Hi all! My name is Vlad and I’ve been brought on to head the marketing team at Flamingo. Thankfully, this isn’t the first time I will be building a marketing department from scratch, so the experience should come in handy. Now it’s time to dive into the world of MSPs and find myself in this new world.

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Frequently Asked Questions

MSP AI Agents

Yes. In production MSP shops today, 10% to 25% of tickets close before a human opens them. Thread alone has processed 173 million tickets across 750-plus MSP partners at 96% triage accuracy, handing back 490,000-plus technician hours. Agents own the low-risk, high-volume work (password resets, MFA enrollment, known installs, onboarding and offboarding) and flag anything that touches production data or needs judgment for a human to take.
On a five-person desk, reported deployments show $78,000 to $130,000 in annual direct labor savings, roughly 30% fewer escalations, and 15% to 20% better SLA compliance. Broader MSP adoption data adds ticket handling time cut by 45% and five to 12 points of margin, all from reclaimed capacity rather than headcount cuts.

AI MSP

MSPs use AI to triage and route tickets, cut alert noise, schedule patches, assist L1 security work, and draft client reports. Kaseya's 2025 benchmark found 30% already use it to eliminate tedious tasks, with ticket triage the most common starting point.
Most MSPs start with AI features inside their existing PSA, RMM, and ticketing systems rather than standalone products. Common categories include AI ticket triage, alert correlation, scripting assistants, and AI-native all-in-one platforms like OpenFrame that run intelligence across the whole stack.
Start with a readiness assessment, not a tool purchase. Confirm your ticket history is clean and your RMM, PSA, and monitoring systems connect. Then pick one high-volume, low-risk workflow, usually ticket triage, and pilot it on internal tickets before any client sees it.
Automate high-volume, low-risk tasks first. Ticket triage and alert noise reduction top the list because they run constantly and a human still resolves the underlying issue. Save security approvals, billing changes, and client-facing actions for later, always with a human in the loop.

AI Safety

It can be, with governance. Keep a human in the loop on high-risk actions, log every automated step for audit, and choose platforms that keep your data yours with no vendor lock-in. Pilot on internal data first so you catch issues before client systems are involved.

AI for MSPs

Set a baseline before rollout, then track tickets closed per technician, mean time to resolution, percentage of tickets resolved with no human touch, technician hours reclaimed, and cost per ticket. AI-driven automation commonly cuts operational cost per ticket by 25 to 40%.
No. AI automates routine tickets, patching, and monitoring, but trust, accountability, and complex business judgment still need people. The future of managed services moves technicians from closing tickets to advising clients, which makes the human role more valuable, not obsolete.

About OpenFrame

OpenFrame isn't built to plug into your stack. It replaces it. Instead of duct-taping a dozen tools together (RMM, MDM, SIEM, patching, remote access, each its own login and bill), we bundle it into one unified platform: RMM, MDM, monitoring, automation, remote access, patch management, security monitoring, and ticketing, plus built-in AI copilots. So "does it integrate with X?" usually means: you won't need X anymore.