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How Tickets Begin with Fae

AI IN ITGUIDEOPENFRAMEPSATICKETING

Phase 6 — Tickets & PSA Workflow · Step 1

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July 22, 2026

Published

Vladislav Marchenko

Vladislav Marchenko

Head Of Marketing

How Tickets Begin with Fae

Phase 6 — Tickets & PSA Workflow · OpenFrame Onboarding

Most tickets in OpenFrame don't start with a technician — they start with Fae, your clients' AI IT assistant. Before a ticket ever lands on your board, Fae has usually already talked to the client, pulled the device's real health data, and tried the safe fixes. This guide explains what happens on the client side so you understand exactly what a ticket has been through by the time it reaches you.

Where Fae comes from

Fae is the client-facing counterpart to Mingo (your technician-facing AI). The moment the OpenFrame agent is deployed to a machine, the Fae chat appears on the client's screen — it's their client portal, branded as "Your IT is managed by [Your MSP]." From then on, Fae is their first line of support: they're meant to ask Fae before anything else.

You configure Fae's branding and behavior under Settings → AI Settings & Guardrails → Customer AI Assistant (its name, avatar, and the "Get Started" landing), while what Fae is allowed to do on a device is governed by the Guardrails tab (see AI Guardrails & Approval Policies, Phase 9).

What the client sees

The portal is deliberately simple — no forms, no category trees to navigate:

  • A prompt: "Hey! How can I help? Describe what's happening and I'll take a look."
  • An "Enter your request here…" box where the client types the problem in plain language.
  • Quick-start chips that rotate through common issue types — Hardware Repairs & Swaps, VPN & Wi-Fi Issues, Password, Printer & Peripheral Fixes, Calendar & Delegation Setup, "How do I…" questions, and more — to nudge them if they're not sure how to start.
  • "Your Chats" — a list of their past conversations. Each one is a ticket, shown with its number and current status (e.g. AI Assistance).

What Fae actually does

This is where Fae earns its keep. When a client describes a problem, Fae doesn't just chat — it works the issue against the device's live data:

  1. It reads the machine. Fae correlates the complaint with real telemetry — CPU, memory and swap pressure, uptime, running processes. (A "my computer is slow" turns into "Cap.app was eating 64% CPU, memory is nearly exhausted with heavy swap activity, and you have 29 days of uptime.")
  2. It proposes and runs safe fixes. Fae lays out the highest-impact, lowest-risk actions and executes them, showing each command transparently and checking it off as it completes. What it can run on its own — versus what pauses for approval — is exactly what your AI Guardrails decide (Phase 9).
  3. It reports back in plain language. After each step Fae tells the client what changed ("Cap.app is closed. Memory is still very tight…") and continues if there's more it can safely try.

Many issues are fully resolved right here, in the AI Assistance stage, without a technician ever touching them.

How a ticket reaches your board

There are two ways a Fae conversation turns into work on your side:

  1. Fae escalates it. When Fae hits the limit of what it can safely resolve — or an action needs a human — it hands off. The client sees "Waiting for Technician Response," and the ticket moves into your board at the Tech Required stage.
  2. The client escalates it themselves. At the top of the portal there's a Create Ticket button. If a client would rather go straight to a person, they can open a ticket directly without waiting for Fae to give up.

Either way, what arrives on your Tickets board isn't a blank complaint — it's a ticket carrying the full Fae conversation, the device context Fae gathered, and a record of everything it already tried. You pick up with Mingo on the Technician Chat side (see Using Mingo AI in a Ticket Chat).

Why this matters for your techs

Fae changes the shape of your queue. By the time a ticket reaches a technician, the easy stuff is often already handled, and the hard stuff arrives pre-triaged with context — you're not starting from "it's slow," you're starting from "Cap.app was the culprit, memory's still tight, here's what's been tried." That's less time gathering basics and more time on the actual fix.

The lever you control is the Guardrails: tighten them and Fae asks before acting (more approval cards for you); loosen them and Fae resolves more on its own (fewer tickets reach you). Tune that deliberately in AI Guardrails & Approval Policies (Phase 9).

Quick checklist

  • Understood that Fae appears on the client's device once the agent is deployed and is their portal
  • Knew clients are meant to ask Fae first, describing the problem in plain language
  • Saw that Fae reads device telemetry and runs safe fixes in the AI Assistance stage
  • Knew the two routes to a ticket: Fae escalates ("Waiting for Technician Response") or the client clicks Create Ticket
  • Understood a ticket arrives pre-triaged, with the Fae conversation and device context attached
  • Knew Guardrails (Phase 9) control how much Fae does on its own

What's next

Now that you know where tickets come from, see how they move across your board in Ticket Lifecycle — From Open to Resolved, and how you work them with your AI assistant in Using Mingo AI in a Ticket Chat.

Based on OpenFrame v0.9.19. Fae's portal and behavior are actively evolving between releases — what's in your console (and your Customer AI Assistant settings) wins.

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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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.
ConnectWise AI is the umbrella for ConnectWise's AI features on the Asio platform: the Sidekick GenAI assistant and ConnectWise RPA. It handles ticket triage, sentiment analysis, AI-assisted scripting, and natural-language automation across PSA and RMM, delivered through the ConnectWise Pro package.

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.

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.
AI for MSPs means using machine learning and large language models to augment technicians on repetitive work like ticket triage, scripting, monitoring, and threat detection. It accelerates the grind while a human stays in the loop and owns the final decision.

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.

Open Source Helpdesk

Zammad works well as an MSP helpdesk for teams that route tickets across multiple channels. It handles ticketing, automation, and a knowledge base, but it is not a PSA, so billing, contracts, and asset management still live elsewhere.