It's Saturday night. You have a QBR with an important client at 10 a.m. Monday. You know the pack isn't done, and you know it'll take you two hours to pull the numbers from Autotask, another 30 minutes to check devices in Datto RMM, another 20 to run down the MFA coverage in CIPP, and another 15 to see what's changed in Auvik since the last review. Then an hour to actually make it look like a story instead of a data dump ...

If it sounds exhausting, it is. But it's also a necessary part of customer support and retention. Since it has to be done, let's look at the actual problem and how Conduit can make it easier.

The Problem Isn't Any One System

The QBR prep problem isn't that any one tool is hard to pull data from. Most MSPs can generate a ticket-volume report from Autotask in under 5 minutes. The problem is that a QBR pack has to pull from four tools that don't talk to each other, and the account manager (often the MSP owner) is the human middleware between them.

The pack itself needs:

• Ticket history from your PSA
• Device health from your RMM
• Identity and MFA coverage from your M365 admin tools
• Configuration changes from your network and change monitoring

That’s four systems having one conversation, with you as the connector.

What "Automated" QBR Reports Actually Mean

When we say Conduit can automate your QBRs, we don't mean a template or a rerun of a report you built in 2023.

An automated QBR pack in Conduit is a scheduled agent that reads from all four systems in real time, reasons about what matters, and assembles the pack. It writes the pack to your documentation platform as a living document, with the same URL every time. And since it’s updated in place, history accumulates naturally. Then it posts a one-line summary to Slack when it's done.

You don't have to tell it what needs to be done. It's just ready.

What the Pack Might Look Like

In this example, the pack that lands in IT Glue every Monday morning for one designated client has four sections:

1. Ticket summary. Volume, priorities, and what's still open, pulled from Autotask.
2. Device health. Count, offline devices, patch status, and reboots pending, pulled from Datto RMM.
3. Identity coverage. User count and MFA adoption, pulled from CIPP.
4. Configuration changes. What's changed in the client's environment over the last 90 days, pulled from Auvik.

Plus an executive summary paragraph at the top, so the person delivering the QBR knows what the story is before they even open the doc.

If any one system fails to respond, the pack still comes through, with the failed-system section marked "Data not available." No missing sections, and no quiet failure.

How to Set Up QBR Automation

Setting up an automated QBR agent takes about 20 minutes the first time. You designate a client, connect the vendor tools you already use, and paste a build prompt into your AI agent of choice. The AI agent discovers the client's IDs across your systems, sets up the scheduled routine, and confirms everything's wired up.

The full walkthrough (with the actual prompts) lives in the Conduit docs. It's a worked example, not a theoretical one, so you can build the exact agent described in this post.

Get Your Weekend Back

QBR prep used to be a chore (to put it mildly). With Conduit, it becomes a document you review. You get your weekend back, your team gets a repeatable process, and your clients get a picture built on fresh data. Reporting like this is also a natural first piece of managed AI for your clients.

If you'd like to talk about what automated client reporting could look like for your shop, let's talk.