How close are we to a truly autonomous RMM?

The AI space is constantly reinventing itself and evolving at speeds we’ve never seen before in tech. After seeing AI fundamentally change how people work across thousands of industries, it naturally begs the question: what does the peak of AI in RMM look like? 

Right now, with the advent of Agentic AI, the answer is the Autonomous RMM. 

On paper, there’s a ton of potential for autonomous RMMs to completely change the IT management game. But in reality, can it bring value or is it just vapor? How much can we trust it to run operations on its own without the stress of it breaking something? 

But before we get into the potential impact and implications of autonomous RMMs—let’s talk about the fundamentals.

What is an autonomous RMM?

An Autonomous RMM is a fully agentic system that is designed to go beyond spotting issues and proactively act based on the issue it identifies. 

You can think of an Autonomous RMM as a technician operating an RMM tool to run your operations, like deploying patches, diagnosing issues, and proactively remediating issues to keep escalations at bay.

How would an autonomous RMM work?

The autonomous RMM would be powered by Agentic AI, operating with a combination of AI agents that would own a specific pillar of operations from end to end. These agents actively explore and analyze the context available to take proactive action in real time. This means that your RMM’s constantly analyzing real-time data, using it to predict issues, and taking action such as rebooting, isolating endpoints, or tweaking configurations, before these issues get a chance to disrupt.

Let’s take an AI patching agent as an example to understand what this autonomy looks like in a specific pillar of operations. Its modus operandi would be something like this: 

• It’d start by pulling and analyzing information from patch sources; this could be from OS manufacturers such as Microsoft, Apple, etc., or software entities for patching third-party software.

• Based on its analysis of the Patch KB, it would assess the scope and impact of the update, evaluating whether it addresses critical vulnerabilities, performance issues, or minor cosmetic bugs. 

• It would then assign an appropriate patch severity level and analyze it in conjunction with the patch type to determine if this patch should be approved and deployed immediately or put on hold. 

• Based on this analysis, the AI patching agent approves the patches that are good to go and immediately deploys it to all impacted endpoints, after which it would routinely attempt to re-deploy patches to ensure a perfect patch deployment score. 

The value proposition here is clear as day: your users’ devices are patched and secure from online threats and system crashes, without you having to move a muscle. 

It sounds too good to be true, so what’s the catch? 

Challenges with autonomous RMMs

It’s an upcoming concept that brings raw potential to completely redefine the way IT has worked for decades. But with this potential comes real concerns.

The biggest concern is trust. 
Can I trust an AI agent enough to let it completely own critical operations? Can I trust how it goes about fixing issues, not having to worry about bigger problems down the line? For an autonomous system designed to deal with routine tasks, how would it react to unexpected situations and edge case scenarios?

These are valid questions to ask. In a high-stakes domain that relies heavily on human oversight to keep things running smoothly, handing the reins over to an AI agent to take independent decisions is a big call to make. 

This trust issue cascades down to data responsibility and sovereignty as well. How much transparency do I have in the way these AI agents handle and process your data? Can I clearly observe and explain the way these agents resolve issues to a client if a client has questions? 

An AI agent’s output is only as good as the data it has access to. But enabling it with full visibility for peak performance can’t come at the expense of putting sensitive data at risk in the process.

Should you consider an Autonomous RMM?

It’s not a matter of whether you should move to an Autonomous RMM, it’s simply a matter of when you should make the switch.

The agentic segment in RMM will keep evolving; they’ll get more accurate in analysis, operate with transparency, and offer meaningful fixes that won’t require a second check, and eventually become the new normal for running IT. 

All things considered, the benefits outweigh the risks, and early movers here have a unique chance to set themselves apart from competition by running operations. 

My advice? 

Start cautiously. It’s okay to take half-measures here; start experimenting with semi-autonomous systems that can own execution completely but leave the decision-making to you. Have guardrails in place to make sure that you have complete control over system stability and data safety. Keep testing until you’re confident of running specific operations on autopilot with minimal intervention and keep expanding. 

It’s still early days, but the trajectory is clear and the pace is picking up—and a fully autonomous RMM might be much closer than you think. And the sooner you get on board, the faster you’ll be ready to embrace the future of IT operations.