AI is progressing so fast it can make your head spin. Industry thought leader, Robert Cioffi even suggested we submit “AI” to the BANISHED WORD LIST for 2023, especially when it comes to vendors marketing to MSPs. I’m not sure I disagree. But like many of you, I have spent a considerable amount of time this year trying to understand AI and its practical application in the managed services industry. There are the obvious things like suggesting ticket resolutions or even implementing fixes to tickets automatically. I just kept thinking there has to be more..
In reading Matt Schilcht’s news letter The Complete Beginners Guide To Autonomous Agents, it finally clicked for me. What if AI was “smart” enough that you simply gave it an objective and let it figure out how to accomplish it? Cue dramatic music… Well hello Autonomous Agents!
There have been some early adopters to traditional AI, particularly around security threat monitoring/response, network traffic management as well as workplace device management, but traditional AI systems typically require specific inputs and instructions to perform tasks, are limited in their adaptability and often require human intervention (pesky humans).
You may already be an expert on this topic, but just in case you missed that “Ah Ha!” moment like had recently, let’s break it down to a few simple common use cases we can all relate to.
First, what are autonomous agents and how are they different from what we see in the market today? Autonomous agents are AI-powered programs that can create, complete, and reprioritize tasks autonomously to achieve a given objective. Yes, read that again. Unlike traditional AI systems that require us to give specific inputs and instructions to perform a task, autonomous agents can independently analyze a given objective, devise a strategy, and execute the necessary steps to accomplish it, making adjustments along the way. Wow! Its ability to think, plan, and adapt makes autonomous agents a potentially powerful tool for MSPs. Take these 5 common MSP functions:
- In network monitoring and performance optimization they can be proactively identifying issues and implementing solutions automatically. They can learn and adapt and not rely on specific triggers like traditional AI systems.
- With security management and threat detection agents can be analyzing security logs and identifying potential threats in real-time without relying on constant manual analysis. I can hear the criticisms of this already, but for the sake of my utopia, let’s roll with it please. I don’t think automating threat remediation and zero trust have to be mutually exclusive.
- In the backup and disaster recovery arena, an autonomous agent might learn and adapt by analyzing historical data, such as previous system failures, backup performance, and recovery times. With this information, the agent can identify patterns, optimize the backup schedules, and even prioritize critical data for recovery. Here is where it gets cool for all you DR nerds like me. As new data emerges or your client’s infrastructure evolves, it can adjust its strategies, ensuring optimal backup and recovery performance tailored to the specific needs of your client’s environment.
- Software updates and patch management are typically based on a predetermined set of rules or schedule, without the ability to adapt to changing circumstances or prioritize updates based on urgency. Instead, autonomous agents could continuously monitor the software environment, intelligently assess the importance and potential impact of updates, and apply patches accordingly. Again, I can hear the angry screams of security experts from here (sorry Susan Bradley) but, when done right, might this adaptive approach address critical security vulnerabilities more efficiently by reacting to new threats or system changes in real-time?
- When it comes to IT asset management and inventory tracking (a place where we are always looking for improvement) we no longer need to rely on predefined and manually updated rules and static algorithms to discover and track assets. Instead your AI can learn from the data and patterns it observes and automatically adapt its discovery and tracking strategies. With continuous learning and adaptation you should be able to develop better assessments and proactively identify opportunities to optimize or upgrade assets. Kind of like a quarterly asset review, but in real time, constantly.
None of the concepts I have discussed are new or original, but it seems to me that they might now be much more achievable thanks to the next wave of AI. As IT management becomes increasingly complex, MSPs rely on vendors more than ever to provide solutions that not only make processes more efficient, but also safer. Autonomous agents present a massive opportunity for vendors to deliver disruptive technology and profit as a result. Obviously issues of trust and security must be addressed as we move forward in leveraging this powerful technology but I am excited to finally see the potential practicality of using AI in the MSP space with autonomous agents.