The managed service category nobody's named. Yet.

There's a moment in every tech cycle when a product shifts from a novelty to a standard. The partners who recognize that moment early are typically the ones who end up seeing the best returns. When it comes to agentic AI, we're already at that stage, with a McKinsey report stating that 62% of organizations say they’re at least experimenting with agents, and 23% are deploying and scaling them.

Those who've been in the game long enough have seen this before. Think back to endpoint management. A decade ago, the partners who moved fast to own device management built durable, recurring revenue. In creating their own frameworks, service level agreements, and accountability models, they set the standard for what ‘managed’ really meant in their markets.

The interest in endpoint management operated along an S-curve, which saw a slow incubation period, followed by a rapid acceleration of interest. But those who waited found themselves playing catch-up in a category that had already been defined. This same dynamic is unfolding right now with agentic AI. The only difference is that the clock is running a lot faster.

Agent accountability

AI agents can now act as fully integrated team members. They handle day-to-day work: routing support tickets, assisting in the sales cycle through pipeline forecasting, CRM upkeep, etc., running IT operations, responding to cybersecurity breaches, and much more. However, it’s unclear who runs, governs, and holds accountability for these new workers.

Let’s say an IT support company deploys an AI agent that auto-routes support tickets from users to the relevant support department. There’s an expectation that the agent will receive the ticket, route the inquiry properly, and report back to the user without any guidance. If a human were handling these tickets, it would be obvious who to report to if something went wrong. The same isn’t immediately obvious with an autonomous agent, which creates an accountability gap.

This is where Managed Service Providers (MSPs) come in. They're well equipped to guide the partners and vendors they work with who may not be able to handle agent issues on their own, especially considering how new this technology is. Customers deploy the agents, but MSPs should govern them.

Adoption anxiety

There’s been a recent notable change in the questions being asked around AI agents. Six months ago, customers were asking whether they should be adopting AI agents at all, and what ROI they’d see from adopting them. Now, they’re asking whether agents are working properly, and who to call when they aren't.

While the early adopters have already been through this shift, the next wave of customers are only now reaching it. This is the point where interest turns into widespread adoption. Customers are not wearing tin-foil hats and don’t buy into the fears and hyperbole around AI. Most are AI-curious and open to reviewing and utilizing agents, but are naturally anxious about employing autonomous bots.

Data is another source of anxiety that MSPs need to address. Our AI at Work report found that 40% of business directors cited data privacy and security concerns as their top barrier to wider AI adoption. Unlike a chatbot that responds to a prompt, an agent can access systems, use data, and take action on a customer's behalf.

Organizations need to know what data an agent can access and where human oversight begins. That's where MSPs add value, giving customers the visibility and ongoing monitoring they need to adopt agents with confidence.

Governance and the ‘three waves’ of AI

The value of the partner relationship is riding out the waves of AI, then guiding customers through end-to-end implementation. Product knowledge and context are both key for MSPs to really understand a customer's needs. This means learning the business logic, risk tolerance, compliance requirements, and being able to apply that context to every tech decision, from onboarding agents to having them work autonomously alongside employees.

AI agent governance is a natural extension of that process. An MSP who already knows a customer's workflows, data sensitivities, and operational boundaries is positioned to ask the right questions before deploying agents. They can then course-correct if an agent's behavior starts drifting.

That last part matters more than most people realize because agents don't exist in a static environment. Processes, regulations, and business priorities are changing in real-time. The same is true for AI adoption, which unfolds in three waves: first, employees using accessible LLMs like ChatGPT at work; second, vibe coding and structured agents that handle specific, simple tasks; and finally, autonomous agents that understand their tasks and self-improve. Navigating these stages is complex, which is where trusted partners come in to ease the burden.

Early management

MSPs should start by separating upfront work from ongoing management. The initial consulting and deployment phase, starting with understanding a customer's use cases, defining their success criteria, mapping risks, configuring the agent, and going live, is a one-time professional service.

Ongoing management is where long-term value lies. This includes monitoring agent performance, reviewing outputs for errors, and maintaining an audit trail that keeps the compliance team happy. Additionally, different-sized businesses will have different requirements, and where major providers may have dedicated IT support teams, smaller businesses may not have access to the same support structures. MSPs must individualize their plans for their customers, depending on their needs.

This ‘agent as managed service’ model maps naturally onto how MSPs already structure their work with customers. In addition to selling devices, MSPs ensure agents are secure, updated, and performing well over time. MSPs ensure that agents are doing their job correctly and remain adaptable. Without continued management, one-time investments into AI agents are unlikely to return real ROI for customers.

Acceleration has started

MSPs that saw the best returns from managing endpoints weren't slow to realize the value, nor did they treat endpoint management as a one-time service. Instead, they created a whole new managed service model. That's the opportunity available to the channel right now with AI agent oversight.

With the majority of businesses now arriving at agent adoption, this may be the last chance for MSPs to set the standard for an agent management service model. While there is no name for this service model, by managing customer anxieties around agent adoption, offering ongoing support for agents, and adapting services in line with customer needs, forward-looking MSPs will write the rules for agent governance across the channel.