“Agentic AI”: Hype vs. Reality for MSPs

 Let’s talk honestly about “agentic AI.” No doubt you’ve seen vendors pitching it as the next big thing for managed services and IT consulting. As always, the concept has a solid technical backbone—but the marketing narrative is another animal entirely.

That means it’s on us to decode the buzzwords, sort out what matters to our businesses, and steer clear of distractions.

Before We Go Further: Two Meanings of “Agentic AI”

Here’s a key distinction that’s getting missed:

- Agentic AI (marketing version): The promise of AI systems acting independently, making decisions, and running environments on autopilot—sometimes with less oversight than reality can support.

- Agent-based automation (actual tech): Software agents that operate semi-independently within carefully crafted guardrails, reliably handling tasks like patching, identity changes, and security responses. The architecture is proven; it just doesn’t run free of ownership and review.

My concern isn’t with agent architectures—they’re part of every MSP’s toolkit. It’s the vendor story of frictionless autonomy that leads us astray, downplaying how risk, responsibility, and oversight actually work in real service environments.

Why Accountability Rules—Now and Always

Here’s the non-negotiable: In managed services, someone’s always legally and contractually responsible for the result, whether a person or an automated agent made the change.

If a system triggers downtime, data loss, or a compliance blunder, the client won’t accept "the agent did it" as the answer. They'll pull up the contract, the SLA, and maybe even their insurance—because what matters isn’t philosophy, it’s economics.

This core accountability isn’t optional. It’s the backbone of our business model.

Automating With Agents—The Real MSP Example

Let’s anchor this with something MSPs live every day: patch management.

You set up a patching agent to scan, download, and deploy updates across client endpoints. The vendor’s glossy pitch might suggest it “handles updates for you.” But in practice, you:

- Set strict approval workflows (no patch goes live without your review)

- Track all actions in an audit log

- Roll back if something conflicts or fails

- Take ownership for outcomes in client-facing reporting

Automation speeds up the routine, but it doesn’t shift responsibility. You, not the agent, are named in the contract—and you’re who the client will call if something breaks.

That’s the pattern again and again, whether with identity changes, automated ticket responses, or security rules. Agents are powerful. They’re not autonomous in any risk-free sense.

Where Vendor Narratives Create Confusion

The hype version of “agentic AI” imagines fleets of self-directing systems making high-stakes choices without human check-ins. But in real managed services:

- Workflows require business context and review

- Systems need clear, documented guardrails

- Compliance is audit-driven

- Risk management is built on accountability

What we get from technical advances (LLMs, orchestration, agent architectures) is better task decomposition, smarter interfaces, and robust automation—but we don’t get out of the loop.

In short: The marketing label is ahead of the architectural reality.

On “Citizen Development”—And Why Governance Still Matters

You’ll also hear the claim that natural language AI will make everyone a kind of software developer (the so-called “citizen developer” revolution). In MSP terms, this sounds like clients being able to “write” their own automation inside their business systems with just a few prompts.

That’s interesting—but here’s what’s missing: It’s not just skill levels that matter, it’s governance and risk. Whether your technician, admin, or client is using an agent-powered tool, someone has to:

- Establish rules and boundaries for changes

- Validate outputs before they go live

- Track accountability for actions taken

AI can simplify building processes—but it can’t remove the need for structured thinking, domain knowledge, and clarity about who’s responsible when things go wrong.

If You’re an MSP: What Matters Most Right Now

Where should you focus?

1. Leverage agent-style automation for repeatable, auditable tasks. Patch approvals, overnight reporting, security hygiene—these are real wins.

2. Build governance into every deployment. Keep approval flows, audit trails, and risk frameworks in place from the start.

3. Don’t sell autonomy—sell reliability and accountability. Your clients are trusting you, not your bots, to deliver business outcomes.

4. Educate clients about their process, not just their tech. Help them understand what makes sense to automate.

5. Stay in the loop. Oversight, review, and judgment are why you exist.

Automation is the engine, but you’re always the driver.

Summing Up: Agentic AI Is Useful—But Frictionless Autonomy Is a Myth

Here’s the upshot:

Agent architectures and automation are here to stay, and they’re great tools for any MSP. But the hype around “autonomous AI agents” doesn’t align with the reality of economic liability, SLAs, or how clients expect risk to be managed.

The opportunities are real. The responsibility is, too.

When something goes wrong, you won’t point to the agent—you’ll answer for the fix. That’s how the managed services model works, and it’s not going anywhere.