Many organizations we talk to are navigating the same paradox right now. There’s real pressure to adopt AI fast and capture the efficiency everyone keeps promising. At the same time, there’s real fear about what happens if it goes too far, and the same organizations pushing adoption are the ones quietly holding it back.
That contradiction doesn’t stay contained at the leadership level. It shows up as mixed signals for the people actually doing the work: executives say move fast, IT says be careful, and finance asks what this is costing. Caught in the middle, employees don’t stop using AI. They just stop asking permission. The tools are sitting right there in a browser tab, so people use them anyway, quietly, in ways that are exactly the kind of unsafe an organization was trying to avoid.
The fix is uncomfortable but simple: Stop trying to prevent every AI risk and pay someone to find it on purpose.
Make It Somebody’s Job to Do the Scary Part
Most companies are still operating on a build-it-and-they-will-come instinct: roll the tool out, see what happens, and hope it goes well. Agentic AI breaks that instinct, because “what happens” now includes an agent taking real action: closing tickets, touching customer data, making calls without a person’s sign-off.
The organizations getting this right share one thing in common: Somebody’s actual job is to see how far these agents can really run the business, and just as deliberately, to find where they break down. That’s not a side project or whatever role has spare bandwidth this week. It’s a real, named responsibility, with enough business context to know what “broken” actually looks like for that company, not just for the tool.
That person is answering three of the most important questions before an agent gets anywhere near production:
- Does it actually save time?
- Does it save money once you count what it takes to babysit it?
- Can the team be trained on it quickly enough for that upside to be real?
What That Looks Like in Practice
At Blacktip, we plan how we’ll use agents before we turn anything on, whether that’s a Copilot agent or a tool that ships with its own. The bar is the same across the board. It has to be cost-effective and move us forward.
Emma owns the go-test-it-and-break-it role at Blacktip, working alongside the team rather than off to the side. A service desk handling 30 to 40 tickets a day generates plenty of reps to test against, and we give a tool two to three weeks before making a call.
Testing goes awry sometimes, and you look up to find you’re spending more time training the tool than it’s saving. What “not working” looks like is rarely dramatic. It’s a few minutes per ticket spent correcting the agent, re-checking its work, or feeding it context it should have had. That adds up quietly. On a queue that size it runs to roughly an hour a day, four to five hours a week going into training a tool that was supposed to give that time back.
Then the results go to our CEO, and we assess whether the tool is worth keeping. It’s a real comparison: time spent doing the work manually, time spent training the tool, and time the tool actually saved. We always submit feedback to the vendor. If the tool changes, we keep it. If we don’t see noticeable improvement, we stop using it.
Testing Isn't the Same as Governing
It’s easy to stop too early here. An agent gets tested, it looks promising, and the instinct is to declare victory and move on. But testing tells you whether something works once. Governing tells you whether it keeps working the fiftieth time safely, and when it hits an input nobody planned for. The same person doing the breaking needs a clear answer to a second set of questions before anything scales: What is this agent allowed to touch? What happens when it’s wrong? Who finds out, and how fast?
This kind of structured testing becomes a foundational part of agentic AI governance. This will help organizations to understand not only what works, but what can be trusted at scale.
The Title Doesn’t Matter. The Job Description Does.
There’s a role emerging within companies doing this well that doesn’t yet have a settled name. Some call it a forward-deployed engineer. It doesn’t matter what it’s called. What matters is the job description: technical enough to actually understand how these agents work, embedded enough in the real workflow to know whether one is actually helping or just looking helpful, and current enough to keep up as the technology itself keeps moving.
That last part is easy to miss. The job doesn’t stop at understanding how agents work today. It’s staying ahead of where the technology is heading and using that to guide the business, IT included. It means focusing on the use cases actually worth pursuing for that specific company, not the generic list every vendor is pitching.
That combination is rare on purpose. Most technical hires are optimized for building the system. This role is optimized for living inside how the business actually runs and translating between the two. It also means catching the gap between what an agent is supposed to do and what it’s actually doing before that gap becomes a customer’s problem.
In the agentic era, that person isn’t a nice-to-have. They’re the difference between AI adoption that compounds and AI adoption that quietly creates risk nobody notices until it’s expensive.
What This Means If You’re an MSP
If you’re managing this internally, the stakes are already high. If you’re an MSP, they’re higher: our customers are about to ask you to be their forward-deployed engineer, whether you’ve formally offered that or not. The MSPs getting ahead of it aren’t waiting for a customer to ask, “Who’s watching our AI?” They’re putting a real person in that seat proactively, someone who can sit inside a customer’s environment, test what’s actually being deployed, and say honestly what’s working and what needs a guardrail.
The best thing an MSP can do is test, report back to the vendor, and test again. Sometimes that works and the tool gets better. Sometimes it doesn’t, and you move on without it. Being able to tell the difference honestly, with your own numbers behind it, is becoming part of what “managed” means in an agentic world.
The Bottom Line
The paradox doesn’t resolve itself, and mixed signals don’t stop shadow AI use on their own. What actually works is simpler than another policy memo: Put someone in the seat whose job is to find out, on purpose, how far these agents can run the business and where they break, then pair that with the governance to make sure what works today still works safely tomorrow.
Agents are worth trying, but they aren’t always worth forcing. Knowing when an agent is delivering real value and when it isn't takes somebody whose job it is to find out.
Want more on avoiding the pitfalls of agentic AI adoption?
Join AvePoint’s AI Virtual Summit, Analog Insights. AI Trust, on September 10 for the “Avoiding the Pitfalls” session, featuring Microsoft’s AI Red Team on where agentic rollouts actually go wrong and how to catch it early.
