5 Questions to Ask Before Buying an AI Agent for Higher Ed

With the rise of AI, higher education leaders are being told that AI agents will solve some of their biggest challenges — from recruiting and enrolling students, to keeping them on track to completion, to preparing them for careers beyond graduation — all with fewer resources.

It’s a compelling promise. AI agents can draft personalized outreach, summarize advising appointments, answer student questions, and automate tasks that once consumed hours of staff time.

But there’s a question that most AI demos never answer: What is the AI agent acting on?

AI capabilities are only part of the equation. As Heather Kellogg of MIT Sloan observes, “As you move agency from humans to machines, there’s a real increase in the importance of governance and infrastructure to control and support agentic systems.” The more responsibility institutions give AI, the more important it becomes to ensure it’s operating on trusted data, clear governance, and measurable outcomes.

Definition

What is agentic AI?

Agentic AI refers to AI systems that can do more than answer questions or surface insights — they help turn intelligence into action. In higher education, that can mean identifying students who may need support, helping staff determine the next best action, drafting outreach, and eventually carrying out multi-step interventions with greater autonomy. The important part is what guides those actions. AI agents are only as effective as the data and institution-specific intelligence behind them.

In other words, an AI agent is only as effective as the intelligence guiding its decisions. Like a GPS, it can calculate the fastest route and navigate flawlessly, but if the destination is wrong or the map is outdated, it will still take you to the wrong place.

This is the real challenge institutions face: closing the gap between what they know about their students and what they can actually do about it. AI agents promise to close that gap, but only if the intelligence underneath them is accurate, current, and specific to your institution.

The same is true for agentic AI. An agent can automate outreach, recommend interventions, and execute workflows perfectly. But if it’s acting on incomplete, outdated, or generic information, it simply gets you to the wrong outcome faster.

The real differentiator isn’t how autonomous an AI agent is. It’s what that autonomy is built on. Autonomy is only as valuable as the intelligence underneath it. An agent that understands your institution, your students, and the factors that actually influence success can be trusted with more. One that doesn’t, can’t.

So before evaluating features, demos, or automation capabilities, ask these five questions.

Five Questions to Ask Before Buying an AI Agent

1. 1

   What data is the agent actually using?

   An AI agent is only as good as the data it can access. Is it relying solely on SIS and LMS data, or does it also account for advising interactions, classroom engagement, financial indicators, enrollment patterns, and career readiness signals?

2. 2

   Does it understand your institution?

   No two institutions define student success the same way. Institution-specific models can reach 85–93% predictive accuracy; generic models trained on national averages can’t, because your students aren’t a national average. Ask whether the intelligence is trained on your institution’s historical data, student population, and success patterns—or whether every institution receives the same recommendations.

3. 3

   Is the information current enough to act on?

   Student risk changes quickly. Data that is days or weeks old may no longer reflect a student’s current situation. Understand how frequently institutional data is refreshed and how often predictive models are recalibrated so recommendations remain timely and relevant.

4. 4

   Can people understand why the agent made a recommendation?

   Trust requires transparency. If advisors and administrators can’t understand the factors behind a recommendation, they’re less likely to act on it. Look for AI that explains its reasoning and supports—not replaces—human judgment.

5. 5

   Does the agent help people make better decisions—or simply automate tasks?

   Automation isn’t the goal. Better decisions are. The most valuable AI helps institutions identify the right students, prioritize the right interventions, and allocate resources more effectively. It should strengthen the expertise of advisors and student success teams and not simply accelerate existing workflows.

As Dan Harder, Chief Academic Technology Officer at the University of Tennessee, Knoxville, notes, “Accuracy is always a problem if you’re going to fully automate things.” That’s not a reason to avoid autonomy. It’s a reason to get the intelligence right first. Institutions that build on accurate, institution-specific prediction will be positioned to hand agents more responsibility with confidence. Those that don’t will be automating guesswork.

The five questions above shift the conversation from what an AI agent can do to what makes its recommendations trustworthy. As you evaluate vendors, here’s a simple way to reframe the conversation:

Before: Typical AI Demo	After: What Buyers Should Ask
What tasks can it automate?	What information is it using?
How quickly can it act?	How current is the data it’s acting on?
What systems does it connect to?	What does it actually do with that data?
What models power it?	Can advisors explain its recommendations?
How autonomous is it?	What intelligence is its autonomy built on?

Lawrence Technological University: What Good Intelligence Looks Like

Lawrence Technological University (LTU) offers a good example of what this looks like in practice. Rather than starting with AI, the institution first built a strong foundation of connected, institution-specific intelligence making future AI capabilities far more valuable.

Before, advisors relied on spreadsheets, emails, and individual knowledge to determine which students needed attention. As Linda Kucinski, a Senior Business Analyst at LTU, explained, patterns often weren’t visible until problems had already emerged, making proactive intervention difficult.

After implementing the Student Impact Platform, LTU brought together siloed institutional data to create a shared, real-time view of student success. Advisors could identify emerging risks earlier, prioritize outreach with greater confidence, and coordinate interventions across colleges using insights specific to LTU’s students rather than generic benchmarks or national averages.

The biggest change wasn’t automation. It was better intelligence. By replacing fragmented information with institution-specific insights, LTU has created a stronger foundation for predictive analytics, AI, and more proactive student support.

The same approach shows up across institutions seeing measurable gains: connect the right data, build intelligence around their own students, then use those insights to guide action. Civitas Learning partners typically see 3% to 11% improvements in retention, with gains driven by reaching the right students at the right time, not simply reaching more of them.

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The Bottom Line: Evaluate What an AI Agent Knows, Not What It Does

AI agents are already reshaping how institutions support students across the full lifecycle, from enrollment and persistence to completion and career readiness. But institutions should not confuse automation with intelligence.

The institutions that get the most from AI won’t be the ones that automate the most tasks. They’ll be the ones that close the gap between what they know about their students and what they can actually do about it. That starts with intelligence worth acting on.

Before evaluating what an AI agent can do, evaluate what it knows. After all, the most advanced GPS is only useful if it’s navigating with the right destination and an accurate map. The same is true for AI: the value isn’t how quickly it moves. It’s whether it’s guiding your institution in the right direction.

See what AI acting on the right intelligence looks like.

Explore how institutions use Civitas Learning’s AI-powered workflows to turn institution-specific insight into coordinated action — from identifying the right students to launching the right interventions.

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