How IT Leaders Should Evaluate Agentic AI Beyond the Demo

Agentic AI is quickly becoming an enterprise technology priority, but the term is already losing clarity. Some agents can interpret a goal, coordinate steps across tools, and adjust when conditions change. Others simply add a conversational interface to existing automation.

Nearly two-thirds of enterprises worldwide have experimented with AI agents, but fewer than 10% have scaled them to deliver tangible value. Enterprise IT leaders need a way to distinguish an impressive demonstration from a system that can perform reliably inside a real organization. That evaluation should focus on these key questions: Is the agent designed around a meaningful workflow? Does it have the context and system access required to act? Does it know when human judgment should take over? And can the organization measure whether it actually improved the outcome?

Evaluate the Workflow Before the Agent

A meaningful use case begins with a specific process and outcome:

• What outcome is this workflow supposed to produce?

• Which systems and data sources does the agent need to access?

• Where are the handoffs between systems or teams?

• What exceptions routinely occur?

• Which actions can the agent take independently?

• Which decisions require approval or human judgment?

IT leaders should also ask which audience-specific journeys and best practices come built in, as generic tools often leave teams designing every path themselves. For example, purpose-built advancement journeys should translate donor engagement signals into recommended next steps, with best practices guiding whom to engage, when, and toward what goal.

Higher education offers a useful example of why this matters, particularly when workflows span multiple departments and systems. A student’s journey spans admissions, financial aid, registration, advising, career support, and eventually alumni engagement. While institutions may manage these as separate functions, students experience them as one university. In fact, most consider connected tools that eliminate redirects and repeated information in their definition of a modern campus digital experience. Connecting that data helps students, alumni, and donors feel known and makes staying engaged easier.

Context Turns an Answer Into Action

A language model might be able to explain a process, but the explanation alone doesn’t make a system agentic. To help someone move forward, an agent needs relevant context, like who the person is, what’s already happened in their journey, which requirements apply, and what actions actually remain available.

Consider a student trying to determine whether they’ve submitted all the documents required to complete an application. A conversational assistant might provide a standard list and relevant links. But an agentic system could identify which requirements apply to that student, recognize what’s already been submitted by or on behalf of the student, flag the remaining outstanding items, and even guide or initiate the appropriate next step.

This requires reliable data, access to the right systems, an understanding of the workflow, and clear rules governing what the agent may do. It also requires domain expertise: enterprise processes contain policies, dependencies, and exceptions that are rarely obvious from the outside.

When evaluating agentic capabilities, IT leaders should ask:

• What information can the agent access, and which sources are authoritative?

• Can it maintain context across interactions and systems?

• Does it understand the rules and exceptions within the workflow?

• What can it recommend, initiate or complete?

• What happens when information is incomplete or systems conflict?

• What permissions does the agent inherit, and how are those permissions enforced across systems?

• Can administrators define which actions require approval?

• Can IT teams trace what information the agent accessed, what actions it took and why?

Test the Exceptions, Not Just the Happy Path

Agentic capabilities are easiest to demonstrate when every system responds, the required data is available and the user follows the expected path. Enterprise environments rarely work that cleanly.

An evaluation should deliberately introduce the conditions most likely to expose an agent’s limitations. What happens when a required field is missing? When two systems return conflicting information? When an integration is unavailable? When a user falls outside the standard policy? When the agent lacks sufficient information, confidence or permission to take the next action?

The goal isn’t simply to determine whether the agent completes the task. IT leaders should evaluate whether it recognizes uncertainty, asks for additional information, explains what prevented it from proceeding, chooses an appropriate alternative or escalates to a person. The most valuable agents are designed to recognize when they should stop and ask a person before taking action. A system’s behavior when it can’t complete a workflow may reveal more about its enterprise readiness than a successful demo does.

Determine Where Autonomy Should Stop 

When agents can access student records, donor information, and financial details, security and compliance become essential evaluation criteria. IT leaders should ask how the system protects sensitive data, enforces institutional policies, and limits independent action. Institutions need to see those safeguards work before entrusting an agent with sensitive information or consequential actions.

Some actions are straightforward candidates for automation, such as identifying an incomplete form, retrieving information, surfacing a deadline, or initiating a routine process. Other situations require judgment, empathy or an understanding of circumstances that can’t safely be reduced to a rule.

An agent might recognize, for example, that a student has attempted the same process several times without success. Another automated message may only add to the frustration. The more useful action would be to alert an advisor and provide the context needed to intervene.

Organizations should define these handoffs before deployment, with human involvement reflecting the complexity and consequences of each action. Requiring approval for every step can eliminate efficiency, but broad autonomy without clear boundaries introduces unnecessary risk.

An agent that prepares personalized outreach to a student, customer or donor can identify the opportunity, gather relevant context and draft the message, but a person should review and approve it before it’s sent. The technology prepares the work; the team decides what happens next.

The evaluation should therefore consider not only whether human oversight exists, but whether the system consistently respects approval requirements and recognizes when additional human judgment is needed.

Look for Evidence of Value

Define success before deployment, then measure whether more people completed the process, routine requests were resolved faster, fewer users abandoned the workflow, employees spent less time moving information between systems, or cases requiring human attention were identified sooner. Messages sent, questions answered, and tasks initiated show activity. The scorecard needs to show whether the workflow improved.

There’s real reason for excitement when AI can help someone finish a process, get timely support, or reconnect with an institution that understands them. IT leaders should expect vendors to demonstrate how their systems deliver those outcomes, even when data is incomplete, systems conflict, or human judgment is required. That’s the standard worth scaling.