Imagine an AI system that can access organizational records, send emails, update databases, and initiate business processes. Now imagine that it makes a mistake. It sends confidential information to the wrong person, changes an incorrect record, or carries out an action that was never intended.
Who is responsible?
This is becoming an important question as organizations explore agentic artificial intelligence. Unlike systems that simply generate answers, AI agents can be designed to interact with software and execute tasks. Their growing ability to act independently creates opportunities for efficiency, but it also introduces new challenges for information systems management.
When AI moves from advice to action
Most people are familiar with AI tools that generate text, answer questions, or summarize information. In these cases, a person usually reviews the response and decides what to do next.
Agentic AI can reduce the number of steps requiring direct human involvement. An agent might identify a problem, gather relevant information, select an action, and execute it through connected software.
This can be useful, but it changes the nature of risk.
An inaccurate answer can mislead a user. An inaccurate action can alter a database, expose information, disrupt a service, or affect a customer’s experience. When agents operate quickly across multiple systems, an error may spread before anyone notices it.
The challenge is therefore not simply whether an AI agent can perform a task. It is whether the organization can trust it to perform that task within acceptable boundaries.
Why traditional controls may not be enough
Information systems already use access controls, authentication, audit logs, and organizational policies to protect data and manage user activities.
These controls remain necessary, but autonomous agents introduce additional questions.
What permissions should an agent have? Should it be allowed to modify records or only recommend changes? Can it access information beyond the task assigned to it? How can an organization identify which agent performed an action and which person authorized its deployment?
An agent that has broad access to several organizational systems may create significant risks if its instructions are misunderstood or its behaviour is manipulated.
For this reason, organizations need to treat AI agents as identifiable, managed components of their information systems, with clear ownership and carefully limited permissions.
Keeping humans accountable
One possible response is to require human approval for every AI action. Although this may be appropriate for highly sensitive activities, it could also remove much of the efficiency that agentic AI is intended to provide.
A more practical approach is to match the level of supervision to the potential impact of the task.
For example, an AI agent might be permitted to search a library catalogue, identify relevant resources, or prepare a routine report without individual approval for every step. Updating official student records, granting access to confidential files, or making consequential financial decisions may require additional verification or explicit human authorization.
The important distinction is between allowing an agent to perform routine work independently and allowing it to make consequential decisions without appropriate oversight.
Human accountability should remain clear even when execution is automated.
Building governance into the information system
Responsible agentic AI requires practical safeguards rather than policies alone.
Organizations should establish clear ownership for each agent, restrict access to the information and tools it needs, maintain records of its actions, and monitor its performance. They should also define conditions under which an agent must stop, request approval, or escalate a problem.
Testing is equally important. Before an agent is allowed to operate in a live environment, organizations should assess how it responds to incomplete information, conflicting instructions, unexpected system responses, and attempts to access unauthorized data.
There should also be a way to suspend an agent quickly if it behaves unexpectedly.
These measures are particularly important in environments handling sensitive information, including universities, hospitals, financial institutions, and public organizations.
The management challenge
The introduction of agentic AI is not only a technology decision. It affects organizational responsibilities, staff skills, operational procedures, and the relationship between people and automated systems.
Managers need to decide which tasks are suitable for automation, how success will be measured, and who is responsible when something goes wrong.
They must also consider whether the organization has reliable data, suitable infrastructure, sufficient technical expertise, and effective security practices.
Deploying an AI agent without addressing these foundations may simply automate existing weaknesses.
What should organizations do next?
Organizations exploring agentic AI should begin with a clearly defined, low-risk use case. They should test the agent in a controlled environment, evaluate its performance, establish permissions and approval requirements, and document what happens when it fails.
Expansion should depend on evidence that the system is reliable, secure, and genuinely useful.
The goal is not to prevent AI from becoming more capable. It is to ensure that increasing capability is matched by appropriate control.
Agentic AI challenges information systems professionals to rethink how technology, responsibility, and decision-making fit together.
As systems gain the ability to take action, organizations will need to understand not only what their AI can do, but also what it should be allowed to do.
The central question is no longer simply whether an AI agent can complete a task. It is whether the organization can explain, monitor, and take responsibility for the actions it performs.