Artificial intelligence has changed how people interact with information systems. We can now ask a chatbot to explain a concept, summarize a document, draft an email, or help us write code. But a new development is taking AI beyond answering questions. AI systems are increasingly being designed to plan tasks, use digital tools, and carry out sequences of actions with limited human intervention. This development is known as agentic AI.
What makes an AI agent different?
A conventional chatbot primarily responds to a user’s prompt. An AI agent, by contrast, can be given a goal and work through several steps towards achieving it. Depending on its design and permissions, it may retrieve information, interact with other software, evaluate results, and adjust its next action.
Consider a university that receives hundreds of student enquiries about registration, library access, course materials, and online learning.
A conventional chatbot might answer questions using information available to it. An AI agent could potentially go further by checking a student’s registration status, identifying missing documentation, retrieving relevant instructions, and preparing a response. Actions that affect official records or student accounts would still require appropriate permissions and, where necessary, human approval.
The difference is important. AI is moving from helping people generate information towards helping them complete work.
What does this mean for Information Systems?
Information systems bring together people, processes, data, and technology to support organizational activities. Agentic AI introduces a new possibility: some tasks that previously required a person to move between different systems could be coordinated by an AI agent.
In universities, this might include assisting with student enquiries, locating relevant learning resources, or identifying incomplete administrative processes. In businesses, agents could support customer service, inventory monitoring, and routine reporting. In IT operations, they might investigate system alerts, collect diagnostic information, or recommend corrective actions.
These applications could reduce repetitive work and allow staff to concentrate on tasks requiring professional judgement, creativity, and personal interaction.
However, the benefits depend on how well the agents are integrated into existing information systems. An AI agent cannot reliably complete a task if the underlying data is inaccurate, systems cannot communicate, or access permissions are poorly designed.
Why organizations need to rethink system design
Traditional information systems are usually designed around predefined workflows, user roles, and explicit actions. Agentic systems introduce a different way of working because an AI agent may select its next action based on the information it receives and the goal it is trying to achieve.
Organizations therefore need to consider more than the AI model itself. They must decide which systems an agent can access, what it is allowed to change, how its actions are recorded, and when a person must intervene.
The quality of the surrounding information system becomes just as important as the intelligence of the agent.
Moving beyond the excitement
There is a risk of treating agentic AI as a solution to every organizational problem. Some tasks are too sensitive, ambiguous, or consequential to delegate without close supervision. Others may be better handled by simple automation or a well-designed conventional system.
The sensible starting point is to identify a specific problem, establish measurable outcomes, and test whether an AI agent offers an advantage over existing approaches.
Organizations should measure not only time saved but also accuracy, cost, user satisfaction, security, and the frequency of errors requiring human correction.
The future of Information Systems
Agentic AI is creating new questions for information systems professionals. How should autonomous agents be integrated into existing infrastructure? What happens when several agents interact? Who is accountable when an agent takes an incorrect action? How do organizations measure whether greater autonomy actually improves performance?
These are not purely technical questions. They involve management, organizational behaviour, ethics, security, and governance.
As information systems become more autonomous, the role of the systems professional may increasingly involve designing the conditions under which AI can operate safely and usefully.
The objective should not be to remove people from every process. It should be to build systems that combine machine capabilities with human judgement, accountability, and experience.
The next stage of digital transformation will depend not simply on how intelligent our systems become, but on how effectively we integrate that intelligence into the way organizations work.