Agentic AI and the future of higher education
18 February 2026
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AI already supports teaching and knowledge work. The next phase is agentic AI - systems that don't just respond to prompts but take action, end to end. For universities under pressure to do more with less, this shift points straight at the operational core: admissions, administration, student support and compliance.
From content creation to autonomous action
Generative AI helps with summaries, lecture prep and on-demand answers. Useful, but passive. Agentic AI is active: it reasons over inputs, applies rules, triggers approvals, updates multiple systems and leaves a complete audit trail.
Think of it as moving from a smart assistant to a reliable process executor. The output isn't just content; it's a completed task you can verify.
Admissions, engagement and student success
Admissions is the obvious first win. An agent can validate documentation, flag gaps, auto-prompt applicants, sync internal systems and communicate status in real time - with every action logged for audit. This removes repeat work and shortens cycle times.
Student engagement benefits too. Integrated with your LMS and student systems, agents can spot risk signals early, trigger alerts, schedule interventions and escalate to staff when judgement or empathy is required. Humans set the guardrails and make the calls; AI clears the admin roadblocks.
Compliance first, not as an afterthought
Staff are already using public AI tools, which increases exposure to data leakage, IP loss and regulatory issues. A University of Melbourne and KPMG survey reports 72% of employees in emerging economies use AI regularly, versus 49% in advanced economies, while fewer than half of businesses have an AI governance policy in place. Source
Regulators are watching. France's competition authority fined Google more than $271 million in a case touching on copyright and LLM training practices. Details Italy's data watchdog has also penalised an AI chatbot developer. The message is clear: governance, role-based access and auditability are non-negotiable.
Questions to ask before you scale
- Which licences are in use across the institution?
- Where are prompts, chats and outputs stored?
- Do the tools comply with POPIA and your institutional policies?
- Can you monitor the data being sent to AI tools?
- Can you track who did what, and when, across departments?
- Is your intellectual property protected end to end?
- Are staff using public or free AI tools on corporate networks?
- Have you blocked public LLMs from campus networks where appropriate?
Build a secure, governed AI environment
Contain interactions, monitor usage and keep everything auditable. Use role-based access so only the right people can run the right agents against the right data. Without this visibility, the risk can outweigh the reward fast.
Compliance-by-design isn't paperwork - it's architecture: identity, permissions, logging, retention and clear human-in-the-loop checkpoints.
A new operating model for higher education
Generative AI helps people work faster. Agentic AI changes how work is done. Admissions, administration, student support and compliance can move from "chasing tasks" to "verifying outcomes."
The real shift is organisational. Institutions that embed security, governance and purpose into their AI strategies from day one will capture the value and avoid the headlines.
Starter roadmap for universities
- Select 2-3 high-friction workflows for pilots (e.g., admissions document checks, appeals, bursary processing).
- Stand up a governed AI environment: private data access, role-based permissions, full logging and audit trails.
- Define human review points for fairness, ethics and edge cases.
- Integrate agents with LMS, SIS and CRM via APIs; avoid swivel-chair steps.
- Run small, time-boxed pilots; measure cycle time, accuracy, staff hours saved and student satisfaction; iterate.
- Train staff on use, risks and escalation paths; communicate clear do/don't guidelines.
- Form an AI governance group; align with POPIA; set data retention and incident response.
- Scale what works, retire what doesn't; update policy and training as you grow.
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