Something is shifting in artificial intelligence (AI). For the past two years, the big story in education was generative AI. Tools that answer questions. Tools that draft essays, summarise notes, explain concepts. Universities debated plagiarism. Accreditation bodies issued advisories. Faculty worried about shortcuts. Those were early tremors.
The larger disruption is still arriving. It is called agentic AI.
Agentic AI does not merely respond to a prompt. It plans, decides, acts, and adapts. A generative AI model answers your question. An agentic AI system takes your goal, breaks it into tasks, selects tools, executes steps, and iterates until the work is done. The difference is not incremental. It is categorical.
For Indian higher education, this raises a question that cannot be deferred. Are we ready for AI that does not wait to be asked?
The simplest way to understand agentic AI is through what it can do that earlier tools cannot.
A conventional AI tool might generate a quiz from a textbook chapter. An agentic system would go further. It would identify learning gaps from student data. It would design a remedial module. It would schedule delivery, track completion, adjust pacing, and flag at-risk students. All of this without a single instruction from a faculty member after the initial setup.
Prototypes of such systems are already running in international universities. The underlying technology is advancing fast. The question is not whether agentic AI will reach Indian campuses. It is whether Indian institutions will shape its arrival or simply receive it.
Indian higher education is vast and uneven. It spans elite research universities and under-resourced colleges in small towns. It includes a large online and distance segment serving learners who work full-time, live far from cities, and study on mobile phones. It operates under frameworks designed for a different era. It faces a documented shortage of qualified faculty, especially in emerging disciplines.
These realities matter. Agentic AI, if thoughtfully deployed, could address some of the system’s most stubborn problems. Faculty shortages could be partially offset by AI agents delivering personalised support at scale. Assessment backlogs could be managed by agents that evaluate, give feedback, and track outcomes. Students in remote locations could receive the kind of continuous academic engagement that was previously available only in well-staffed institutions.
The opportunity is real. So is the risk of getting it wrong.
The classroom has always been about mediated learning. A teacher mediates between knowledge and the student. Pedagogy, assessment, feedback, and mentorship are the instruments of that mediation.
Agentic AI introduces a new kind of mediator. One that is always available. One that is infinitely patient. One that tracks individual progress at granular levels and adapts in real time.
This is not the end of the teacher. It is a renegotiation of what teaching means. If an agent handles identification of gaps, targeted intervention, and outcome tracking, the faculty member is freed for higher-order work. Debate. Mentorship. Ethics. The cultivation of judgment. Whether Indian faculty are prepared for this shift is an open question.
Assessment will also change. The logic of a single end-term examination that determines 80 per cent of a grade begins to collapse. Outcome-based education, which India’s accreditation bodies have mandated for years, becomes genuinely achievable when agents track attainment continuously rather than inferring it from a three-hour paper.
Indian universities are not short of policy. They are short of policy that anticipates.
Who is responsible when an AI agent gives incorrect academic guidance and a student makes a consequential decision based on it? Who owns the learner data that agentic systems accumulate over months? What are the limits of AI agency in evaluation? How do institutions ensure autonomous assessment systems do not encode existing biases? These questions do not have settled answers anywhere in the world. In India, they are barely being asked.
The readiness problem is also about infrastructure and culture. Most Indian universities lack the data architecture that agentic AI requires. Learner records are fragmented. Faculty training in working alongside AI agents does not exist. Leadership teams that understand the difference between a chatbot and an agentic system are rare.
If there is one segment where agentic AI has immediate and tractable application, it is distance and online education.
Students in this segment are underserved by conventional support systems. They cannot walk into a faculty member’s office. They study in isolation. Dropout rates are higher. The reasons trace back largely to the absence of timely academic support and personalised engagement.
An agentic system designed for distance learners could function as a continuous academic companion. It could answer questions, track progress, surface relevant resources, flag disengagement early, and connect students to human support when needed. This is not a replacement for a university. It is an extension of one, into times and places conventional institutions cannot reach.
The regulatory framework for distance education in India is already more open to technology integration than its conventional counterpart. This creates a real opportunity for universities willing to experiment carefully, build evidence, and share findings. Institutions that move first, with proper attention to ethics and equity, will shape the template that others follow.
Readiness for agentic AI is not a technology procurement decision. It begins with institutional clarity about purpose. What problem is the institution trying to solve? What does good learning look like for its students? What is the faculty member’s role when agents handle routine academic tasks? These questions are prior to any technology choice.
Faculty development is the most critical and most underinvested dimension. Faculty who understand what agentic systems can and cannot do, who can design curricula that leverage AI agency while maintaining integrity, and who can mentor students in an AI-augmented environment, are the real infrastructure. No software licence substitutes for that.
Data governance is the second critical dimension. Agentic AI learns from interaction. It accumulates behavioural and performance data over time. Universities deploying such systems must be clear about what data is collected, how it is stored, who can access it, and what protections students have. India’s data protection framework is still maturing. Universities cannot wait for legislation to catch up. They need to build responsible data practices now.
The third dimension is equity. Agentic AI can democratise academic support in a country where access to quality teaching is deeply unequal. It can also widen gaps if the best systems go to well-resourced institutions and cheaper, lower-quality agents land in under-resourced ones. That choice is not made by technology. It is made by policy and institutional intent.
There is a particular risk in Indian higher education of treating agentic AI as a compliance exercise. When regulators issue advisories, institutions write policies. When accreditation bodies ask for AI integration evidence, universities add AI mentions to their documents. This reactive posture is understandable. It is also inadequate.
Agentic AI is not a feature to be added to existing processes. It will change the underlying logic of those processes. Institutions that treat it as an add-on will find themselves perpetually behind. Those that engage with it as a structural shift, rethinking curriculum, faculty roles, assessment, and governance together, will be positioned to use it for genuine improvement.
India has demonstrated in sector after sector that it can move quickly when will and infrastructure align. The question is whether the educational establishment can move with sufficient speed and intentionality. The window for shaping the transition rather than enduring it is open. It will not remain open indefinitely.
Agentic AI is arriving in education whether institutions prepare for it or not. The choice before Indian universities is not between a world with agentic AI and a world without it. The choice is between engaging with it on terms that serve students and institutions, or encountering it as a disruption to be contained.
The autonomous classroom is not a distant scenario. In some form, in some institutions, it is already beginning to take shape. The more useful question is not whether Indian higher education is ready. It is what readiness would actually require, and how quickly institutions, regulators, faculty, and students can build it together.
That conversation should have started yesterday. The next best time to begin it is now.
Sanjay Fuloria is a Professor and Director of the Centre for Distance and Online Education (CDOE) at ICFAI Foundation for Higher Education (IFHE), a deemed-to-be University in Hyderabad. He teaches Managing Digital Transformation and Quantitative Methods in the MBA programme and researches at the intersection of AI, consumer engagement, and digital transformation.
Views expressed are the author’s own and don’t necessarily reflect those of Down To Earth