AI agents: Support systems, not substitutes

From chatbots to doers

Artificial intelligence has already moved beyond tools that only answer questions. Todayfs systems can actually plan, reason and take action.Earlier this year, OpenAI fs Codex showed how an AI program could read plain English instructions and write working computer code, turning it into a digital teammate for programmers. Likewise, LinkedInfs Hiring Assistant (LiHA) helps recruiters by shortlisting candidates and providing justification of each candidate with evidence, freeing up time for the human side of hiring for interviews and decisions.

These are examples of AI agents, programs that donft just talk, but execute in steps autonomously. They can handle small but important tasks that usually take hours of coordination. For a country like India, where every system serves millions at once, this kind of technology could make an enormous difference. AI agents could be applied in many areas, but this article focuses on three that touch almost every Indian household: healthcare, justice, and education.

Everyday challenges at national scale

 India fs progress over the years has been remarkable, yet the countryfs population makes every public service a challenge of scale.

Healthcare: With the large population, hospitals and clinics are always crowded. Doctors and nurses juggle endless patients and paperwork. The question is not effort, but how to make their time go further.

Justice: Indian courts are handling millions of cases. A large part of the delay comes from organising documents, scheduling hearings as well as notifying parties. These are important but time consuming tasks that slow down the system.

Education: More than 2.6 crore students attend Indian schools, but many classrooms have limited resources and large class sizes. Teachers spend most of their time managing rather thanmentoring. In rural areas, poor infrastructure and limited resources make quality education even harder to deliver.These sectors all face a similar issue: dedicated people doing more work than time allows.

How AI agents could help

In Healthcare: Imagine if a hospital had a digital assistant that automatically reminded patients about check-ups either through text message or a call, kept track of test reports and prepared summaries for doctors before each visit. Such an agent would not diagnose or treat patients and it would simply handle the routine coordination that often eats up valuable time. This could help medical staff focus on patients instead of paperwork and ensure that fewer people fall through the cracks of follow-up care.

 In Justice: In courts, AI agents could help clerks and lawyers by organizing filings, listing upcoming cases, and pulling out earlier judgments that might be

relevant. They could prepare summaries or timelines that make it easier for judges to review a case quickly. All this information would still be verified by humans and the agent only handles the repetitive groundwork. Even a small time saving per case could, over millions of cases, make the system run far more smoothly.

In Education: In schools, AI agents could act as multilingual teaching companions. They could translate lessons into different languages, suggest quizzes based on what students need to revise, or help teachers check assignments faster. For students, these agents could become patient tutors, explaining concepts step by step and adapting to each learnerfs pace. If designed well, these systems could run on affordable smartphones or computers, helping rural schools improve learning quality without needing big infrastructure changes.

Help, not replacement

The real opportunity with AI is to make people more effective, not to make them unnecessary. Doctors, judges, and teachers all make complex human decisions that no algorithm can replace.

The goal is for AI to take care of the background work so professionals can focus on thinking, listening and deciding.

To do this safely, every AI system should work under clear human supervision. For example, a healthcare agent may remind a patient about a test but cannot decide the treatment. A teaching agent may grade an assignment, but the teacher still decides the final mark.

Government guidelines and responsible companies can ensure that these boundaries are respected, and that all data used by AI agents remains private and secure. Another key step is training.

The people who use AI should understand what it can do and where it can go wrong. When professionals are comfortable with the technology, it becomes a helpful partner instead of a source of worry.

Building for India’s scale

India fs greatest strength is its ability to innovate for large, diverse populations. That same mindset can guide the country fs AI journey.

The systems we build should be simple, inclusive, and affordable. Some ways to move forward include:

• Testing AI assistants in real hospitals, classrooms, and courts to measure real benefits, not just theoretical ones.
• Encouraging Indian startups and universities to create their own local-language agents.
• Making sure these systems are lightweight so they can work on basic devices and limited internet connections.
• Using diverse, representative data so the technology works fairly for everyone, for example: urban and rural, Hindi- and Tamil-speaking alike.

By focusing on usefulness and accessibility, India can ensure that AI serves the many, not just the few.

A human-centered future

AI agents are already here, and they are getting better at working alongside people. Their promise lies in collaboration, not control.

When built with care, they can help a doctor spend more time listening to patients, a teacher devote more time to mentoring, and a judge focus more on justice than on scheduling. India has an opportunity to lead by example by showing that technology can support human expertise instead of competing with it. If developed responsibly, AI agents could become quiet, reliable helpers in some of our most important public systems, a proof that progress is not about replacing people, but amplifying what they do best.

Shikhar Mathur is a senior software engineer at Airbnb with expertise in agentic and generative AI. He holds a Master’s degree from Carnegie Mellon University and is an IEEE Senior Member. Shreya Chadha is a software engineer at LinkedIn with expertise in AI and real-time event streaming. She holds a Master’s degree from Carnegie Mellon University.; views are personal