For three decades, India has tried to fix its bureaucratic lethargy with a digitization drive: e-offices, online portals, dashboards and single-window systems. These efforts have certainly reduced paperwork and improved data availability, but they have hardly changed the basic experience of interacting with the state. Decisions still take a lot of time. The files are still pending. Citizens still rely on personal networks to solve routine problems. The main drawback is that most e-governance efforts have treated technology as a speedy filing clerk and not as an active partner in decision making and implementation. I vividly remember when I was attending a seminar lecture by a former Government of India bureaucrat at my alma mater JNU, I was struck by a comment that has stuck with me ever since. He said, almost in passing, that the most accurate predictor of whether a file would move quickly through the Indian system was not the complexity of the issue involved, but how the signing officer would feel if the decision later went wrong. Complexity, he said, the system can absorb. Exposure, it can’t be. That single observation reframes the entire administrative reform debate, because it tells you that delay in India is not usually a failure of capacity. This is a rational survival strategy. Thus, to solve this problem in Indian administration, the article argues that agent governance with the help of AI bureaucrats can be the most advanced possible solution here. In a recent conversation on YouTube, Yuval Noah Harari was talking about AI bureaucrats which is exactly what this article is talking about in the context of agent governance.
Recent public administration research has defined agentic artificial intelligence (AI) as systems that integrate perception, reasoning, planning, execution, and continuous learning, often by connecting foundation models with sensors, data sources, and actuators. Unlike traditional rule-based automation or static chatbots, agentic AI systems are designed to pursue goals in complex environments. They can interpret inputs, decompose tasks, organize tasks across systems, monitor progress, and adapt strategies over time, all within the constraints set by humans and law. In the public sector, scholars and practitioners already talk about AI bureaucrats: autonomous or semi-autonomous software agents that route bills, monitor tax compliance, coordinate emergency responses, or vet citizen requests. The World Economic Forum’s preparedness framework for governments emphasizes that agentic AI enables states to scale from automating individual tasks, such as scanning a document, to delivering holistic results, such as processing an application from end to end across multiple agencies. The non-linear consequence of this change is important. A state that automates individual tasks gets marginal efficiency gains. A state that delegates entire outcome chains to coordinated agents gets an entirely different range of administrative capacity, because the earlier constraint on each handoff between departments disappears. If we take it seriously, the question for India is not whether the government has AI or not. This is where agentic AI should sit inside the administrative machine and how it should be controlled.
A practical way to think about agent governance is to imagine four layers of AI agents embedded in India’s governance system.
The first layer is citizen-facing service agents, who live where people actually touch the state: portals, helplines, local offices and mobile apps. Samadhan Didi is an early example, a voice agent that listens, asks clarifying questions, categorizes complaints, and routes them to the right authority without forcing citizens to understand the internal organization of the government. Similar agents can sit on municipal or departmental portals, auto-fill forms, check eligibility, keep track of status updates and proactively prompt citizens when additional information is needed. In a country where language, literacy and geography still block access to services, multilingual, conversational agents in the state’s own infrastructure are a powerful equalizer.
The second layer consists of workflow-orchestration agents that serve civil servants rather than citizens. A close friend of mine, who is a Sub-Divisional Magistrate in the Government of Uttar Pradesh, often complains that the bulk of his working day goes not in concrete decisions that his post actually requires, but in chasing pending responses from other departments, re-verifying documents that were once verified at an earlier stage, and explaining the same procedural history again to every new officer who rotates into the adjacent role. His complaint, repeated almost identically in district administrations across India, is exactly the problem that workflow-orchestration agents are designed to solve. An internal agent can monitor the life cycle of files, read incoming documents, identify relevant plans and rules, draft noting templates, schedule consultations, and flag conflicts or missing approvals. It can draw from precedents from past decisions, model timelines and resource implications, and recommend rules-consistent options with clearly logged reasoning, giving executives like my friend a structured starting point rather than a blank page and a pile of unrelated departmental questions.
The third layer focuses on oversight and accountability. Oversight agents analyze dashboards of complaints, project milestones, budget usage, attendance and social sentiment to detect anomalies across departments and geographic areas. They can automatically flag long-term delays, escalations, or unusual complaint patterns, and trigger alerts or reviews at the appropriate administrative levels. Scholars studying oversight structures for agentic AI warn that agents intensify existing challenges of continuous supervision, interdepartmental coordination, and operational visibility in public organizations. That caveat can be turned into a design criterion: Inspection agents should be built with full audit trails, clear separation between monitoring and approval functions, and visualization tools that let supervisors understand what the agents are doing and how the underlying workflows behave.
The fourth layer focuses on agentic AI that raises real risks: data bias, opaque logic, security vulnerabilities, and over-automation of sensitive decisions. Four railings are necessary here. First, limited autonomy: agents deal with complaints and validate documents but only recommend options on welfare or enforcement, leaving final decisions to accountable officials. Second, continuous oversight: inter-departmental governance bodies with full audit trails, not episodic compliance checks. Third, secure domestic infrastructure: Government-run AI, built on Indian language platforms, extends beyond scheduled languages ​​to languages ​​like Bhojpuri and Khasi. Fourth, public and workforce trust: clear communication about what agents do, complaint mechanisms to counter automated decisions, and AI literacy investments for civil servants.
If autonomous algorithms already decide who will die on the battlefields in Gaza, Ukraine and the Sahel, then the question of whether an AI agent can decide which pension file to process or which complaint to resolve first finds itself answered. The risks in governance are no less than in war. They are higher because the beneficiaries of better governance are not a few hundred people within range of a weapons system, but 1.4 billion people, whose entire economic trajectory depends on whether their state acts at a pace commensurate with their ambitions. The countries that most quickly deploy agentic AI in governance will not only be more efficient. They will generate higher economic output per citizen, span the time frame to higher per capita income, and accumulate institutional credibility that will shape the global AI governance narrative for the next generation. India must move forward now, not because there is a comfortable margin to experiment within, but because the window is rapidly shrinking. There are hundreds of thousands of vacant posts in the civil services and district administrations of India. These are not vacancies awaiting new appointments. They are structural gaps in the delivery architecture of the Indian state that no politically realistic recruitment timeline will be able to close. Agent AI fills those gaps as a multiplier of existing human capability, allowing each officer to focus on the decisions that actually require an officer rather than the compliance tasks that currently consume most of the work day. India does not need to be run by officials sitting on files deciding the fate of the people. It needs to be run by people who bring real solutions to real problems. Agent administration removes the hurdle from the officer who stamps and places it on the citizen who has the idea. When UPI was launched, India proved that when it builds it right, it sets global standards. Agent governance is the next proof of concept. The question is not whether India should build it. The question is whether India will move fast enough before the opportunity runs out.
(Views expressed are personal)
This article is written by Sudhanshu Kumar, Visiting Research Fellow, Center for Joint War Studies, Headquarters (IDS), Ministry of Defence, New Delhi and MGIMO, Moscow.
