For three decades, India has tried to fix its bureaucratic sluggishness with digitisation drives: e-Office, online portals, dashboards, and single-window systems. These efforts have certainly reduced paperwork and improved data availability, but they have barely changed the basic experience of interacting with the state. Decisions still take too long. Files still languish. Citizens still depend on personal networks to get routine problems resolved. The core flaw is that most e-governance efforts have treated technology as a faster filing clerk, not as an active partner in decision-making and enforcement. I remember very precisely that while attending a seminar lecture at my alma mater JNU by a former bureaucrat of the Government of India, I came across an observation that has stayed with me ever since. He said, almost in passing, that the single most accurate predictor of whether a file would move quickly through the Indian system was not the complexity of the issue it contained, but how exposed the signing officer would feel if the decision later went wrong. Complexity, he said, the system can absorb. Exposure, it cannot. That single observation reframes the entire administrative reform debate, because it tells you that delay in India is not usually a failure of capacity. It is a rational survival strategy. Thus, to resolve this problem in Indian adminstration, the article argues that Agentic adminstration with the help of AI bureaucrats could be a most advanced possible solution here. In a recent talk on Youtube Yuval Noah Harari was talking about AI bureaucrats which is actually related to what this article is talking about in the context of agentic administration.
Recent public administration research defines agentic Artificial Intelligence (AI) as systems that integrate perception, reasoning, planning, execution, and continuous learning, often by combining 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, orchestrate actions across systems, monitor progress, and adapt strategies over time, all within 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 triage citizen requests. A World Economic Forum readiness framework for governments stresses that agentic AI lets states move from automating individual tasks, like scanning a document, to delivering entire outcomes, such as processing an application end-to-end across multiple agencies. The non-linear consequence of this shift is significant. A state that automates individual tasks gets marginal efficiency gains. A state that delegates entire outcome chains to coordinated agents gets a different category of administrative capacity altogether, because the bottleneck that previously occurred at every handoff between departments simply disappears. If we take this seriously, the question for India is not whether AI belongs in government. It is where agentic AI should sit inside the administrative machine and how it should be governed.
A practical way to think about agentic administration is to imagine four layers of AI agents embedded into India's governance stack.
{{/usCountry}}A practical way to think about agentic administration is to imagine four layers of AI agents embedded into India's governance stack.
{{/usCountry}}The first layer is citizen-facing service agents, living 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, classifies grievances, and routes them to the right authority without forcing citizens to understand the government's internal organogram. Similar agents can sit atop municipal or departmental portals, auto-filling forms, checking eligibility, tracking status updates, and proactively nudging 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 equaliser.
The second layer consists of workflow-orchestration agents that serve civil servants rather than citizens. A close friend of mine, a sub-divisional magistrate in the Uttar Pradesh government, often complains that the bulk of his working day disappears not into the substantive decisions his post actually requires, but into chasing pending responses from other departments, re-verifying documents that were already verified once at an earlier stage, and re-explaining the same procedural history to every new official who rotates into an adjacent role. His complaint, repeated almost identically across district administrations throughout India, is precisely the problem workflow-orchestration agents are designed to solve. An internal agent could monitor the life cycle of files, reading incoming documents, identifying relevant schemes and rules, drafting noting templates, scheduling consultations, and flagging contradictions or missing approvals. It can surface precedents from past decisions, model timelines and resource implications, and recommend rule-consistent options with clearly logged rationales, giving officers like my friend a structured starting point instead of a blank page and a stack of unrelated departmental queries.
The third layer focuses on oversight and accountability. Oversight agents analyse dashboards of grievances, project milestones, budget utilisation, attendance, and social sentiment to spot anomalies across departments and geographies. They can automatically flag chronic delays, escalations, or unusual complaint patterns, and trigger alerts or reviews at 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 organisations. That warning can be turned into design criteria: oversight agents should be built with full audit trails, clear separation between monitoring and sanctioning functions, and visual tools that let supervisors understand both what the agents are doing and how the underlying workflows behave.
The fourth layer focusses on agentic AI that carries real risks: Data bias, opaque logic, security vulnerabilities, and over-automation of sensitive decisions. Here, four guardrails are essential. First, bounded autonomy: agents route grievances and validate documents but only recommend options on welfare or enforcement, leaving final decisions to accountable officers. Second, continuous oversight: cross-departmental governance bodies with full audit trails, not episodic compliance checks. Third, secure domestic infrastructure: government-hosted AI built on Indian language platforms, extending beyond scheduled languages to tongues like Bhojpuri and Khasi. Fourth, public and workforce trust: Clear communication about what agents do, grievance mechanisms for contesting automated decisions, and AI literacy investment for civil servants.
If autonomous algorithms already decide who dies on a battlefield in Gaza, Ukraine, and the Sahel, the question of whether an AI agent can decide which pension file to process or which grievance to route first answers itself. The stakes in governance are not lower than in warfare. 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 functions at the speed their ambitions deserve. The countries that deploy agentic AI in governance fastest will not merely be more efficient. They will generate higher economic output per citizen, compress the timeline to higher per capita income, and accumulate the institutional credibility that shapes the global AI governance narrative for the next generation. India must move now, not because there is a comfortable margin to experiment within, but because the window is narrowing fast. There are hundreds of thousands of vacant positions across India's civil services and district administrations. These are not vacancies waiting for new hires. They are structural gaps in the delivery architecture of the Indian state that no politically realistic hiring timeline will close. Agentic AI fills those gaps as a multiplier of existing human capacity, allowing every officer to focus on decisions that actually require an officer rather than compliance tasks that currently consume most of the working day. India does not need to be run by officials sitting on files deciding the fate of its masses. It needs to be run by people who come up with genuine solutions to genuine problems. Agentic administration shifts the bottleneck from the official who holds the stamp to the citizen who has the idea. When UPI launched, India proved that when it builds correctly, it sets the global standard. Agentic Administration is the next proof of concept. The question is not whether India should build it. It is whether India will move fast enough before the opportunity runs out.
(The views expressed are personal)
This article is authored by Sudhanshu Kumar, Centre for Joint Warfare Studies, HQ (IDS), ministry of defence, New Delhi and visiting research fellow, MGIMO, Moscow.