From Copilots To Autonomous Agents: India’s Sovereign Agentic AI Shift In 2026

India’s agentic AI shift in 2026 will be shaped by sovereignty, responsibility, trust, and a Human plus AI operating model. As AI systems advance from assisting the decision making to executing actions at enterprise and national scale, the central constraint will be the ability to trust the AI. This would require the autonomous intelligence to increasingly operate on sovereign cloud, with sovereign data, using sovereign AI models, and trusted applications. 

In this model, agents execute with speed and consistency while humans retain judgment, ethical oversight, and strategic direction. This reflects a clear national intent that long-term AI leadership depends on governing the entire AI stack end-to-end, ensuring that the agent autonomy strengthens human capability rather than eroding it.

Between 2024 and 2025, enterprises across India adopted AI copilots to improve productivity and decision support. These systems summarised information, surfaced insights, and recommended actions within well-defined workflows while keeping execution firmly in human hands.

By 2026, enterprises will increasingly deploy production-grade autonomous agents that translate intent into tasks, coordinate across systems, and complete multi-step workflows with speed and consistency. In sectors such as BFSI, manufacturing, telecom, logistics, and public services, the scale and complexity of operations will make manual execution a competitive disadvantage rather than a safeguard.

Organisations that use AI to only get advice will struggle to compete against peers that deploy agents to act. Deloitte research shows that risk and governance are already the primary barriers to scaling AI. Together, these trends point to a clear conclusion: autonomy will expand, and governance must adapt to it.

Pillar I: The imperative of sovereign infrastructure

In the current geopolitical scenario, sovereign infrastructure gives a country the ability to control its AI stack end to end. This includes where data lives, how computing power is accessed, how models are built, and how agents operate at scale. Control at this level give flexibility on how to leverage AI at scale.

The IndiaAI Mission is a step in this direction and focuses on widening access to high-performance compute, improving data quality, building indigenous AI capabilities, and creating shared platforms that industry, startups, and government can use together. It also emphasises talent development, startup funding, ecosystem collaboration, and ethical AI.

Nasscom highlights strong growth in AI infrastructure driven by demand for local compute and industry-focused environments. India is building hybrid sovereign setups that blend domestic data centres, sector-specific clouds (BFSI, government, PSUs), and governed access to global platforms (e.g., hyperscalers, enterprise S/W, etc.).  

Pillar II: Trusted data and context-aware models

Autonomous agents work continuously on data, which makes trust in that data essential. These agents will have to rely on enterprise data used by companies and citizen data used by governments. The agents would require clear Data integrity, clear lineage, and relevance to ensure the agents perform autonomously and how much confidence users place in them.

Strong data governance brings trust and clarity on who owns the data, where it resides, who can access it, and how it can be used. Deloitte’s State of AI in the Enterprise shows that data quality and integration remain key requirements to ensure responsibly scaling AI beyond pilots. 

Context-aware models tuned to local languages, regulations, and operating conditions further improve explainability and reliability as agents interact directly with employees, customers, and citizens at scale.

Pillar III: Human plus AI accountability

Human plus AI combines people’s strategic decision-making with AI’s efficient execution. Humans set goals and boundaries, while AI operates reliably within these limits which is crucial for tasks like payments or supply chain management. India’s abundant skilled workforce supports this model at scale, enabling effective design, oversight, and improvement of AI systems both locally and globally.

Pillar IV: Responsible AI by design

Responsible AI is most effective when integrated in real time with the systems it oversees. As agents assume operational roles, trust is established through controls that function in real time, rather than relying solely on post-execution reviews or reports.

In practice, this means explainability, observability, and auditability are ‘live’ inside the workflow. Decisions are visible as they are made. Teams can see why an agent acted, what data it used, and which rules applied. If conditions change, limits adjust in real time. Deloitte analysis shows that organisations with continuous monitoring experience fewer compliance incidents. The same principles that protect enterprises also enable safer, more responsive citizen-facing services delivered at national scale.

With foundations in place, execution becomes the differentiator.

Telcos and the edge

As agentic systems extend across distributed environments, telecom operators play a critical enabling role. Secure connectivity, edge compute, and low-latency networks allow agents to act in real time. As networks become increasingly software-defined, agentic systems support network operations, resilience, and customer engagement. Some industry reports expect edge deployments to be central to real-time AI decision-making by 2026.

Enterprises as anchors of adoption

Companies are promoting agentic adoption by investing in data preparation, effective governance, and results-driven execution. Industries such as BFSI, manufacturing, telecom, and the public sector are at the forefront of this change. Deloitte programmes in BFSI report operational efficiency gains of 15% to 25% in areas such as collections and fraud detection through automation. These gains allow leaders to focus on judgment and strategy while agents execute consistently.

Global Capability Centres as control towers

India benefits strategically from global capability centres, many of which have become international hubs for agentic AI design, governance, and operations. Teams in India develop, test, and run autonomous systems globally, incorporating trust, security, and compliance from the outset. Nasscom reports that a majority of new GCC mandates now include advanced AI and autonomous systems, reinforcing India’s role as a builder and governor of trusted AI at scale.

India's agentic AI transition in 2026 is guided by a focus on sovereignty, trust, and accountability as essential elements in developing AI as a national strength.

For organisations, this shift unlocks scale with confidence. Agents take on execution across operations, finance, and networks, while leaders focus on judgment and direction. India’s deep talent base, strong skilling programmes, and experience running global operations make it possible to build and govern these systems from India for the world.

For government, the impact shows up at population scale. Agentic systems can strengthen healthcare, benefits delivery, agriculture, and citizen services across regions and languages. Built on trusted data and clear accountability, public services become faster, more inclusive, and easier to scale.

Ashvin Vellody is a partner at Deloitte India.

Disclaimer: The views expressed in this article are solely those of the author and do not necessarily reflect the opinion of NDTV Profit or its affiliates. Readers are advised to conduct their own research or consult a qualified professional before making any investment or business decisions. NDTV Profit does not guarantee the accuracy, completeness, or reliability of the information presented in this article.