Steering intelligence: Building governance foundations for the agentic AI age

The rapid rise of agentic AI systems is reshaping expectations around accountability, risk management and governance across India’s financial sector. As autonomous systems begin to take on more complex decision-making roles, the industry is being compelled to rethink how trust, oversight and human judgement are embedded into AI-driven workflows. These themes took centre stage at the recent AI@Work: Shaping the Future of Business with AI panel discussion in Mumbai, moderated by Nagaraj Nagabushanam, Vice President, Data and Analytics and Designated AI Officer at The Hindu . The discussion brought together senior technology and risk leaders from across banking, insurance and enterprise technology, including Rohit Kilam, CTO, HDFC Life Insurance; Premraj Avasthi, Head - IT and CIO, GIC Housing Finance Ltd; Pushkal Tenjerla, Head IT Security, RBL Bank; and Rajesh Malhotra, senior leader, Data & AI, IBM. Together, they examined how agentic AI is altering not just operational models, but the very foundations of governance in regulated industries. Reimagining Governance for Autonomous Agents The conversation opened with a close examination of how agentic AI disrupts traditional oversight structures. Kilam framed this shift as a fundamental change in the tempo of governance itself, observing that “we are seeing a transition from slower governance to a faster governance.” As autonomous agents are designed to act, learn and adapt in near real time, conventional post-facto controls are no longer sufficient. To address this, Kilam outlined three governance models that organisations are currently navigating: fully autonomous systems, human-in-the-loop workflows and human-on-the-loop audits. Each represents a different balance between machine autonomy and human supervision. However, he emphasised that regardless of the model adopted, the principle remains unchanged: governance mechanisms must operate at the same speed as the systems they are designed to supervise. Embedded controls, continuous monitoring and real-time intervention are becoming essential features rather than optional safeguards. The Enduring Relevance of Human Judgement While agentic AI promises efficiency and scalability, Avasthi underscored that human judgement remains indispensable, particularly in financial decision-making contexts that demand nuance and contextual awareness. He pointed out that “there has to be a moderation in place that the AI has to learn the aspects of human intent,” highlighting the limits of automation in domains shaped by diverse borrower profiles, regional policies and socio-economic variables. In lending and housing finance, decisions are often influenced by unstructured data, behavioural signals and situational factors that are difficult to codify fully. Avasthi argued that these complexities require deliberate human interpretation, even as AI systems assist in analysis and pattern recognition. Rather than viewing governance as a binary choice between humans and machines, he positioned it as a shared responsibility, where oversight is distributed across technology, process and people. Trust, Risk and the Compliance Mindset From a risk and security standpoint, Tenjerla broadened the discussion by linking governance directly to trust. He cautioned that the success of agentic AI in BFSI environments depends not only on technical robustness, but also on behavioural reliability. “We are not just governing the technology part of agentic AI here. We...

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