Navigating The Next Frontier: Agentic AI In Financial Services

Financial services organisations are moving beyond chatbots and predictive models into the realm of agentic AI. These organisations are getting ready for the next decade, where agentic AI will deliver hyper-personalisation, operational agility, and decision automation. Beyond analysing data and generating content, AI agents can reason, plan, and orchestrate tasks across complex environments with varying degrees of autonomy.

The introduction of autonomous, goal-seeking AI systems requires a new kind of discipline - one that blends strategic ambition with rigourous governance, continuous monitoring, and a deep understanding of how autonomy changes the nature of organisational risk. Agentic AI is powerful not because it automates tasks, but because it adapts, plans, and interacts independently. 

Agentic AI presents an opportunity to reimagine how financial institutions operate. Customer journeys can become hyper-personalised as agents dynamically interpret behavioural signals, coordinate between multiple data sources, and adjust recommendations in real time. Routine onboarding, KYC, and Anti-Money Laundering optimisation processes, often fragmented across teams, tools, and legacy systems, can be executed through a coordinated network of principal agents, service agents, and task agents that collectively verify documents, assess risk, and escalate exceptions.

In operational and governance contexts, agentic systems can strengthen control effectiveness, detect anomalies faster, and manage compliance tasks that traditionally require extensive human intervention. Multi-agent setups can generate and review code, automate DevOps pipelines, remediate cloud security issues, and support predictive maintenance.

However, this autonomy also introduces risks. While the traditional AI risks of bias, privacy, and reliability are still present, Agentic AI can reinterpret goals, pursue unintended optimisation paths, misuse tools creatively, or collaborate in ways that bypass established controls. Long-term memory can cause outdated or sensitive information to persist. Decision strategies can drift subtly over time, making failures harder to detect. Multi-agent ecosystems also introduce other challenges, such as authority creep, cross-agent collusion, and cascading system effects across interconnected digital environments. 

These risks are not theoretical. They emerge from the fundamental design of agentic AI: the ability to reason, plan, and act with varying levels of independence. This is why financial services leaders must develop a disciplined, end-to-end framework for introducing autonomy safely.

Organisations must now ensure that the risk mitigation strategies are developed and implemented alongside the AI itself. For this to happen, organisations should first define clear and explicit objectives, articulate agent personas and behavioural boundaries, and map the acceptable risk appetite for each use case. Other decisions that have to be set early and codified include determining which systems an agent may access, the tools it may invoke, the level of autonomy it has, and the escalation paths. Most importantly, organisations should adopt a "compliance-by-design" mindset where controls, guardrails, testing protocols, and human-in-the-loop thresholds are part of system architecture, not governance afterthoughts. 

Equally important is the emergence of codified guardrails, which are machine-interpretable rules that define what an agent can and cannot do. These guardrails must govern goal adherence, data access, tool usage, privacy boundaries, memory lifespan, and escalation triggers. When designed as modular, reusable components, they allow financial institutions to scale AI more rapidly and consistently across functions.

Agentic AI changes too quickly for traditional monitoring practices. It requires continuous, real-time monitoring across performance, quality, latency, and cost. The most advanced organisations are now deploying AI-as-judge models — secondary agents that evaluate the behaviour, alignment, and output quality of primary agents.

This monitoring must be centralised, not siloed by department. A unified oversight layer gives risk, compliance, and technology teams a shared view of how every agent behaves across the enterprise, enabling proactive intervention rather than reactive containment.

Despite its autonomous capabilities, agentic AI cannot replace human judgment, particularly in areas involving customer impact, regulatory exposure, or fiduciary responsibility. Human-in-the-loop oversight must be preserved, but applied intelligently. Not every agent action requires intervention; however, high-impact decisions certainly do.

This demands a clear taxonomy of decisions: agents that can perform independently, those requiring human approval, and those requiring joint decision-making. By designing this up front, organisations avoid slowing down the system while ensuring that autonomy never oversteps its mandate.

Introducing agentic AI should be a phased and intentional journey. Many financial institutions are starting with controlled pilots in low-risk domains, testing multi-agent orchestration within well-defined parameters. As confidence grows, agentic capabilities can extend into more complex workflows, customer-facing experiences and risk-sensitive areas.

Agentic AI represents a reimagination of how financial institutions make decisions, manage risk, and create value. It can be a force multiplier for growth, efficiency, and customer experience. However, it has to be deployed with caution and foresight. 

Agentic AI is a new frontier. It offers immense promise, but its power must be matched by discipline. The future belongs to the institutions that learn to embrace autonomy, without ever relinquishing governance.

Rishi Aurora is Managing Partner, IBM Consulting, India & South Asia.

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.