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 Long-term memory can cause outdated or sensitive information to persist.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.