A Transformation in the Financial Sector: The Rise of Agentic AI
Over the years, financial institutions have been quick to harness the power of machine learning and automation, drastically transforming their operations. The recent surge in generative AI has further expedited this shift, with various banks introducing copilots, coding assistants, and customer-facing tools throughout their organizations. However, the core design of most banking systems still revolves around human users, be they retail customers, traders, relationship managers, portfolio managers, or the software engineers who maintain these systems. The basic assumption is that a human user will log in, navigate the interface, interpret the data, follow a process, and ultimately decide the next course of action.
Agentic AI, a more advanced form of artificial intelligence, is challenging this notion. Unlike generative tools that mainly provide answers, agentic AI systems are capable of planning, calling tools, executing tasks, and working towards a defined goal. This emerging technology is beginning to question the fundamental design of banking systems.
Shifting Towards Agentic AI in the Financial Industry
This shift towards agentic AI is beginning to permeate the financial industry, and it is predicted to accelerate rapidly. According to the Cambridge Centre for Alternative Finance’s 2026 Global AI in Financial Services report, 52% of firms are already piloting or deploying agentic AI. Furthermore, an impressive 81% of respondents anticipate that autonomous agents will be significantly achieved by 2030.
Goldman Sachs, for instance, is collaborating with Anthropic to develop AI agents to expedite trade and transaction accounting, client due diligence, and onboarding. These are all crucial areas in banking that rely on seamless data management, controls, judgement, and execution.
Moving Towards Agent-Ready Financial Systems
The next generation of financial systems will not only be driven by human users navigating through static interfaces, but also by AI agents acting on behalf of customers, employees, traders, and risk and operations teams. The use of natural language will expand beyond chatbot prompts to become a programming syntax, instructing systems, triggering workflows, and coordinating activities across the organization. This introduces a new design question for banks: Can their data and IT systems be discovered, understood, operated, and audited by AI agents?
Large financial institutions often operate on legacy technology platforms, which include decades-old code, manual processes, and human interpretation. In many cases, these systems contain critical business logic that is buried within undocumented rules, manual workarounds, fragmented data stores, and systems. According to AWS, over 40% of business rules are embedded solely in code with no supporting documentation. This creates a new form of agent-blind technical debt, as AI agents cannot rely on institutional memory and need systems they can access, interpret, and operate.
Preparing for a Future Dominated by Software
The next banking transformation is not just about adopting AI. It’s about preparing for a market where more financial activity is initiated, interpreted, and coordinated by software. The most competitive institutions will not simply be those with the best apps or the largest AI teams. They will be the ones whose systems can be safely discovered, understood, operated, and audited by AI agents. The next users of banking infrastructure may not be human, and banks that prepare for this shift will be better placed to move faster, serve clients more intelligently, and preserve their relevance in the next generation of digital finance.
The incorporation of AI agents in banking is definitely a game-changer. However, their successful implementation requires the transformation of legacy systems into platforms that AI agents can actually use. These agent-ready systems could revolutionize the financial sector, providing more personalized financial guidance, faster onboarding, more responsive support, and better operational efficiency.
Kruttik Aggarwal, Director of Engineering at Lab49
Source: Here
