Think of a multi-step agentic workflow as a highly skilled and efficient team of digital employees. It is a series of connected steps that are dynamically executed by a team of AI agents to achieve a specific goal. Each agent in the workflow is a specialist, with its own unique set of skills and knowledge. They work together, communicating and coordinating their actions, to complete a complex task that would be impossible for a single AI model to handle on its own.

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For example, a simple task like answering a customer question might be handled by a single AI chatbot. But a complex task like creating a personalized financial plan for a new wealth management client would require a multi-step agentic workflow. This workflow might include:

• An Onboarding Agent that interacts with the client to gather their financial information and risk tolerance.
• A Data Analysis Agent that analyzes the client’s financial data and the current market conditions.
• A Financial Planning Agent that creates a personalized financial plan based on the analysis.
• A Communication Agent that presents the plan to the client in a clear and understandable way.

This ability to break down a complex problem into a series of smaller, manageable tasks and to assign those tasks to a team of specialized AI agents is what makes multi-step agentic workflows so powerful.

The financial services industry, with its reliance on data, speed, and accuracy, is a natural fit for agentic AI. As the World Economic Forum has noted, agentic AI has the potential to transform financial services, promoting greater efficiency and inclusion. Here are a few of the platinum use cases that are emerging:

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1. Autonomous Financial Research and Analysis

Imagine an army of AI research analysts working 24/7 to monitor the markets, to analyze companies, and to identify new investment opportunities. This is the promise of agentic AI. A multi-step agentic workflow could be used to create an autonomous research platform that can read and understand news articles, financial reports, and social media posts; to analyze market data; and to generate detailed research reports with actionable investment recommendations. This would not only accelerate the research process but also provide a level of depth and breadth of analysis that is simply not possible for human analysts alone.

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2. Hyper-Personalized Wealth Management at Scale

Agentic AI can be used to provide a level of personalized service to wealth management clients that was previously only available to the ultra-rich. An agentic workflow could be used to create a personalized financial plan for each client, to continuously monitor their portfolio, and to automatically rebalance it as needed. It could even proactively reach out to clients with personalized advice and recommendations based on their life events and financial goals.

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3. Next-Generation Fraud Detection and Prevention

Fraud is a multi-billion dollar problem for the financial industry. Agentic AI can be used to build more sophisticated and effective fraud detection and prevention systems. A multi-step agentic workflow could be used to monitor transactions in real-time, to identify suspicious activity, and to automatically block fraudulent transactions. It could even initiate an investigation by gathering additional information and alerting the appropriate human personnel.