Financial institutions are on the cusp of leveraging artificial intelligence agents to become more streamlined, efficient and automated. 

Whether it’s banks and credit unions, capital markets, insurance, or asset management, the different financial sectors use agentic AI in similar ways.

Agentic AI represents a sophisticated form of autonomous AI that mimics human cognitive processes. These AI “agents” are designed to make decisions, collaborate and adapt independently. By ensuring that agents are focused and trained on very specific tasks, they can operate reliably and securely in an autonomous manner. As they learn and optimize their functions, these agents significantly enhance efficiency, save time and streamline complex workflows that traditionally required human intervention.

Building agentic personas

In the realm of financial services, developing agentic personas is crucial for leveraging agentic AI to its full potential. Agentic personas are digital representations of various roles within an organization, designed to perform specific tasks and interact with both systems and humans in a way that mimics real-life interactions. Think of it as a combination of a job description and standard operating procedures for someone performing a task.

These personas start with a well-defined system prompt, which involves identifying the specific tasks and responsibilities associated with the role, as well as the skills and knowledge required to perform them effectively. Just like human employees, agentic personas need to have a defined personality that aligns with the organization's brand and values. This is especially true for agents that interact with people outside of the organization, including determining the tone and style of communication, as well as the level of formality and empathy the agent should exhibit.

Once their role is defined, additional context on how they execute their role is necessary. Context grounding is essential for various applications within intelligent automation. For example, in the financial industry, it sets the parameters for AI agents' access to databases, standard operating procedures, regulatory information, and other credible sources. This ensures that the agents are always working with the most up-to-date information and understand how to leverage that information in the performance of their tasks.

By grounding AI models with specific business context, organizations can ensure that their AI systems are not only accurate but also aligned with their unique operational needs and regulatory requirements.

After defining the role and personality and providing context for the sources of data, it’s time to define the specific boundaries under which the agent is authorized to operate. This involves feeding the agent with relevant use cases or scenarios to observe its responses. Unexpected responses are used to refine the system prompt to drive proper behavior.

The latest prompt-building tools will assess how robust your use cases are based on the defined role and will recommend additional use cases to help harden the prompt. External black hat agents can also be leveraged to test the boundaries provided to the agent and help refine your system prompts.

As the agent performs its tasks, continuous training and development are essential to ensure that the agent remains up-to-date with the latest information and can handle new and evolving tasks. This involves tracking key performance indicators and gathering feedback from users to identify areas for improvement. Regular evaluation helps to ensure that the agents are meeting their objectives within the overall context of their role.

Orchestrating complex workflows

In the dynamic world of financial services, the ability to model and manage complex, long-running workflows is crucial. Recent advancements in technology have made this possible, allowing organizations to integrate agents, bots and humans into seamless, end-to-end processes.

But managing long-running workflows that touch multiple sub-processes and systems presents several challenges. One of the primary challenges is ensuring that each step of the process is executed accurately and efficiently. This requires meticulous coordination and monitoring at the transaction level to prevent errors and delays. Additionally, the complexity of integrating various systems and tools can lead to compatibility issues and data silos, which can hinder the smooth flow of information.

Providing an orchestration layer enables the seamless integration of various agents, bots and human tasks into a unified workflow. This orchestration ensures that each component of the process works in harmony, reducing bottlenecks and enhancing overall efficiency. By coordinating and monitoring each step, the orchestration layer helps to prevent errors and delays, ensuring that the entire workflow operates smoothly and effectively.

For example, within a loan application, an agent can conduct an initial review of documents, automatically request missing information or clarification from the applicant, and then kick off the next steps, such as a credit check, verification of income and employment, and predicting the likelihood of loan default. Completed packages can then be sent to underwriters for review, and once approved, the agent would immediately generate a loan offer and send it to the applicant.

This transaction-level concierge service advances the transaction through the process as soon as a process step is complete, ensuring no unnecessary delays and prompt execution of each step. The agent only requests human support when needed, allowing human agents to focus on more complex and strategic tasks or providing more time for direct interaction with customers. This approach not only enhances the efficiency of the workflow but also ensures that the overall process is smooth and responsive to the needs of the customer.

One of the most significant advantages of central orchestration is its ability to retain the overall context of what is happening within the process. As transactions seamlessly move between agents, bots and people, the central orchestration ensures that the context is maintained throughout. This means that each component of the workflow is aware of the previous steps and the overall objective, allowing for more informed decision making. This continuity is crucial for maintaining the integrity and efficiency of the process.

This visually orchestrated end-to-end process flow can also provide a level of visibility and accountability that can be leveraged by audit and compliance departments. By having a comprehensive view of the entire workflow, organizations can ensure that every step of the process is transparent and traceable. This transparency not only improves accountability but also allows for better decision making and optimization of processes.

Furthermore, the ability to visualize the entire process flow allows for continuous improvement and optimization. Organizations can analyze the data collected from these workflows to identify bottlenecks, inefficiencies and areas for improvement. By making data-driven decisions, organizations can enhance their processes, reduce costs, and improve overall performance.

The road ahead

As AI technology continues to advance at an exponential rate, the impact of agentic AI is set to grow even more profound. These intelligent agents will become increasingly sophisticated, capable of handling complex tasks and making nuanced decisions with greater precision.

Despite the current landscape of disparate systems and applications in AI, the future holds immense promise. The financial services industry stands on the brink of a transformative era driven by the power of agentic AI.