Navigating the New Frontier: Unlocking Agentic AI Frameworks for Financial Innovation

Financial services firms are now moving beyond conventional automation and artificial intelligence use. They are now actively seeking to implement agentic AI frameworks that streamline decision-making in complex, regulated environments while also reducing costs.

While the current landscape is awash with emerging agentic AI tools, the challenge for these companies lies in understanding what makes a framework truly fit for use in financial services systems.

What Is Agentic AI?

The term “agentic” comes from the word “agent,” which means something that takes an action. In this context, agentic AI goes further than basic generative AI (GenAI) in that it can make decisions or perform tasks autonomously using “agents”. If you ask the agent to do something, it will work to get it done without the need for further prompts or questions. Separate agents can also work together effectively to get things done without needing a human to connect them.

Choosing the Right Agentic Framework for Your Business

The financial services industry operates under strict institutional constraints, which means that regulatory requirements, auditability, and a high bar for reliability must always be front of mind when companies are exploring new tech. When selecting an agentic AI framework, technical decisions must align not only with the latest AI advancements, but also with the unique operational context and compliance mandates of financial firms.

Leading open source agentic AI frameworks include LangGraph, Agno, SmolAgents, Atomic Agents, Pydantic AI, CrewAI, and AutoGen – and each of these frameworks differs significantly in their underlying assumptions about control, autonomy, and abstraction. These differences all directly impact how systems behave in production, handle failures and exceptions, and scale to new demands and regulatory landscapes.

Consider a Comprehensive Approach

When you’re selecting agentic AI tools, you should consider a comprehensive approach that aligns with your strategic goals, operational needs, and ethical values.

There are some key factors that can guide your decision-making process:

1. Define your specific needs and goals
   • Identify the tasks or processes you want the agentic AI tool to improve or automate.
   • Determine whether your focus is on customer service, data analysis, decision support, automation, or something else.
2. Evaluate features and capabilities
   • Assess the agentic AI’s ability to perform required tasks reliably, accurately, and in line with regulatory compliance.
   • Check for features like natural language understanding, learning capabilities, adaptability, and integration options.
3. Ensure that it is ethical and responsible
   • Review the AI’s transparency, fairness, and bias mitigation strategies.
   • Ensure compliance with relevant regulations and standards (e.g., data privacy laws).
4. Consider integration and compatibility
   • Verify how well the AI tool integrates with existing systems and workflows.
   • Prioritize tools that offer seamless compatibility and scalability.
5. Assess the vendor’s reputation and support offering
   • Research the vendor’s track record, customer reviews, and stability.
   • Evaluate the quality of support, training, and updates provided.
6. Analyze cost and ROI
   • Compare costs against expected benefits and efficiency gains.
   • Consider the long-term value, not just upfront expenses.
7. Pilot, test, and test again
   • Implement pilot programs that test agentic AI capabilities in real-world scenarios.
   • Gather feedback from users to identify potential issues or limitations.
8. Focus on adoption and UX
   • Choose tools with intuitive interfaces that facilitate adoption and ease of use.
   • Provide training and support to ensure these tools are utilized properly.
      

By carefully balancing these considerations, businesses can select agentic AI tools that enhance productivity, support compliance and ethics, and align with their strategic vision.

You should also ask architectural questions

When choosing your framework, you should see it as a fundamental architectural decision, not just a matter of developer familiarity or popularity. Your focus should be on core factors like:

• Agency distribution: Determine whether the framework enables granular control over AI agents, or if it favours greater autonomy? In highly regulated settings like financial services, traceability and auditability often require a more centralized or explicitly structured agency.
• Workflow structuring: High-stakes environments demand repeatable, observable workflows. The right framework should support clear workflow orchestration, handle edge cases well (i.e. problems or situations that occur only at an extreme level - maximum or minimum), and allow easy integration with monitoring tools.
• Reliability and explainability: Financial institutions must ensure decision systems can be explained, audited, and, if necessary, overridden. Select frameworks that provide visibility into agent actions, reasoning paths, and escalation protocols.

Compliance Versus Agility

In regulated environments, frameworks like Pydantic AI and LangGraph offer solid support for data validation and transparency, while more experimental frameworks, like SmolAgents or CrewAI, allow greater flexibility but will likely require additional safeguards to align with strict compliance standards. Importantly, it’s not enough for an AI agent to be effective in isolation. The broader success of agentic AI in financial services will hinge on how these systems align with institutional constraints, while at the same time maintaining consistent performance under regulatory scrutiny, and delivering transparent, auditable outcomes.

Selecting an agentic AI framework for financial systems is less about chasing trends and more about thoughtful architectural stewardship. By prioritizing distributed agency, robust workflow structuring, and reliability, financial services firms can lay the groundwork for transformative agentic AI solutions − without compromising trust, compliance, or control.