AI agents offer tremendous potential in financial services to automate workflows, scale operations, improve decisions, and accelerate product development. And in this rapidly evolving AI environment, you're not alone in asking:
What is an AI agent? How does it operate? How do all the various AI components like Large Language Models (LLMs), retrieval augmented generation (RAG), application programming interfaces (APIs), and reasoning networks tie to agentic AI?
The purpose of this article is to explain AI agents at a high level by simplifying the terminology and underlying mechanics. To illustrate key concepts, we use a simplified metaphor of a self-driving car, highlighting the core components of AI agents and their relevance in business terms, particularly within financial services.
Already today, agents are executing tasks at a speed and scale that was previously thought to be unimaginable. For example, pinpointing opportunities in global markets, conducting thousands of compliance checks, and delivering a complete risk assessment of a novel investment idea.
With agents, AI becomes both a true efficiency tool and an elevator of human capabilities. They also represent a timely opportunity for firms to differentiate—not by replacing people, but by augmenting human talent and creativity with intelligent digital capabilities.
The Brains of the Car: Large Language Models + Reasoning Networks
Every self-driving car needs multiple brains to solve multiple problems, and this require a more complex skillset than the computers in today’s modern cars provide. While current car computers manage functions like cruise control, maps, or braking systems, the brain of a self-driving car must continuously analyze vast amounts of data, make context-sensitive decisions in real time, and handle ambiguous scenarios. It requires sophisticated perception, planning, and reasoning capabilities that far exceed what traditional automotive software can do. For example:
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Where is the car now?
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Where is the car going in the next 5 - 60 seconds?
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What's the long-term destination?
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Which moving objects are of concern and how are they prioritized?
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What are the current weather and daylight conditions?
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What's the weather forecast?
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How is traffic flowing?
At the most basic level, the brains are Large Language Models. They give the system the ability to understand natural language, respond intelligently, and process information with context. The LLMs enable the AI to interpret instructions (like a destination), make sense of complex information (like a road full of signs and signals), and communicate its decisions.
Although the self-driving car metaphor could be solved by a single LLM with a number of long and complex prompts trying to solve many diverse problems as the vehicle tries to safely reach the final destination, there is a better (but more complex) solution.
The metaphor could also be solved by creating a network of specialized units/agents, each addressing a specific challenge. It is easier to design, implement, and upgrade each agent for a specific task, but the tradeoff is added complexity to coordinate their actions. Given what’s at stake with a self-driving car—driver, passenger, and pedestrian safety—this more robust solution is worth it.
That’s where a reasoning network comes in.
A reasoning network refers to a structured system that enables an AI to break down a task into logical units that interact with one another, assess options, and make decisions in a sequenced, context-aware manner. It coordinates inputs from the LLM, external data sources, and environmental signals to determine what actions to take and when. For example, one unit detects a closed road and contacts another unit to find an alternate route.
That’s much like a group of different experts that are self-coordinating or directed by a master LLM as new information becomes available. This layer helps the AI think through sequences of actions and adapt based on changing circumstances.
In business terms
The LLM and reasoning network work together as the intelligence core of your AI system. The LLM serves as the communication and comprehension layer—it interprets instructions, processes natural language, and conveys responses. In financial services, this might mean it interprets a client's query about their portfolio, understands regulatory language, or processes unstructured content like earnings call transcripts.
The reasoning network builds on that by acting as the strategic thinking layer. It interprets a goal (e.g., onboard a client, generate a compliance report, analyze a portfolio), breaks it down into steps, and makes adaptive decisions to achieve it.
Together, they form a dynamic system where the LLM understands what’s needed, and the reasoning network figures out how to get it done.
The GPS with Accurate Real-Time Traffic: Retrieval Augmented Generation
Even a self-driving car with the smartest brains won't operate optimally if it doesn't have a way to access accurate data such as real-time traffic flows, road conditions, construction zones, and road closures.
While the core intelligence (LLM) of an AI can generate language and make decisions based on the knowledge it was originally trained on, it becomes far more useful when paired with up-to-date, relevant information from outside sources.
In a self-driving car, an agentic framework would assign distinct responsibilities to specialized agents. For example, one focused on routing, another on environmental awareness, and another on traffic conditions. Some agents use retrieval augmented generation (RAG) to pull in relevant, real-time data for its domain: The routing agent might access current road closures, the environment agent might detect sudden fog or ice, and the traffic agent might reroute around a jam.
Because each agent is focused on a specific task, its knowledge is more targeted, resulting in better performance on key metrics like accuracy and latency. While the foundational LLM might know the major roads from training, the agentic system ensures it adapts in real time to rapidly changing, high-stakes conditions where static knowledge alone would be insufficient.
An important contextual nuance here is that the LLM can still function independently. RAG simply makes it more accurate, current, and context-aware—especially in the data-centric, fast-moving financial sector.
In business terms
Data accuracy is arguably the No. 1 requirement for financial services firms using GenAI and LLMs. Inaccurate, low-quality, or disconnected data has a cascading effect with implications for strategy, operations, risk management, and compliance. Hallucinations are another cause of inaccurate data and are one of the key challenges around GenAI to resolve in the financial sector.
RAG is the programmatic version of providing context in a prompt and helps ground LLM responses in factual information. Augmenting responses from an LLM with RAG provides a number of benefits:
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No need to re-train the LLM
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Better accuracy and fewer hallucinations since answers are derived from proprietary data
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Improved auditability with the source of an answer
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Enablement of up-to-date knowledge and user-based security
This is what keeps your AI accurate and relevant in financial services. For example, a LLM using RAG within a governed, regularly updated data source will summarize the specific risks to an investment in a company stock and link to each risk’s specific source, such as a 10-Q. It could also provide a view of that company’s financial highlights and keys stats on trading, valuation, and estimates.
More broadly, it could also retrieve the latest market movements, policy updates, or client transaction data before making a portfolio recommendation, flagging a compliance risk, or generating an investment report.
The Controls and Connections: APIs
Now the car needs to take action: Steer through a curve, accelerate onto a highway, slow down at a backup, signal a lane change, or wirelessly pay at a toll booth. It doesn’t just need to understand the situation; it must be able to interact in real time with its environment—and other technology and data systems in that environment—to execute those specific tasks that move it toward its destination and keep you safe.
In AI systems, actions are powered by APIs (Application Programming Interfaces). In the context of a self-driving car, APIs function like the interfaces that connect the car’s brains to physical components and external systems. It’s how software tells the car to turn the wheel, apply the brakes, or communicate with external infrastructure such as electronic toll collection systems, traffic management networks, and cloud-based navigation systems. APIs allow the car to not only perceive the environment but also to act on it, coordinating those actions with other systems in a standardized, reliable way.
In business terms
APIs serve as the backbone for connecting Large Language Models, generative AI, and data management systems like data warehouses and data lakes and are crucial for businesses aiming to leverage AI without overhauling existing infrastructure.
For integrating different content formats into applications, APIs serve as standardized interfaces for structured, unstructured, and file-based data. That enables developers to send and receive data in different formats through specific requests and, in turn, process inputs and manipulate outputs.
That capability connects various media types with application functionalities and ensures generative AI systems can use complex data inputs. Consequently, developers can create more dynamic and versatile applications to meet future data demands.
APIs also unlock the potential for real-time analysis, personalized services, and enhanced operational efficiency. In financial services, this might include APIs that connect an AI system to live trading platforms for market execution, CRM systems for client personalization, or compliance databases to validate regulatory filings in real time.
The Autonomous Driver: AI Agents in Action
Thread together the smart brains, live data, and the ability to interact with systems and you have an AI agent. An AI agent is a self-contained unit that perceives, decides and acts. For example, the weather agent’s goal is to find and/or predict the weather along the car’s path. In addition, it needs to trigger an alert when required.
The AI car is an agentic framework where multiple agents need to work together (agent interoperability), each with a local goal and with a global goal to safely reach the final destination.
Without this integrated, goal-oriented agent driving decisions and execution, a self-driving car would simply be a collection of disconnected smart tools. It's the AI agent that turns all these components into a coordinated, autonomous system capable of reaching its destination intelligently and safely.
In business terms
AI agents will augment and empower financial professionals to fully offload tasks and workflows autonomously. Rather than waiting for instruction, agents can proactively act based on a goal and can perceive the environment in your part of the financial sector.
Agents can think about the right course of action, plan steps, and then learn in a way that’s similar to our human colleagues from natural language training materials. And then they can act on your behalf. Here are three examples of the concept relevant to financial services:
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For junior investment bankers, an integrated AI agent could streamline the due diligence process in a live deal scenario. It could automatically retrieve and analyze the latest financial statements, market sentiment, and regulatory filings about a target company; generate custom deal comps and valuation models; and flag potential legal or reputational risks. This proactive, multi-system orchestration would accelerate the time from pitch to close and free bankers to focus on senior banker support and strategy.
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For portfolio managers, an agent could serve as a dynamic co-pilot across portfolio construction, monitoring, and rebalancing. It could continuously ingest macroeconomic indicators, earnings results, and geopolitical events, assess how they impact sector or asset-class exposures, and propose timely adjustments to align with investment mandates or risk budgets. For example, if inflation expectations rise, the agent could recommend shifting allocations and back it with citations and data drawn from live market feeds.
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For financial advisors, AI agents could enable them to be responsive and personalized at scale while focusing on relationship-building and business growth. For example, an agent could monitor shifts in a client’s financial situation or market conditions, flag potential risks or opportunities, and draft personalized communications with portfolio suggestions or planning reminders. An agent could also alert the advisor if a client's portfolio grows heavily concentrated in one sector, retrieve diversification strategies, and generate a client-ready message proposing next steps.
Conclusion: From Concept to Use
Although you don’t need to build a self-driving car yourself, it’s useful to understand how it functions, where it can take you, and what kind of roads you want it to drive. That analogy is especially applicable in financial services, where the strategic potential of AI agents is substantial.
At scale, agents can reduce the time between insight and action, automate workflows, personalize client service, and enhance the quality of employee collaboration. Their ability to proactively sense, decide, and act makes them well-suited for fast-paced, data-driven environments—fueling the optimism across the financial industry about their transformative value.
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