The ability to create autonomous agents is rapidly evolving, AI agents are emerging as a transformative technology that will provide true assistance in our industries. But without proper standards and guardrails an improperly developed agent is just a mere chain of conditionals or well engineered rules to prompts.

This article will delve into what constitutes an AI agent specifically how it applies to the financial sector, exploring its key dimensions and components and crucially differentiate it from the deceptive practice of "AI Agent Washing."

What is a Financial AI Agent?

An AI agent is an autonomous entity that operates with a given purpose. It independently seeks and utilizes relevant information to understand its environment, making decisions and adapting its behavior. Unlike traditional AI programs that adhere to predefined rules, AI agents learn continuously, rationalize outcomes, and determine subsequent actions to fulfill their purpose. They interact with their environment through effectors to achieve their goals.

Financial AI Agents take this a bit further, where they’re focused on augmenting the productivity of any financial analyst. Their role is to reduce the need for analysts' daily involvement in monitoring and collecting market movements, processing data, and generating reports allowing them to focus on more higher value revenue generating work, reducing their involvement on exhaustive rudimentary tasks.

Dimensions and Components of a Financial AI Agent

To fully grasp the concept of a Financial AI agent, it's helpful to understand the various dimensions and the core components that enable an AI Agents functionality.

Dimensions of an AI Agent

The effectiveness and sophistication of an AI agent can be evaluated across several key dimensions, we will enumerate them here:

• Purpose: The agents fundamental reasoning, the initial logic that will provide the agent with the motivation to and direction to accomplish its goal 
• Autonomy: This refers to the degree to which an agent can operate without direct human intervention. Highly autonomous agents can make complex decisions and execute tasks independently.
• Perception: Leveraging general knowledge(LLMs), agents will make sense of the information obtained to make decisions
• Reactivity: A reactive agent can perceive changes in its environment and respond in a timely manner. This dimension is crucial for agents operating in dynamic or real-time scenarios.
• Proactiveness: Proactive agents don't just react to their environment; they also initiate actions to achieve their goals. This involves planning, goal-setting, and anticipating future states.
• Learning: The ability to learn from experience, seek new information and then adapt to that information, improve performance over time is a hallmark of advanced AI agents.
• Adaptability: This relates to an agent's capacity to adjust its behavior in response to unexpected circumstances or changes in its operating environment.

Components of a Financial AI Agent

The internal architecture of a Financial AI agent typically comprises several core components that work in concert:

• Inputs: These are the input mechanisms through which the financial agent perceives its environment. Examples include financial data APIs, Financial News, Realtime and Delayed financial prices data, Financial data streams.
• Percept: The raw data received from inputs that are processed into meaningful directives, which form and provide the agent with a perceived state and environment
• Outputs: These mechanisms that allow the financial agent to act. This could involve a UI interface or communication module, a financial charting system, financial tool/function, or financial rules engine or alerting system.
• Contextual Memory: Ability to maintain memory from previous interactions in order to make guided decisions obtained from historical knowledge.
• Goal Representation: Financial agents have clear objectives or goals that guide their actions and decision-making processes.

AI Agent Washing: The Deception

As AI agents gain traction specially in the financial sector, a deceptive practice known as "AI Agent Washing" is emerging. This term refers to the misrepresentation or exaggeration of a system's AI agent capabilities to make it appear more advanced, autonomous, or intelligent than it truly is, where prompt engineering and prompt conditionals are miscategorized as learned outcomes, or decisions that are dynamically selected based on the current acquired information.  

vAST(viaNexus Agentic Service Technology)

At viaNexus, we are dedicated to empowering financial institution or individuals with the capabilities to construct truly autonomous Financial AI agents.

vAST (viaNexus Agentic Service Technology), provides a comprehensive suite of tools and resources designed to facilitate the rapid development of such agents by meticulously applying the critical dimensions of AI agents through an easy to implement opensource viaNexus agent SDK that will abastract away the integration of MCP and our extended OAuth2 protocol that allows for humanless agent authentication and authorization.

For instance, attempting to implement an AI Agent that has access to properly entitled and permissioned financial data is not just beneficial but imperative. When developing financial agents it is crucial that they fully embody the established dimensions of an AI agent. This means these agents must exhibit genuine adaptability to the dynamic and often volatile changes within the market. Beyond mere data processing, they should be capable of providing insightful and actionable suggestions on how to effectively navigate and respond to these shifts. This stands in stark contrast to many of today's so-called "AI Agent implementations," which, upon closer inspection, often amount to nothing more than sophisticated, yet fundamentally linear, workflows that can be adequately modeled and executed within a binary decision tree. True AI agents, as championed by viaNexus and enabled by vAST, transcend these limitations, offering a level of autonomy, responsiveness, and strategic thinking that redefines the potential of artificial intelligence.

At viaNexus we're creating and applying the fundamentals of Autonomous Agent design and utilizing vAST to build true autonomous agents that are actively monitoring our platform, the quality of our financial datasets, in addition to building external products and services like our very own Financial Assistant Agent "AskNexus" that provides realtime financial market insights through conversational interactions.

Go to https://vianexus.com and invoke a session with askNexus our Financial Assistant agent, which we ape(vibe) coded in 5min utilizing vAST, and experience how it learns, maintains contextual memory as it discovers its financial data environment to then be able to provide market analysis and insights.

The following video is a short demonstration of these capabilities.

Getting Started and Build your Financial Agent with vAST!

To begin developing your Financial Agent with fully entitled and permissioned financial data, create an account with viaNexus here. After signing up for either our free or paid tier, please contact us. As this product is currently in beta, we are eager to collaborate closely with you on the development of your first autonomous agents.