As the pioneer in creating AI Agents for financial crime compliance, we launched our first AI Agents back in 2022 — long before AI Agents became the trendy talk of tech. With the technology gaining serious momentum (and serious media attention), a slew of industry pundits, consulting firms, technology providers, and practitioners across industries have put forth a wide range of definitions for what Agentic AI and AI Agents actually are. But with no single voice dominating the thought leadership, the market definition has always been—and remains—extremely fluid.
We experience that fluidity every day. Our AI Agent customers query us for a single definition as they seek to build business cases to support the broadening of their deployments. Prospects hear varying definitions from multiple sources and are often confused by them, slowing everybody’s progress. Vendors promote the AI or tangential AI features within their own products and say, “That’s Agentic AI.” It all serves to keep the media and industry analysts on their toes as they try to make sense of it. And practitioners are left uncertain about how to define their associated programs and desired outcomes.
To remedy the situation, we would love to put a stake in the ground and say, “This is what Agentic AI is, and here’s what an AI Agent does.” But before we plant that stake, you should recognize that it will do little to bring broad market clarity among the cacophony of voices putting forth their own definitions.
So, where does this leave organizational leaders who want to gain the benefits delivered by AI Agents? If you are one such person, our advice is to identify your use case, define your needs, and find a technology provider whose definition and solutions align with those needs. For example, our AI Agents are purpose-built to handle the costly, tedious, and error-prone work that has plagued financial crime compliance organizations for years. They augment traditional teams by performing highly skilled and decision-centric work in anti-money laundering (AML) and sanctions compliance operations areas. As such, we define our AI Agents this way:
AI Agents are digital co-workers that decide, act, and communicate. WorkFusion AI Agents are also pre-built, explainable, and controlled.
If that definition is close to how your organization needs it to be, then we have a great place to start.
The Wide Range of Definitions
A small sampling of definitions for Agentic AI and AI Agents in recent media illustrates the challenge.
In this recent CIO.com article, two seasoned AI practitioners put forth this broad definition:
“Unlike traditional software that executes predefined instructions, agentic systems make adaptive, autonomous decisions grounded in reasoning.”
Yet, within the same article, support for that position comes in the form of Carnegie Mellon’s analysis of the situation. In it, Carnegie Mellon replaces “autonomous” with “semi-autonomous.” That singular change in the definition is quite significant. Here’s why:
If an organization pursues Agentic AI or AI Agents based on believing they are fully autonomous, the entire approach to what they do, how they do it, and the appropriate solutions for them change dramatically. For example, risk-averse organizations (like traditional banks) who hear “fully autonomous” will likely run to the hills, shouting “down with AI!!” because it lacks the controls they need for regulatory approval. For them, the most independence they would ever give a virtual agent would be semi-autonomous actions and decision making. At the other end of the spectrum in the financial services industry would be envelope-pushing FinTechs that seek to automate as much as possible to optimize speed and efficiency. They hear “autonomous” and run toward such a solution, hoping to speed operations to win the customer experience race and sign-up new customers before speedy and nimble competitors do.
Other prominent AI industry voices have devised a different way to define Agentic AI and AI Agents. They currently state that Agentic AI can have multiple definitions. That’s not really putting a stake in the ground. But unfortunately, that’s where the broad market finds itself here at the halfway point of 2025.
For example, Anthropic states that “Agent” can be defined in several ways. Here is their position: “Some customers define agents as fully autonomous systems that operate independently over extended periods, using various tools to accomplish complex tasks. Others use the term to describe more prescriptive implementations that follow predefined workflows. At Anthropic, we categorize all these variations as agentic systems, but draw an important architectural distinction between workflows and agents:
- Workflows are systems where LLMs and tools are orchestrated through predefined code paths.
- Agents, on the other hand, are systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks.
To understand how this multiple-choice definition brings absolutely zero definition clarity, consider this: WorkFusion’s AI Agents align very closely with the first Anthropic bullet point above. That would lead you to believe that we provide workflows, not AI Agents. Yet, AI agents are what we’re all about. The reason we don’t align with the second bullet is that we don’t allow our LLMs to run independently wild. Instead, we leverage LLMs in a very controlled manner and never cease control to them. As you can see, a definition blur confuses potential practitioners.
In the world of management consulting, the definitions grow even more plentiful. According to McKinsey, “An AI agent is a software component that has the agency to act on behalf of a user or a system to perform tasks. Users can organize agents into systems that can orchestrate complex workflows, coordinate activities among multiple agents, apply logic to thorny problems, and evaluate answers to user queries.”
Fair enough. We’re on board with that. But shortly following that definition, McKinsey puts forth what they term “a nonexhaustive list of some of the agents being created today”:
- Individual augmentation (“copilot” agents). These agents serve as copilots for individual users, with the intention of augmenting that person’s productivity and capabilities.
- Workflow automation platforms. This type of agent focuses on automating single or multistep tasks or smaller workflows, serving as an AI-powered process orchestrator and executor for existing workflows.
- Gen AI–native agents for domain solutions. These agents are purpose-built solutions for specific business domains or functions. Examples include AI-driven customer service systems or AI-enabled software development pipelines. Gen AI–native agents reimagine a particular domain with AI at the core of the solution, rather than traditional AI agents, which layer AI onto existing roles or workflows.
- AI-native enterprises and operating models. These agents are woven throughout the enterprise operating model, instead of being applied to individual workflows or functions. In these cases, a company undergoes an end-to-end AI-first redesign where the interaction layer, processes, organizational structures, and even the business model are reimagined.
- AI virtual workers. AI virtual workers are agents that function as employees or team members and represent the most potentially disruptive category of agents. These virtual workers could enable companies to sidestep full organizational transformation by allowing AI to operate within the company’s current model, which might help capture value more quickly.
These AI agents are not mutually exclusive. Many organizations will pursue a mix—for instance, rolling out personal AI copilots while automating select workflows and piloting a few virtual workers.” That’s for sure. In fact, considering the five types of AI Agents McKinsey lists above, WorkFusion AI agents are clearly a combination of number 2 and 5—AI virtual workers and workflow automation.
So, essentially, there are so many definitions that organizations freeze up for extended periods of time as they struggle to define what they need, how they need it, and whether or not AI Agents are how they get there. This brings us back to our original statement: Define your needs and find a technology provider whose definition and solutions align with those needs.
To understand why so many top US banks and financial institutions around the world use WorkFusion AI Agents to optimize their FinCrime compliance operations, request your own personal demo today.
ai agents,AML,BSA/AML compliance,FinCrime
