As the adoption of AI agents accelerates, definitions remain fragmented. WorkFusion, which introduced its first AI agents for financial crime compliance in 2022, has seen firsthand how inconsistent terminology complicates deployment and decision-making. Despite the rapid rise in popularity, there is still no single, unified understanding of what AI agents or Agentic AI actually mean.
According to Workfusion, this ambiguity has led to confusion among clients and prospects alike. Many approach vendors hoping for a clear definition, only to encounter conflicting narratives. Some vendors claim their AI-enabled tools qualify as Agentic AI, adding to the noise. As a result, practitioners and decision-makers often struggle to define programmes, measure impact, or build internal consensus, slowing down the adoption process.
While WorkFusion would welcome setting a definitive standard, the company acknowledges that any single definition is unlikely to break through the industry-wide cacophony. Instead, it encourages business leaders to start with their use case, then find a provider whose solution fits that need. WorkFusion’s own AI agents focus on augmenting compliance teams in areas like anti-money laundering (AML) and sanctions operations, with agents designed to be explainable, controlled, and purpose-built.
Recent media coverage has only deepened the definitional divide. A CIO.com article described agentic systems as autonomous tools capable of reasoning-based decisions—until Carnegie Mellon clarified them as “semi-autonomous,” which shifts their risk profile significantly. This single word can determine whether a traditional bank backs away from implementation or a FinTech firm eagerly embraces the tech.
Anthropic’s multi-tiered explanation of agentic systems adds further complexity. They define agents as either autonomous, adaptive entities or structured workflow tools driven by LLMs. WorkFusion aligns more with the former but maintains tight control over its systems, distancing itself from fully independent AI decision-making.
McKinsey introduces yet another perspective, outlining five categories of AI agents—from productivity copilots to AI-native operating models and virtual workers. According to McKinsey, these models are not mutually exclusive. WorkFusion positions itself between categories two and five, combining workflow automation with AI virtual workers.
Ultimately, the industry’s struggle with defining AI agents risks slowing progress just when adoption is most needed. For now, organisations are best served by grounding their AI strategy in specific, practical goals—and working with vendors whose definitions and offerings match those ambitions.
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