As the discussion around AI agents continues to dominate the tech world, one thing remains stubbornly unclear—what exactly is an AI agent? For organisations looking to deploy AI agents for compliance, productivity, or innovation, the absence of a unified definition creates more confusion than clarity.
According to Workfusion, since launching its first AI agents in 2022, WorkFusion has witnessed first-hand the uncertainty in the market. With consulting firms, media outlets, and vendors all offering their own take, the definition of agentic AI remains fluid.
This ambiguity often stalls decision-making, with prospects unsure about how to align tools with business goals. Vendors aren’t helping either—some label any feature with an AI component as “agentic”, leading to further misinformation and delays.
Rather than declaring a universal definition, WorkFusion urges organisations to reverse-engineer the problem: identify the use case first, then find a solution that fits. Their AI agents are tailored for financial crime compliance, especially in the areas of anti-money laundering (AML) and sanctions monitoring. These tools take on complex, decision-oriented tasks that were once the exclusive domain of human teams. The company defines its AI agents as “digital co-workers that decide, act, and communicate”—and stresses that they are explainable, controlled, and pre-built.
Even as thought leaders attempt to solidify definitions, contradictions persist. In a recent CIO.com article, one definition highlights AI agents as autonomous systems, while Carnegie Mellon refines this to “semi-autonomous”—a small word change with major implications. Banks and highly regulated institutions may fear the idea of full autonomy, while agile FinTech firms might embrace it in pursuit of speed and scale.
Big AI players aren’t aligned either. Anthropic, for instance, acknowledges multiple interpretations of “agent”. Some see agents as autonomous problem-solvers; others describe them as tools executing defined workflows. Even Anthropic themselves draw lines between “workflows” and “agents”, yet blur them in practice. WorkFusion aligns more with structured, managed agents—contradicting interpretations that equate full LLM autonomy with agency.
McKinsey introduces even more categories—from copilots and workflow platforms to AI-native workers embedded throughout an enterprise. WorkFusion identifies most with the automation and virtual worker models, stressing real-world applications over philosophical debates.
The takeaway? Definitions are in flux. To make meaningful progress, organisations must define their problems clearly and partner with providers whose AI agent philosophy aligns with their operational and risk tolerance needs. WorkFusion believes the focus should remain on function, control, and transparency—rather than a catch-all definition that changes with every analyst’s report.
Copyright © 2025 RegTech Analyst
Copyright © 2018 RegTech Analyst
