Silicon Valley is all in on AI agents. OpenAI CEO Sam Altman boldly claims that agents will “join the workforce” this year. Microsoft’s Satya Nadella predicts they will replace certain knowledge work. Meanwhile, Salesforce’s Marc Benioff aims for his company to be the “number one provider of digital labor” through its various AI-driven services.
But amid all this enthusiasm, one glaring issue remains: No one can seem to agree on what an AI agent actually is.
The Hype Around AI Agents
Over the past few years, the tech world has heralded AI “agents” as the next transformative breakthrough. Much like how AI chatbots—such as OpenAI’s ChatGPT—revolutionized information retrieval and interaction, AI agents are being touted as the key to reshaping the way we work.
The idea is appealing. AI agents could automate complex workflows, handle tasks autonomously, and fundamentally change productivity. But the challenge lies in defining exactly what qualifies as an AI agent. The term is rapidly losing clarity, much like other overused AI jargon—think “multimodal,” “AGI,” or even “AI” itself. As more companies incorporate “agents” into their product lines, the lack of a clear, unified definition is causing confusion and, increasingly, frustration among users.
Everyone Has a Different Definition
This definitional chaos has become more pronounced as leading AI firms take vastly different approaches to building their so-called agents.
OpenAI, for instance, recently published a blog post describing agents as “automated systems that can independently accomplish tasks on behalf of users.” Yet, in the same week, its developer documentation defined agents as “LLMs equipped with instructions and tools.” Then, to further blur the lines, OpenAI’s API product marketing lead, Leher Pathak, stated in a post that she views “assistants” and “agents” as interchangeable.
Microsoft adds to the mix by distinguishing between AI assistants and AI agents. According to Microsoft, assistants help with general tasks like drafting emails, while agents are more specialized and tailored for specific expertise—essentially, “new apps” for an AI-driven world.
Anthropic, another major AI lab, acknowledges the ambiguity outright. It defines agents broadly, from “fully autonomous systems that operate independently over extended periods” to “prescriptive implementations that follow predefined workflows.”
Meanwhile, Salesforce provides perhaps the most wide-ranging definition, labeling agents as any system that can “understand and respond to customer inquiries without human intervention.” The company even categorizes agents into six types, from “simple reflex agents” to “utility-based agents.”
Why the Confusion?
Part of the problem is that AI agents are still an evolving concept. Companies like OpenAI, Google, and Perplexity are only beginning to roll out their first versions of agents—such as OpenAI’s “Operator,” Google’s “Project Mariner,” and Perplexity’s AI shopping assistant. Each of these products operates in vastly different ways, adding to the confusion.
There’s also a historical precedent for this kind of definitional drift. According to Rich Villars, GVP of worldwide research at IDC, tech companies have a long history of prioritizing innovation over rigid terminology. “They care more about what they are trying to accomplish rather than adhering to strict definitions—especially in fast-evolving markets.”
Marketing is another culprit. Andrew Ng, the founder of DeepLearning.ai, points out that AI agents and “agentic” workflows used to have clear technical meanings. But over the past year, marketers and big tech firms have co-opted the terms, stretching them to fit a wide array of products and strategies.
The Risk of Undefined AI Agents
This lack of clarity is both an opportunity and a liability. On one hand, the flexibility allows companies to tailor AI agents to their specific needs. On the other, it leads to mismatched expectations and difficulty in measuring real value and ROI.
Jim Rowan, head of AI for Deloitte, warns that without a standardized definition, companies may struggle to benchmark performance and ensure consistent outcomes. “This can result in varied interpretations of what AI agents should deliver, potentially complicating project goals and results. While flexibility can drive innovation, a clearer understanding would help enterprises maximize their AI investments.”
The Future of AI Agents: Standardization or More Chaos?
If history is any guide, the term “AI agent” is unlikely to be universally defined anytime soon—if ever. The same fate befell “AI” itself, which has been stretched to encompass everything from simple machine-learning models to highly complex neural networks.
The lack of consensus might not stop the rise of AI agents, but it does mean that businesses and consumers alike will need to be skeptical of the buzzword. Before investing in an AI “agent,” companies should ask critical questions: What capabilities does it truly have? How autonomous is it? And, most importantly, how does it fit into existing workflows?
Until the industry coalesces around a clearer definition, AI agents will continue to be whatever their creators—and their marketers—say they are. And that’s both the promise and the problem.
