The truth about AI agents

What do AI agents, big data, and data science have in common?

Everything. They are different incarnations of the same goal: turning data into insights that enhance business decisions using artificial intelligence (AI).

Some of these have been hyped over the years. Big data got hyped over twenty years ago. Data scientist was declared the "sexiest job of the 21st century" by the Harvard Business Review over ten years ago. AI agents shot to fame only last year.

What are AI agents?

AI agents are AI solutions that address a specific business problem. They analyze data and triggers to take actions, sometimes independently of a human.

AI agents are meant to execute processes autonomously. That’s only possible if the business processes and systems are clearly understood and documented. The AI component in the solution is often relatively small in comparison to the complex process automation.

Under the hood, AI agents are basically software systems talking to each other. They often do that through APIs, Application Programming Interfaces, which have been used to exchange information across computer systems since the 1990s. The newly created Model Context Protocol (MCP) is a specific exchange interface for Large Language Models (LLMs). Think of MCP as API for chatbots.

The current hype behind AI agents seems to imply that they became only possible with LLMs, such as ChatGPT or Copilot. This couldn’t be further from the truth. The core of AI agents lies in intelligent process automation. They don’t need LLMs for that.

But it’s also true that AI chatbots improve access to AI solutions and process automation by allowing communication with these systems in natural language. In the technology language, this is called on- and off-ramping. Here, instructions to execute processes can be provided in natural language through chatbots and outcomes can be explained to humans by chatbots. LLMs may add some value for non-technical employees in engaging with AI agents.

Are AI agents the new hype?

Yes and no.

The term “AI agents” surfaced only in 2023 and replaced the GenAI hype last year. So, you are not alone feeling yet another hype wave.

However, many of the AI solutions that are now labeled as AI agents have been used by companies for decades. This includes AI systems such as predictive maintenance in manufacturing, fraud detection in finance, and personalized product recommendations in e-commerce. So far, this was simply called AI.

The AI agent terminology has unfortunately spread now to almost everything that has to do with AI. Experts usually shy away from the latest buzzwords since the new labels don’t change the underlying concepts. However, maybe experts could benefit from adapting their communication to reclaim the AI space.

I was in a similar situation eight years ago.

Back then, I built and led a data science team at a pharma company. We have been developing machine learning solutions to segment customers, optimize marketing investments, and predict revenues. At that time, consultants started selling “AI” to executives while we were developing “only” machine learning solutions (which is one form of AI). So, we changed our approach. Before consultants label the things we have been doing for years as AI and sell it as the new greatest invention, we started labeling our work as AI, rightfully so.

Five years ago we developed a personalized recommendation system. These recommendations were for sales reps: which customers to contact this week, through which channels, and with which content. This AI system learned from the user behavior to refine its recommendations, was leveraging machine learning for optimization, and run autonomously every Monday. Back then this was simply called AI. Today, this is called an AI agent.

Should we adopt AI agents in our business?

It’s easy to get distracted in a space that you just only learned about and that is being continuously hyped in (social) media. This is especially challenging if the terminology changes constantly. It’s hard to distinguish between true innovations and new labels.

AI agents are a new label. However, this doesn’t change the underlying approach of embedding AI solutions in business processes. By doing so, instead of throwing chatbots at employees, companies improve AI adoption.

And there is one benefit that AI agents bring: they require to define upfront what business needs the AI is supposed to address. Instead of simply taking AI as a technology and searching for a problem that it could solve, agentic AI forces companies to clearly describe their business challenges.

Companies who are successful with AI adoption have a clearly defined data and AI strategy in place, created based on their business goals. They understand what AI really is, allowing them to recognize and ignore the latest hype. They don’t chase new labels. Instead, they continue executing their plans to drive AI adoption.

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PS
For a deeper dive into Agentic AI, including more technical aspects and examples, I recommend the book “Agentic Artificial Intelligence” by Pascal Bornet and other AI experts.