Agentic AI is becoming a practical business tool, not just a research idea. A November 2025 report by MIT Sloan School of Management and Boston Consulting Group found that 35 percent of surveyed businesses had already deployed AI agents, while another 44 percent planned to implement agentic AI soon.
That fast adoption raises a simple question with complicated consequences: what exactly is an AI agent, and where should people trust it to act?
What makes agentic AI different
Phillip Isola, an associate professor in the Department of Electrical Engineering and Computer Science (EECS) and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL), describes agentic AI as AI that takes actions in the world. Those actions can be physical, such as robotic manipulation, or digital, such as booking a flight.
That is the core distinction from generative AI models like ChatGPT and Claude. Generative AI is commonly used to create stories, poems, art, images, and other outputs. Agentic AI is framed around doing something for a person, a business, or a system.
The term agent is not a precise technical boundary. In Isola’s explanation, it usually means AI built to help people interact with an application, a website, or the physical world. Most agents people encounter today are digital agents, including customer service agents that handle product complaints.
Under the surface, many agents are not entirely new kinds of intelligence. Most companies offering agents use the same few AI models under the hood, then give those models ways to act and remember what happened. A foundation model such as Claude can sit at the center, while a company adds a product-specific wrapper around it.
Those wrappers matter. An agent might receive access to a calculator so it can solve math problems. In a more complex application, it might receive access to a hard drive and operating system so it can remember a firm’s financial data and past business negotiations.
The training problem behind useful AI agents
The promise of agentic AI is easy to understand when the task sounds ordinary. Booking a flight, for example, appears simple to a human who has done it before. For an AI agent, the challenge is that the needed training data is limited.
To complete that kind of task, an agent may need to know where to move the mouse, which buttons to click, what to do when something goes wrong, and even how to call somebody and negotiate about the price of the airline ticket. The source material emphasizes that there is not a large supply of data spelling out those steps in the exact way an agent would need.
One possible training path is trial and error. An AI agent can visit airline websites, try actions, and learn from what works and what fails. But these environments are hard to model, which means learning by trial and error can become an important part of the process.
This helps explain why agentic AI is not only about adding a button to a chatbot. The system needs tools, memory, feedback, and enough structured experience to act usefully when the situation changes.
Why coding agents are ahead
According to Isola, coding agents are the area where agentic AI has seen the most success. The reason is closely connected to how generative AI developed. Language models were trained on code, and that gives them a basis for predicting what a human might do to solve a programming problem.
Coding also provides a useful feedback loop. An agent can try different solutions, check whether it got the answer right, and keep iterating until it finds a better strategy. As long as the system can check the answer, trial and error becomes a productive method rather than random experimentation.
That makes coding a clearer fit than many open-ended real-world tasks. The agent can generate code, test an approach, observe failure, and revise. In other areas, the right answer may be harder to define or verify.
Still, Isola points to a larger balance that applies beyond coding: automation is not the same as assistance. Analytical AI methods can help predict possible outcomes of decisions without being agentic. These systems can inform human decision-makers even when they are not taking action themselves.
That distinction becomes especially important in high-stakes or safety-critical settings. The source names medicine, security, and high-level business policies as examples where the technology might not be ready to fully automate processes, or where people may not be comfortable allowing it to do so.
The risks come from ease, not only errors
One risk of AI agents is that they can make hard work feel too easy. With coding agents, people can vibe code by asking an agent to make code for them instead of doing the hard work themselves.
The danger is not only that an agent might be wrong. It is that people may fail to put enough effort into checking whether the agent is doing the right thing. The source specifically warns that bugs will be introduced and private data will get leaked, and says this is already happening.
Agents can fail because they are not well-trained and do not know what to do. But even a competent agent can make mistakes when a human uses it poorly or gives an instruction that is too vague. In that case, the error may begin with the human but be amplified by the system’s ability to act.
There is also the issue of de-skilling. If people rely on agents to do homework, coding, and math, they may lose the ability to do those tasks themselves. The concern is sharper because the technology is not yet ready to fully automate all of those processes.
The open question for the future
Today’s agentic AI often means large language models using tools to interact with digital and physical systems. That setup has an obvious limitation: the underlying architecture is still that of a language model trained on text data.
More powerful AI agents may require models that can handle more than language. Isola points to videos, physical forces, time series, radar scans, and other modalities. He also raises the possibility of fundamentally different architectures that can work with continuous data, high-dimensional data, stochastic data, and related challenges.
There is another possibility. A very strong coding model might act as a puppeteer for sensors, actuators, and web APIs. A reasoning system that understands math, language, and code might be given a camera and a keyboard and learn what to do in the spatial domain.
That leaves the central question: will the next wave of AI be Claude with sensors, actuators, and tools, or will it be built in a new way from the ground up? For now, agentic AI is advancing quickly, but its most important design choice may be deciding when it should act and when it should keep humans firmly in the loop.