AI Agents Explained: How They Work and Where They're Headed

The rapid adoption of AI agents has outpaced public understanding of what these systems actually do. According to a November 2025 study by MIT Sloan and Boston Consulting Group, 35 percent of surveyed businesses have already deployed AI agents, with another 44 percent planning implementations.

Phillip Isola, an associate professor at MIT's Computer Science and Artificial Intelligence Laboratory, offers clarity on a technology category often obscured by marketing terminology.

The core distinction

The fundamental difference between generative AI and agentic AI comes down to action versus creation. While systems like ChatGPT generate text, images, or code, AI agents execute tasks in digital or physical environments—booking flights, manipulating robotic arms, or navigating customer service workflows.

"Agentic AI is AI that takes actions in the world," Isola explains. Most agents today are digital, built by wrapping foundation models like Claude with application-specific tools and memory systems. A customer service agent might access a company's financial database and past negotiations; a coding agent might have calculator functions and file system access.

The training data problem

The biggest technical challenge isn't the underlying AI model—it's the scarcity of training data that demonstrates how to complete real-world tasks. Teaching an agent to book a flight requires detailed examples of mouse movements, button clicks, error recovery, and negotiation strategies. Such datasets barely exist.

This forces developers toward trial-and-error training, where agents interact with live environments and learn from outcomes. Coding agents have succeeded with this approach because code solutions can be automatically verified. Other domains lack such clear feedback loops.

Why it matters

The ease of deploying AI agents creates a verification gap. When agents can generate code or complete tasks with minimal human effort, organizations may skip rigorous checking—introducing bugs, leaking private data, or making decisions based on vague instructions. The risk extends beyond technical failures to potential de-skilling, where reliance on agents erodes human capabilities before the technology is mature enough to fully replace them.

Isola notes that high-stakes domains like medicine, security, and strategic business decisions may not be ready for full automation, regardless of technical capability. The balance between assistance and automation remains a judgment call.

The architectural question

Current AI agents are language models at their core, trained primarily on text. Isola points to a fundamental debate shaping the field's future: Will the next generation of agents simply be language models equipped with sensors and tools, or will they require entirely new architectures designed to handle video, physical forces, time-series data, and other continuous, high-dimensional inputs?

"Is the next wave of AI just going to be Claude with sensors, actuators, and tools, or is it going to be something built in a new way from the ground up?" Isola asks. "That's the big question a lot of people in AI are grappling with right now."

The answer will determine whether today's agent deployments represent the foundation of future systems or merely an intermediate step toward fundamentally different technology.

These details were first reported by MIT News in an interview with Phillip Isola.