AI agents represent a shift from conversational AI toward systems that can pursue goals across multiple steps. Instead of waiting for a user to provide every instruction, agents can reason about a task, interact with software, retrieve information, make decisions within defined limits and return results. The transition could reshape office work, software, customer service and digital commerce while creating new questions around oversight and accountability.
The first generation of mainstream generative AI taught millions of people to communicate with machines through a chat window. The next generation is increasingly designed to do the work itself.
AI agents are being developed to move beyond answering questions and generating individual pieces of content. They can break larger objectives into smaller steps, interact with software tools, access information and continue working toward a goal with less continuous human direction.
MIT research on agentic AI describes these systems as a new class of technology capable of perceiving, reasoning and acting with varying degrees of autonomy. Unlike traditional chatbots, agents can connect their reasoning to actions in external systems. 0
From answering to acting
A conventional chatbot generally waits for a prompt and responds. An agent starts with an objective and can determine which steps are necessary to reach it.
For example, a user could ask an agent to research several suppliers, compare their prices, organize the information in a spreadsheet and prepare a recommendation. Rather than requiring the user to direct every individual action, the agent can coordinate the workflow itself.
This distinction is important because the value of agentic AI is increasingly measured not only by how well a model generates text, but by how reliably it can complete a sequence of real-world digital tasks.
Digital workers inside companies
Businesses are beginning to explore agents as digital members of work teams. MIT's Center for Information Systems Research describes "digital colleagues" as AI-enabled systems that can collaborate with humans on complex work, operate continuously and escalate consequential decisions to people. 1
Such systems could handle workflows that previously required employees to move repeatedly between different applications. An agent might read incoming requests, update a customer database, prepare documents, check internal information and notify a human when a decision requires approval.
The important change is that the AI is no longer confined to one narrow interface. It can potentially operate across the software environment in which the work takes place.
Companies are already experimenting
MIT Sloan research published in 2026 found that more than one-third of surveyed organizations were already deploying agentic AI systems, while another 44% said they planned to do so. The research was based on a global survey of more than 2,000 respondents. 2
The applications range from software development and research to customer operations, finance and administrative workflows. The common feature is that the system is given a broader objective rather than being used only for a single response.
Companies are therefore having to rethink the boundary between automation and human work. Traditional automation usually follows predefined rules, while agentic systems can respond to changing circumstances and select different actions as they work.
The rise of software that can use software
One of the most important developments is the ability of agents to interact with other digital systems. Instead of simply describing what a person should do, an agent can potentially open an application, enter information, search a database, call an API or manipulate a document.
This creates a new layer of automation. The AI model becomes a decision-making component that coordinates existing software rather than replacing every individual application.
It also means that the consequences of an AI mistake can become more significant. A wrong answer in a chat window may simply be ignored. A wrong action performed automatically inside a company's systems can create financial, operational or security problems.
Human oversight becomes harder
Greater autonomy does not eliminate the need for humans. Instead, it changes where humans participate in the workflow.
Instead of checking every individual action, employees may increasingly define objectives, establish permissions, review important decisions and intervene when an agent encounters an unusual situation.
That creates a difficult balance. MIT Sloan research identifies supervision versus autonomy as one of the central tensions organizations face: excessive supervision can reduce the value of autonomy, while insufficient oversight can introduce unpredictability and risk. 3
What happens to human jobs?
The emergence of digital workers does not automatically mean that entire occupations disappear. Many jobs consist of numerous tasks, and agents may initially automate particular parts of those jobs rather than replacing the people who perform them.
At the same time, the economics of automation are changing. MIT research has highlighted a growing gap between the falling cost of AI automation and the continuing cost of human verification. That creates pressure to redesign how work is divided between machines and people. 4
Some workers may spend less time executing routine digital tasks and more time setting objectives, reviewing outputs, handling exceptions and making decisions that require context or responsibility.
AI agents could change the internet itself
Agentic AI could eventually affect more than workplaces. Agents are already being developed to interact with digital marketplaces, including systems capable of buying, selling and negotiating on behalf of users. MIT researchers have noted that this could require companies to redesign digital platforms for machine users as well as humans. 5
That could change how websites, online stores, advertising systems and financial services are designed. Instead of millions of people individually navigating digital interfaces, software agents could increasingly perform those interactions on their behalf.
The result could be an internet where machines become an increasingly important class of user.
The next phase of AI
Chatbots introduced people to generative AI through conversation. Agents are extending that model into action.
The technology is still developing, and reliability remains a major limitation. Agents can make incorrect assumptions, misunderstand objectives or take inappropriate actions. For organizations, deploying them therefore requires technical controls, security boundaries, monitoring and clear responsibility for outcomes.
But the direction is increasingly clear: AI systems are being designed not only to tell people what to do, but to carry out parts of the work themselves.
Conclusion
The transition from chatbots to autonomous digital workers represents a fundamental change in how artificial intelligence can be used. Instead of treating AI as a tool that waits for instructions, companies are increasingly exploring systems that can pursue goals, coordinate multiple steps and operate across digital environments.
The technology could make many workflows faster and more automated, but it also moves responsibility into a more complicated area. The more actions an AI system can take independently, the more important it becomes to determine what it is allowed to do, when a human must intervene and who remains accountable for the final result.
