When People Say “The Future Is Agentic,” Here’s What They Mean

You've probably heard people say that the future of AI is agentic. It's become a common phrase in conversations about artificial intelligence, although I'm not sure everyone using the term understands what it means, and I suspect plenty of people hearing it aren't entirely sure either.

If your experience with AI has mostly involved ChatGPT, Claude, or Gemini, you might be wondering what makes an AI agent different from the AI you're already using. Is it a different kind of AI model? Does it have more intelligence? And what exactly does it mean when someone says an application has agentic capabilities?

These are worth understanding because AI agents have already become an important part of how artificial intelligence is being used. For anyone who wants to get more out of AI, understanding how agents work is a good place to expand your knowledge beyond the familiar chat window.

Most of us were introduced to generative AI through a fairly straightforward interaction. We type something into a chat box, the AI generates a response, and we continue the conversation. We might ask it to explain something, help write an email, analyze information, or work through an idea. Even as the models have improved, that conversational experience has remained familiar.

AI agents allow us to give those same systems considerably more responsibility.

For example, you could ask a chatbot to help plan a vacation, and it might recommend destinations or put together an itinerary based on the information you provide. With an AI agent that has the appropriate tools and permissions, you could assign a broader goal. It might research destinations, compare actual flight options, look into hotels, and organize everything into a document you can review. If something doesn't work along the way, it can attempt another approach without requiring you to explain every step.

You can also give agents responsibilities that continue over time. Imagine having one research developments in your industry every week, determine which stories deserve your attention, and prepare a briefing that is waiting for you on Monday morning.

In both cases, you're giving the AI something to accomplish and allowing it to work through the process.

An AI agent is generally a software system that uses an AI model to pursue a goal, make decisions about what to do next, and take actions using the tools available to it. As it works, it can evaluate the results of those actions and adjust its approach. Depending on how the system is designed, it might complete a task in a few minutes or work through a much larger project over an extended period.

That brings us to the word agentic.

When someone describes an AI system as agentic, they're usually referring to its ability to take actions and exercise some independence in deciding how to accomplish something. There are varying degrees of this capability, which is why the terminology sometimes gets confusing.

A chatbot that can search the internet already has access to a tool that allows it to do something beyond generating text. If that same system can determine when it needs additional information, search several sources, compare what it finds, and continue investigating based on the results, it's demonstrating more agentic behavior.

Give it access to a computer where it can interact with software and create files, and the range of possible actions expands considerably. If it can remember information from previous sessions or start working automatically on a schedule, we're getting closer to what people generally have in mind when they describe a personal AI agent.

 One useful way to think about AI agents is that you can give them an outcome you want to accomplish and let them work through the steps, rather than having to direct every individual action yourself.

There is another concept that helps explain why these systems can behave so differently from the AI chat applications we're accustomed to using. It's something called an agentic harness, and I think understanding it makes much of what we're seeing in AI today easier to follow.

The AI models powering ChatGPT, Claude, and Gemini provide the intelligence that interprets instructions and reasons through problems. But those models operate inside software that determines which capabilities are available to them and how they can interact with the outside world.

That surrounding software is commonly called a harness.

A harness can connect an AI model to a browser or give it access to a computer environment where it can run programs. It can manage the model's interaction with external tools, maintain information about the work in progress, and allow the model to continue making decisions based on the results of previous actions.

Imagine taking an AI model that you're accustomed to using inside a chat window and putting it inside an environment where it can operate a computer. It might now be able to work with documents, navigate websites, interact with applications, or execute code. Add a scheduler, and it can begin certain tasks automatically. Give it an approved way to communicate with you through your phone, and you can assign work or check on its progress without sitting at your desk.

The underlying AI model may be the same one you've been chatting with all along, but the surrounding software has changed what it's capable of doing.

Of course, the model's reasoning capabilities still play an important role. Some models are better at working through complicated problems or recognizing when something has gone wrong. But a considerable amount of what makes modern agents useful comes from the environment they're operating in and the tools their developers have made available.

Understanding this also helps explain why so many companies can offer different agent experiences, sometimes using similar underlying AI models.

How we got to today's AI agents

Several of the technologies that made modern agents possible were already developing before AI chatbots became part of everyday life.

Automation platforms such as Zapier, Make, and n8n have allowed people to connect applications and create workflows for years. As generative AI became more accessible, people began incorporating AI models into those automations.

You could create a workflow that monitors incoming emails, sends the contents to an AI model for analysis, and uses the results to determine what happens next. It might organize information into a spreadsheet or generate a response for someone to review.

These kinds of systems can be useful, although many operate along a fairly specific path that someone designed in advance. I tend to think of them as putting AI on rails. You establish the workflow and the conditions that determine which direction it takes.

More autonomous agents have greater flexibility in deciding how to reach a goal, including what to do when something unexpected happens.

Meanwhile, the major AI chat applications were gaining access to tools of their own.

In 2023, OpenAI introduced capabilities such as web browsing, plugins, and Code Interpreter into ChatGPT. Those additions allowed the AI to retrieve current information, interact with external services, and execute code within a controlled environment.

In 2024, computer use became another major area of development. Anthropic demonstrated how Claude could interact with a computer visually, using a screen and performing actions such as clicking and typing.

Giving an AI system the ability to operate a computer opened up possibilities that went well beyond the integrations developers had specifically built for it. An agent could potentially interact with ordinary websites and software through their existing interfaces, much as a person would.

By 2025, products such as Manus were attracting attention for showing what a more general-purpose autonomous agent could accomplish when it had its own computing environment. You could give Manus a broader objective, and it would attempt to work through the necessary steps using its available tools.

At roughly the same time, coding agents were making substantial progress.

Claude Code and OpenAI's Codex demonstrated how AI could work directly inside software projects. Google's Antigravity brought similar agentic capabilities into a development environment where agents could work with code and use a browser.

Coding was particularly well suited to this kind of AI because the agent could make a change, execute something, inspect the result, and decide whether additional work was needed. The tools used by programmers already provided much of the feedback an agent needs to work through problems.

But people were also realizing that these capabilities could be useful for much more than writing software.

Claude Code, for instance, became the foundation for Anthropic's Cowork capabilities, which brought similar computer-based agentic work into a product intended for general productivity.

Another development that attracted considerable attention was an open-source personal AI agent called OpenClaw.

OpenClaw went through several names, including Clawdbot and Moltbot, before settling on its current name. The naming history was entertaining, but what interested people was the ability to run a persistent AI agent that could interact with their own computing environment.

With OpenClaw, users could connect an agent to messaging applications and communicate with it remotely. The agent could use tools and perform scheduled work, depending on how it was configured. You could send it instructions from your phone and have it work on something while you were away.

Hermes, another open-source agent system, has developed similar capabilities around persistent memory and personal automation.

The OpenClaw community also inspired Moltbook, a social network designed for AI agents to interact with one another. It was an unusual experiment, although perhaps we shouldn't be surprised that even AI agents eventually ended up with their own social media.

What interested me more about these developments was how they brought together capabilities that had previously been spread across different types of AI software.

You could have an agent with access to a computer that could remember information about your work and remain available through a messaging service. It could be given recurring responsibilities and use external tools to accomplish them.

That combination brought the idea of a personal AI agent much closer to something ordinary people could imagine using.

What makes a personal AI agent useful?

We're now seeing companies bring different versions of these capabilities into products such as Claude, Gemini Spark, Meta Muse, and Grok Bot. Some are designed around productivity, while others place greater emphasis on persistent personal assistance or computer-based work.

The systems differ in how they operate and what permissions they require, but they share many of the architectural ideas that have been developing across the agent ecosystem.

If I'm going to have a personal AI agent that handles a wide range of responsibilities, there are several things I would want it to be able to do.

I would want it to access the internet and work with the applications I already use. Ideally, it would have a computing environment where it could manage documents or execute programs when necessary, with appropriate restrictions on what it could access.

I would also want to communicate with it from my phone. If I start something while sitting at my computer, I should be able to check on it later without having to return to that machine. For certain kinds of work, it would be useful for the agent to keep running even after I close my laptop.

Connecting an agent to outside applications is another important part of the picture.

One term you'll increasingly encounter is MCP, which stands for Model Context Protocol. It's a standard that allows AI applications to connect to external tools and sources of information. Plugins, APIs, and other connectors provide additional ways of expanding what agents can access.

You may also hear people talking about giving agents skills. A skill typically contains instructions that teach an agent how to perform a particular kind of work. You might create one explaining how you prepare a weekly report, organize research materials, or carry out some process that's specific to your job.

Some systems let you customize the agent's communication style and behavior through persistent instruction files, including files such as SOUL.md. Memory can help an agent retain useful context about your preferences and ongoing projects, subject to the controls and limitations of the system you're using.

Scheduling brings another level of usefulness. You may have heard the term cron job, which is simply a way to run something automatically at a specified time.

With an AI agent, that could mean assigning a recurring task once and allowing the system to perform it whenever it's scheduled. Perhaps you want a daily briefing, a weekly review of information related to your work, or a report that gets updated regularly.

And because agents can take actions on your behalf, security becomes an important part of deciding how to use them.

An agent with broad access to your everyday computer could potentially reach sensitive files and personal accounts. It might make an incorrect decision or encounter malicious instructions while browsing the web. This is one reason we're seeing more systems use isolated cloud computers or virtual machines where the agent operates with controlled access.

You should also be able to determine which actions require your approval. Letting an agent research hotel prices is one thing. Allowing it to spend money, send messages, or make changes to important accounts deserves a different level of oversight.

These considerations are becoming more relevant as agentic capabilities are added directly to familiar AI applications.

You might be using an AI chatbot to ask questions one moment and, during the same conversation, assign it a task that involves several tools and continues working while you're away.

From the outside, the experience may look almost identical because you're still communicating through a chat window. Much of the difference comes from what the system is capable of doing after receiving your instructions.

For anyone who already uses ChatGPT or another AI chatbot, I think understanding agents and the software behind them is one of the most useful areas to explore next.

You don't necessarily need to learn programming or build your own agent from scratch. But understanding how models connect to tools, how an agentic harness operates, and why memory and scheduling matter will help you make sense of the growing number of AI products offering these capabilities.

When you see a demonstration of an AI agent, pay attention to what it can actually access and how it accomplishes its work. Find out whether it can continue independently, how much supervision it requires, and what controls are available when something goes wrong.

Those details will usually tell you far more about the agent than the product description.

When people talk about the future being agentic, they're describing the growing role of AI systems that can take responsibility for accomplishing work with varying levels of independence. That development is already well underway, and we're now seeing those capabilities move into products that more people can use.

For me, understanding agents changes how I think about the opportunities AI presents. Once you recognize that AI can operate software and carry out work over time, you begin noticing responsibilities in your own routine that might be worth delegating.

And that's probably a useful question to leave you with.

If you had a personal AI agent that could use a computer, work with your applications, and handle recurring responsibilities, what would you want it to take care of for you?