An AI agent independently performs a series of steps to complete a task: retrieving data, creating something, checking it, and preparing it. A language model like ChatGPT answers your question in a conversation, after which the next step is up to you. AI automation begins as soon as you set up those steps as a fixed process.
What’s the difference between an AI agent and a language model?
A language model provides an answer; an AI agent performs a task. The agent combines a language model with integrations to your systems, predefined instructions, and skills, enabling it to execute multiple steps in sequence on its own. A large language model (LLM) is the model behind ChatGPT, Claude, or Gemini: it predicts the most appropriate text based on your prompt. You can read more about how a large language model works in the knowledge base. An agent puts such a model to work: instructions determine what it does, skills are defined tasks, and integrations provide access to your data. Choosing the next step is called orchestration, and this way of working is also known as agentic AI. In our knowledge article, you can read exactly what an AI agent is.
ChatGPT can now also function as an agent itself: with ChatGPT Agent since July 2025 and with ChatGPT Work since July 2026. So the difference isn’t in the brand name, but in how you use AI. A one-off command in a chat window is different from a workflow with fixed links, rules, and an owner. A copilot falls somewhere in between and assists you while you do the work. See also the difference between an AI copilot and other AI agents.
Five steps, from keyword to working copy.
AI automation means that an agent runs through a recurring workflow from start to finish, within rules that you define.
Research
Retrieve search volume and queries.
The agent retrieves search volumes from Google Ads’ Keyword Planner and checks Search Console to see what your website is already ranking for. This way, the agent can see if another page is already targeting the same keyword. This article is an example of that: the knowledge base already explains what an AI agent is, so the agent chose a different keyword here.
Writing
Writing according to established frameworks.
Tone of voice, writing guidelines, and the structure for each page type are defined once in instructions and skills. The agent uses these to write a first draft. How well you define these in instructions and prompts largely determines the final result.
Checking
Checking against the organization’s own guidelines.
The agent then verifies the text against the same rules: meta title, headings, word choice, and a source for each figure. If a fact is missing, the agent flags it rather than making something up.
Preparing
Preparing as a draft in the CMS.
Through a connection with, for example, Statamic—a possible CMS for your website—the agent prepares the page as a draft, complete with metadata and internal links. This connection runs through MCP, a standard for securely linking AI to your business systems. Whatever the agent creates is given an AI label that only disappears once a colleague saves the page themselves.
Measure
Measure and adjust.
After publication, the agent checks Analytics and Search Console to see how a page is performing. If performance drops, the agent proposes an improved version—again as a draft.
What work remains with humans?
Humans retain final editorial control, decision-making, and everything that only you know. This principle is called “human-in-the-loop”: at fixed points in an automated workflow, a human reviews and makes decisions. An agent doesn’t know which project you just completed or what position you want to take. Without that input, the agent will write text that’s already been posted online a hundred times. That’s why it’s helpful to have a colleague review the text, refine it, and decide whether something goes live. The agent takes care of the surrounding tasks: researching, organizing, drafting a first version, checking it, and preparing it for publication.
Where does an AI agent run into trouble?
An AI agent gets stuck where access, rules, or oversight are lacking. In practice, three situations keep coming up.
No access to the source. When one of our SEO tools stopped providing data, the research phase came to an immediate halt. The agent could only continue once a different source was found.
Rules that aren’t documented anywhere. If the procedure exists only in someone’s head, the agent will start guessing. If it doesn’t know that you say “guests,” it will write “customers.”
Output that no one reviews. An agent can sound convincing, even when they’re wrong. If they don’t first retrieve the latest version of a page, they’ll overwrite a colleague’s work.
Which processes are best suited for an AI agent?
Processes that recur regularly and involve the convergence of data, decisions, and actions are best suited for an AI agent. Marketing is just one example. Think of an agent that prepares a quote using data from your CRM, routes a support request to the right colleague, or performs anomaly detection on your order data. According to Statistics Netherlands (CBS), by 2025, 29.8 percent of small and medium-sized enterprises (SMEs) and 66.2 percent of large companies were already using one or more AI technologies. If you’re unsure where to start, read about which business processes are suitable for AI.
Not every process requires an agent. If a step always follows the same rules—such as transferring an order from your online store to your ERP—then a fixed integration is faster, cheaper, and more predictable. An agent only adds value where judgment is required, such as reviewing an irregular invoice. In practice, you often combine the two: in workflow automation, fixed integrations handle the routine tasks, while AI evaluates the exceptions.
What needs to be in place before an agent can take over a task?
Before an agent takes over a task, you need four things: access to your systems, accurate data, defined rules, and someone responsible for the outcome.
Access is managed via an API connection or MCP server, with specific permissions defined for each system regarding what the agent is allowed to view and modify. If your data is scattered across separate systems, it must first be made accessible. How you expose data from your systems determines what an agent can do with it.
Also specify what the agent logs and who is responsible if something goes wrong. For applications classified as high-risk under the European AI Regulation, human oversight and logging are even mandatory.
The architecture of your systems matters, too. For Legal Mind, Cube built the website using predefined components in Statamic, with structured data that search engines and language models can read. Read about how that digital foundation for an AI platform was created.
Curious about what an agent can take over in your process? Find out how to have AI agents and copilots built that integrate with your own systems.
What tasks would you assign to an AI agent?
Tell us which recurring process is time-consuming. In a 30-minute conversation, Jarno will work with you to identify which steps an agent can take over and which must first be handled by a human.
Worth reading next...
Which business processes are suitable for AI?
Real-World AI Use Cases: What AI Actually Does in Organizations.
MCP integration: connect AI securely to your business systems.
Questions about AI agents? No problem.
No. ChatGPT is a chat service based on a language model, which now includes agent functions. An AI agent for your own process is integrated with your systems, operates based on predefined instructions, and follows a recurring workflow.
Access via an API connection or MCP server, data that’s accessible and reliable, and predefined instructions. Specify for each system what the agent is allowed to read and modify, and who verifies the results.
Yes, for individual tasks, you can use tools like n8n, Make, or the agent features in ChatGPT. If you’re dealing with business-critical processes involving your own data, make sure to set up permissions, logging, and determine who will manage the agent if the creator leaves.
Technically, yes, but that’s up to you. The agent can prepare a draft, and a colleague publishes it only after approval. For irreversible actions, having a fixed review step like this is wise.
That depends on what’s already in place. If your systems can be integrated, your data is accessible, and the rules are documented, you’ll be able to set up an initial workflow more quickly.
You’ll need to specify this in your agreements. Check whether the language model provider uses your data for training. Specify in the contract with the developer who owns the code, the instructions, and the data.