What is agentic AI?
Agentic AI refers to artificial intelligence systems that can plan and carry out a sequence of actions to reach a goal, with limited human supervision. Where generative AI answers a prompt, an agentic system works toward an objective: it perceives its environment, reasons through the steps, uses tools such as APIs, web browsers and databases, and checks its own results.
How does agentic AI work?
The term comes from “agency,” the capacity to act independently. An agentic AI system is made up of one or more AI agents, each powered by a large language model (LLM) that serves as its reasoning engine.
An AI agent works in a loop. It repeats four steps until the goal is reached or a human steps in.
1. Perceive
The agent gathers context from its environment: files, emails, web pages, databases or the content stored in a CMS.
2. Reason and plan
The agent breaks the goal down into subtasks and decides in which order to handle them.
3. Act
The agent calls tools, APIs or applications to complete each task, such as running a search, editing a file or sending a message.
4. Learn and adapt
The agent evaluates the result, corrects its plan when something goes wrong and tries again if needed. Short-term and long-term memory let it reuse what it learned in later steps.
A single system can combine several specialized agents (research, writing, quality control) coordinated by an orchestrator agent. This is called a multi-agent system. To act inside software, agents rely on connectors. The Model Context Protocol (MCP) is an open standard for these exchanges between agents and applications.
Related terms: Content management system
Agentic AI vs. generative AI vs. chatbots
These three technologies are often confused. The difference lies in what each one does with a request.
| Type | What it does | Example |
|---|---|---|
| Generative AI | Creates content (text, images, code) in response to a prompt | Drafting a product description |
| Chatbot | Answers questions turn by turn in a conversation | Answering FAQs on a support page |
| Agentic AI | Pursues a goal over several steps and takes actions in other tools | Auditing 500 pages and fixing missing metadata |
They often work together: an agent relies on a generative model to reason and write, then on tools to act. Our detailed comparison of agentic AI and generative AI covers these differences in depth.
AI agents vs. agentic AI
The two terms are often used interchangeably, but they are not quite the same. An AI agent is a single piece of software that completes a task using an LLM, tools and the context it has access to. Agentic AI describes the broader system in which one or more agents plan multi-step work, coordinate with each other and adjust as they go.
Examples of agentic AI
- In customer service, an agent reads a support ticket, checks the order history in the CRM and processes a refund within preset limits.
- In IT operations, an agent detects an incident, runs diagnostics and opens a ticket with a proposed fix.
- In software development, coding agents write code, run tests and submit their changes for review.
- In marketing and content, an agent audits a website, flags outdated pages and drafts updates for an editor to approve.
Agentic AI in a CMS
In content management, an agent can take on repetitive tasks that affect many pages at once:
- Inventory a website and migrate its content to a new CMS.
- Translate and localize pages for several markets.
- Check accessibility, SEO metadata and links before publishing.
In an enterprise CMS, the agent works within the permissions, workflows and approval steps already in place, so the team stays in control of what gets published.
Risks and limitations of agentic AI
Because agents act instead of just answering, their mistakes have more impact. Four risks deserve attention:
- A wrong assumption early in a task can carry through every step that follows.
- An agent with broad permissions can be manipulated by malicious instructions hidden in the content it reads, a technique known as prompt injection.
- Without an audit trail, it is hard to know who, or what, made a change.
- Multi-step tasks consume more computing resources than a single prompt, so costs can rise quickly.
Limiting each agent’s permissions, logging its actions and requiring human approval for high-impact steps keeps these risks under control.
In practice
Frequently asked questions
What is the difference between agentic AI and generative AI?
Generative AI creates content when you ask for it. Agentic AI pursues a goal across several steps: it plans, uses tools and takes actions in other software, often relying on a generative model to reason.
Is ChatGPT agentic AI?
ChatGPT started as a generative AI chatbot. Its agent mode adds agentic capabilities: it can browse the web, use tools and complete multi-step tasks, asking for confirmation before sensitive actions. ChatGPT can therefore act as agentic AI, but not every use of it is agentic.
What is an example of agentic AI?
Asked to prepare a website for a new market, a content agent can list the pages to translate, translate them, check their SEO metadata and send the drafts to an editor for approval.
Can an AI agent publish content without human approval?
Yes, if it has been given the permissions to do so. In most organizations, high-impact actions such as publishing, deleting or editing customer data require human approval, and every action the agent takes is logged.