What Is Agentic AI?

Agentic AI is artificial intelligence that can work toward a goal across several steps with limited supervision. Instead of only answering a question, it can make a plan, use tools such as a web browser, email or a spreadsheet, check the results and decide what to do next. A person sets the goal and the limits. The AI works out the steps.

What agentic AI means

"Agentic" comes from agency: the capacity to act. The word describes how an AI system behaves, not a single product or technology.

Most people first meet AI through a chat window. You ask a question, the AI answers, and the next move is yours. If you want something done with that answer, you do it yourself.

Agentic AI carries the task forward. Ask it to reschedule a meeting and it can check calendars, find a time that works, send the invitation and tell you when it is done. For the short version, see the agentic AI entry in the LATHIC Glossary.

Agentic is a matter of degree. Some systems follow a fixed sequence that a developer designed in advance and let the AI handle a few steps inside it. Others let the AI choose its own steps and tools as it goes. Anthropic, which makes the Claude models, draws this line in its guide to building agents: "workflows" run through predefined paths, while "agents" dynamically direct their own processes and tool use. Anthropic calls both "agentic systems."

How agentic AI works

Agentic AI works by repeating a cycle of planning, acting and checking until a goal is met or it needs help. A typical task runs like this:

1. It receives a goal. For example: "Find three suppliers who can deliver 500 branded notebooks by March 1 and compare their quotes."
2. It makes a plan. The system breaks the goal into smaller steps: search for suppliers, check delivery times, collect prices, build a comparison.
3. It takes an action. It uses a tool to complete one step, such as running a web search, opening a page, querying a database or drafting an email. This ability is called tool use.
4. It checks the result. Did the search return useful suppliers? Did the page load? Is the price missing?
5. It adjusts. Based on what it found, it picks the next step, tries again or changes the plan.
6. It asks for approval when the stakes rise. Well-designed systems pause before actions with real consequences, such as paying, sending or deleting something.
7. It finishes or reports back. It delivers the result, or explains where it got stuck.

Steps 3 through 5 repeat many times. Engineers call this repeating cycle the agent loop.

What sits underneath

Most agentic AI today is built on a large language model, the same kind of model behind chat assistants. The model handles the reasoning: reading the goal, deciding on a plan and choosing which tool to use next. For how these models work, see What Is a Large Language Model?.

The model is only part of the system. Software around it connects the model to tools and data, keeps track of progress on the task, and enforces rules about what it may and may not do. Open standards such as the Model Context Protocol, introduced by Anthropic in 2024, give AI systems a common way to connect to outside tools and data sources.

Larger agentic systems can split work among several specialized agents. One might research, another might draft and a third might check the result. A coordinating component, often called an orchestrator, assigns the work and combines the results.

Examples of agentic AI

The clearest sign of agentic AI is that it completes a task, not just a piece of writing.

Customer support. A customer asks for a refund. An agentic system looks up the order, checks the refund policy, issues a refund within an approved limit and emails the customer. Anything outside the limit goes to a person.

Research. You ask for a summary of how three competitors price their products. The system plans a set of searches, reads the pages it finds, notices gaps, runs more searches and compiles a report with sources.

Invoice processing. An agentic workflow reads incoming invoices, extracts the amounts and supplier details, matches them against purchase orders, flags mismatches for a person to review and sends the rest for payment approval.

IT support. An employee reports that they cannot access a shared drive. The system checks their account permissions, identifies the missing access, requests approval from the drive's owner and updates the ticket when access is granted.

Software development. Coding agents can read a codebase, make changes across several files, run tests, and fix what the tests reveal before handing the work to a developer.

Major AI companies now build agentic features into widely used products, and the product names change quickly. The pattern stays the same: a goal goes in, and a sequence of actions comes out.

Agentic AI vs generative AI

Generative AI creates content, such as text, images or code, in response to a prompt. Agentic AI uses that ability, together with planning and tools, to take actions toward a goal. They are not rival technologies. Most agentic systems are built on generative AI models.

The difference shows up in what you get back.

A useful test: if the AI stops once it has produced something for you to use, it is working generatively. If it goes on to use what it produced to do something, it is working agentically. Generative AI is the engine. Agentic AI is what happens when that engine is given tools and a goal.

Agentic AI vs AI agents

Agentic AI is the broader idea: AI that plans and acts toward goals. An AI agent is a specific system built to do that. A company's "agentic AI strategy" might include several separate AI agents, each handling a different job.

The terms overlap, and people often use them interchangeably. The practical difference is scope. Agentic AI describes a way of working, which can involve a single agent, several agents working together, a fixed workflow with a few AI-driven steps, or an agentic feature inside a larger product. An AI agent is one of the individual systems doing the work.

For what an individual agent can and cannot do, and how it differs from a chatbot, see What Is an AI Agent?.

Why agentic AI matters

Agentic AI changes the question from "Can AI help me write this?" to "Can AI do this part of the job?" That shift is why the term now appears in product announcements, software updates and workplace strategy discussions.

Research firm Gartner predicted in June 2025 that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024. It also predicted that at least 15% of day-to-day work decisions will be made autonomously through agentic AI by 2028. These are forecasts, not measurements, but they show where software vendors are heading.

The same Gartner release warned about "agent washing": rebranding existing chatbots and automation tools as agentic without real agentic capabilities. Gartner estimated that only about 130 of the thousands of vendors marketing agentic AI are real.

That makes it worth knowing what to ask when someone says a product is agentic:

• What actions can it actually take? Reading information is very different from sending, buying or deleting.
• What can it access? Which accounts, files and systems does it connect to?
• When does it ask for approval? Which actions pause for a person to confirm?
• Can you see what it did? Is there a record of each step?
• What happens when it gets something wrong? Can the action be undone, and who is responsible?

Limitations and risks of agentic AI

Agentic AI can save time on multi-step work, but acting on your behalf creates risks that a chatbot answer does not.

Mistakes can compound. A wrong assumption early in a task can carry through every step that follows. A chatbot's wrong answer sits on the screen. An agent's wrong answer can become a sent email or a changed record.

Hidden instructions can mislead it. An agent that reads webpages or emails can encounter text written to manipulate it. OpenAI has warned that agents are susceptible to hidden malicious instructions in places such as a webpage or email. This attack is known as prompt injection.

Too much access raises the stakes. The OWASP security project lists "excessive agency" among the top risks for applications built on large language models in its 2025 list. It describes the danger of giving an AI system more functionality, permissions or autonomy than a task needs.

Projects can stall. Gartner predicted in June 2025 that over 40% of agentic AI projects will be canceled by the end of 2027, citing escalating costs, unclear business value or inadequate risk controls.

Agentic does not mean fully autonomous. A common misconception is that agentic AI runs with no human involvement. In practice, many products keep a person in control of consequential steps. Google says Gemini Agent, launched in November 2025, is designed to seek confirmation before critical actions like making purchases or sending messages. This design is called human-in-the-loop.

Safer agentic setups give the AI a clear goal, limited access and a human checkpoint wherever a mistake would be costly.