Lesson 18 of 28 · Making AI useful

What is agentic AI?

Agentic AI refers to AI systems designed to act with a degree of independence: given a goal, they plan the steps, use tools to carry them out, adapt when something changes and need only limited human direction. The word 'agentic' means having agency, the capacity to act. It describes a design approach and a spectrum of autonomy, rather than one specific product or model.

Agentic AI vs AI agents

The previous lesson covered the AI agent: a piece of software that plans and acts with tools. Agentic AI is the wider idea behind it. It covers single agents, systems where several agents work together, and ordinary software that has gained agent-like abilities. If 'AI agent' is a noun, 'agentic' is the adjective: it says how much a system decides and acts for itself.

Because it is an adjective, agentic AI is a matter of degree. A useful way to picture it is a ladder (a teaching aid, not an official standard):

Responds: answers a prompt and stops (a basic chat assistant).
Uses a tool: takes one action you asked for, such as a web search.
Follows a workflow: runs steps a person designed in advance.
Plans its own steps: decides what to do next until the goal is met (an agent).
Coordinates: several agents split, do and review the work (a multi-agent system).

Agentic AI vs generative AI

Generative AI creates content, such as text, images, audio or code, in response to a prompt. Agentic AI uses generative models as its reasoning engine to pursue a goal through actions. The two are not rivals: almost every agentic system is built on a generative model, the way a car is built around an engine.

Generative AI: the output is content. You ask, it creates, you decide what to do with it.
Agentic AI: the output is completed work. You set a goal, it takes steps, you review the result.
Example: a model that writes a market summary is generative; a system that gathers the sources, writes the summary, builds a chart and emails it every Monday is agentic.

What agentic AI is not

Agentic AI is not a new kind of model, and it is not artificial general intelligence (AGI), the idea of AI that matches people across most tasks. It is also not the same as traditional automation: a fixed 'if this, then that' rule always follows the same path, while an agentic system chooses its path as it goes, which makes it more flexible and less predictable.

Finally, the label is not proof. Some products called 'agentic' are chat assistants with a new name. When you evaluate a claim, ask what the system can do without a new instruction from you, which tools and data it can reach, where a person must approve, and how its actions are logged and undone.

Where you meet agentic AI, and what to watch

You meet agentic features in coding tools that complete multi-step changes, research assistants that browse many sources before writing a report, customer-service systems that resolve requests end to end, and business software that adds 'agent' capabilities to existing products.

More autonomy brings more responsibility. Agentic systems can act on bad information, be manipulated by hidden instructions in web pages or documents (prompt injection), or take a costly action nobody reviewed. Organisations adopting them usually set rules for permissions, approvals and logging, which is part of AI governance.

Example

Illustration: one goal at three levels of agency

Worked example

Goal: get ready for Monday's team meeting. Generative (responds): You: 'Write an agenda for a 30-minute team meeting about the product launch.' AI: returns an agenda. You copy it, send it and book the room. Workflow (follows fixed steps): Every Friday at 4 pm, an automation sends last week's notes to a model, which drafts an agenda that is emailed to you. Agentic (plans its own steps): You: 'Get us ready for Monday's launch meeting.' AI: reads the project tracker, spots two overdue tasks, drafts an agenda around them, finds a free slot in everyone's calendar, then asks you to approve before sending the invite.

Illustrative scenarios. The same model can sit behind all three; what changes is how much the system is allowed to decide and do.

Key points

Agentic AI describes systems that plan and act toward goals with limited supervision.
It is a spectrum of autonomy, not an on/off feature or a new model.
Generative AI creates content; agentic AI uses it to complete work.
Treat 'agentic' as a claim to verify: tools, permissions, approvals, logs.
More autonomy needs clearer limits and human oversight.

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Frequently asked questions

What is the difference between agentic AI and generative AI?

Generative AI produces content, such as text, images or code, when you prompt it, and you decide what happens next. Agentic AI uses generative models to decide and take actions toward a goal across several steps, such as researching, drafting and sending. Most agentic systems are built on generative AI, so the difference is what the system is allowed to do, not the underlying model.

Is agentic AI the same as AGI?

No. Artificial general intelligence (AGI) refers to hypothetical AI that could match people across most intellectual tasks. Agentic AI is a practical design approach that is already in use: language models connected to tools and given goals within limits. An agentic system can be useful while remaining narrow, and it still depends on human-defined goals, permissions and checks.

How do I know if a product is really agentic?

Ask what it does without a fresh instruction from you. A genuinely agentic product can take a goal, choose and run several steps with real tools, recover from simple errors and report what it did. Check which systems it can access, which actions need your approval, and whether there is a log you can review. If it only replies with text, it is an assistant.