Short answer: agentic AI is software that uses a language model to plan steps, use tools and act towards a goal with little human help. Generative AI writes an answer; agentic AI gets a job done.
I build AI products and agents, and “agentic AI” is now the question I am asked most. This guide gives you a plain definition, the difference from generative AI, three examples and the risks to manage. Updated 12 October 2026.
What is agentic AI?
Agentic AI is software that uses a large language model to work towards a goal on its own. It breaks the goal into steps, uses tools such as search, email or a database, checks its results and keeps going until the job is done, asking a person for approval when it matters.
The word “agentic” means having agency: the ability to act. An AI agent is the thing you build; agentic AI is the wider approach. Most agents today are built on the same large language models behind ChatGPT, Claude and Gemini.
How does an AI agent work?
Every AI agent, simple or complex, has the same four parts:
- A model that reasons about what to do next.
- Instructions that describe the goal, the rules and when to stop or ask.
- Tools it can use, such as web search, email, a calendar, a spreadsheet or a company database.
- Memory of what it has already done, so it can check progress and carry on.
The agent runs in a loop: decide the next step, use a tool, look at the result, and decide again. Anthropic’s guide to building effective agents describes this loop and recommends starting with the simplest design that works.
Agentic AI vs generative AI: what is the difference?
| Generative AI | Agentic AI | |
|---|---|---|
| Main job | Creates content: text, images, code | Completes tasks towards a goal |
| How it starts | A prompt from you | A goal or a trigger, such as a new email |
| Steps | One response, then stops | Plans and runs many steps |
| Tools | Usually none | Uses apps, data and the web |
| Human role | You act on the output | You set goals and approve key actions |
| Main risk | Wrong or made-up content | Wrong actions taken at speed |
The two are not rivals. Agentic AI is built on top of generative AI: the language model is the reasoning engine, and the agent adds goals, tools and memory around it.
Three examples of agentic AI at work
1. A coding agent
A developer describes a bug. The agent reads the code, finds the cause, writes a fix, runs the tests and proposes the change for review. Tools like Claude Code and OpenAI’s Codex work this way, and I use them every day.
2. An inbox triage agent
Each morning the agent reads new customer emails, sorts them by topic and urgency, drafts replies to common questions and flags complex ones for a person. Nothing is sent without approval.
3. A weekly report agent
Every Friday the agent pulls numbers from the sales system and a spreadsheet, compares them with last week, writes a one-page summary and posts it to the team chat for a manager to check.
What can agentic AI do for an Australian business?
Beyond those three, these are the jobs I see agents take on first in Australian teams:
- Checking invoices against purchase orders and flagging mismatches for a person
- Preparing meeting briefs from notes, past emails and the CRM
- Answering routine customer questions and routing the rest to the right person
- Updating records in several systems after a form, call or email comes in
The pattern is the same each time: a frequent, rule-based task, limited access, and a person who approves anything important. Agents take the busywork; people stay in charge.
What are the risks of agentic AI?
An agent can take the wrong action, and it can do it quickly and many times. The fixes are practical: give each agent only the tools and data it needs, log every action, test on real cases before launch, and keep a human approval step for anything that spends money, contacts customers or changes important records.
How do you start with agentic AI safely?
- Pick one task that is frequent, rule-based and low-risk.
- Give the agent only the access it needs.
- Require a person to approve important actions.
- Test it on real past examples before it goes live.
- Keep logs and review them each week at first.
How can you learn agentic AI?
The fastest way to understand agents is to build one. The agentic AI course teaches teams to plan, build and safely deploy an agent on their own workflow in two days, and 1:1 AI coaching takes you from first prompt to a working agent on your own tasks. Leaders deciding where agents fit can start with the executive AI programme.
