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Agentic AI: definition, examples, and how to stay in control

Agentic AI refers to AI agents that carry out a series of actions in your tools to reach a goal. A plain definition, business examples, and the guardrails that keep people in charge.

By Published on 3 October 2026

Agentic AI refers to artificial intelligence systems, called agents, that do more than answer: they take a goal, break the work into steps, use your tools to act and check the result. Where an assistant drafts a reply, an agent finds the customer’s file, prepares the reply and submits it to a person for approval.

Being able to act changes the nature of the risk: an agent can make a mistake in your systems, not just in a piece of text. That is why this article is as much about control as about technology.

How does an AI agent work?

An agent brings three things together: a language model, which understands the request and chooses the next step; tools, which it calls to read or to act (a document base, a ticketing system, a code repository); and a working memory, which keeps track of the task.

It works in loops: it observes, chooses an action, carries it out, looks at the result, then carries on, corrects itself or stops. The tools and rights it is given set what it can do: an action it has no tool for is out of its reach.

AI agents, generative AI and RPA: what is the difference?

  • Generative AI produces content from an instruction (a text, a summary, some code), then waits for the next instruction.
  • RPA (robotic process automation) replays clicks and entries defined in advance, without understanding what it handles.
  • An AI agent pursues a goal: it chooses its steps and acts through tools, within the limits it is given.

RPA stays reliable as long as nothing changes, but stops as soon as a screen or a document format does. An agent understands a request or a document it has never seen. The two often work side by side: the agent handles what takes judgement, RPA keeps the repetitive steps already automated.

Watch the labels: in June 2025, the research firm Gartner noted that many vendors present existing assistants, RPA bots or chatbots as “agentic”, and estimated that only about 130 vendors offered genuinely agentic products.

Examples of agentic AI in business

The uses that work best follow clear rules and handle documents or data:

  • Software development: an agent reproduces a bug reported in a ticket, proposes a fix with its tests and opens a pull request, which a developer reviews before release.
  • Incoming requests: an agent reads an email, finds the contract concerned, prepares the reply or creates the ticket; a person approves before anything is sent.
  • Checks and reconciliations: an agent compares invoices, orders and payments, flags the gaps and prepares the entries for approval.
  • Reporting: an agent gathers figures from several tools, writes the commentary and submits the report before it goes out.

What they share: the agent prepares the work, and a person approves whatever commits the company. Both kinds of use are described on our pages Software development automation and Business-process automation.

What an agent needs: data, tools and rights

Reliable data. An agent is only as good as the information it consults. Sources that are up to date, described and open according to each person’s rights keep it from missing the point. The glossary explains RAG, a common technique for letting an agent search your documents.

Declared tools. Every possible action goes through a tool built for it, with its own parameters. Anything not declared stays out of the agent’s reach.

Limited rights. An agent gets its own identity and reaches only what its task needs, like a new starter who is not handed every key on day one.

Staying in control: human approval, limited access, logs, a stop button

Because an agent acts, a mistake or a manipulation has real consequences. These risks are now well described: OWASP, the foundation known for its lists of application security risks, published in December 2025 ten risks specific to agentic applications, from hijacking an agent’s goal to abusing its rights. In April 2026, CERT-FR, the incident response centre of the French cybersecurity agency ANSSI, advised against deploying autonomous assistants on workstations in production and recommended keeping them to isolated test environments.

The same guardrails come back in these recommendations:

  • Least-privilege access: each agent has its own identity and rights limited to its task.
  • Human approval of sensitive actions: sending a message, making a payment or releasing to production waits for a person’s approval.
  • Filters on every exchange: an instruction hidden in a document or a web page (prompt injection) must not be able to hijack the agent.
  • A log of every action: who asked for what, what the agent did, with which data.
  • A stop button: an agent can be suspended at once.
  • A sandbox first: the agent is tested on dummy data before it goes near your real systems.

On the regulatory side, the European AI Act does not create a separate category for agents: an agent is an AI system, subject to the rules that match its use and its level of risk. The glossary sums up its timeline, and our offer AI agents under control details these guardrails.

Where to start

Agent projects rarely fail on technology alone. In June 2025, Gartner predicted that over 40% of agentic AI projects would be cancelled by the end of 2027, because of escalating costs, unclear business value or inadequate risk controls. Three habits avoid these pitfalls:

  • Pick a precise, frequent and measurable task, rather than an agent meant to do everything.
  • Put the guardrails in from the first prototype: limited rights, human approval, logs.
  • Measure before extending (time saved, errors avoided, cost per task), then widen the scope step by step.

This is how we work with your teams (see Agentic AI under your control): a first agent on a task chosen together, under control, and the next step if the results justify it. To talk it through with an engineer: the free scoping day.

Frequently asked questions

What is an AI agent?

A program that relies on an AI model to reach a goal in several steps: it chooses the next action, uses tools to act, checks the result and stops when the task is done or when it needs approval.

Agentic AI and generative AI: what is the difference?

Generative AI produces content in response to an instruction. Agentic AI uses that kind of model to act in your tools and chain several steps towards a goal.

Does an AI agent replace RPA?

Not necessarily. RPA still suits repetitive steps on stable screens; the agent takes on what requires understanding a request or a document. The two can work together.

Can an agent act without human approval?

Technically yes, and that is exactly what needs a frame. Reading, searching and preparing can be automatic; sending, paying, deleting or releasing to production wait for a person’s approval.

Sources

Facts and figures checked on 3 October 2026.