Agentic AI: definition, how it works, and differences with generative AI

Generative IA has changed the way we produce content. Agentic AI is changing the way we make decisions and act. These are two distinct revolutions, often confused under the same term "artificial intelligence".

Since 2024, the term "Agentic AI" has established itself in tech and business discussions. It appears in product roadmaps, investor presentations, and analyst reports. But behind the word, the realities are very different: some speak of AI agents when describing a simple improved chatbot, others refer to systems capable of executing complex tasks fully autonomously.

Agentic AI refers to systems capable of pursuing a goal autonomously, by chaining actions, using external tools, and adapting to intermediate results, without human intervention at every step.

This article sets the groundwork for the different terms related to artificial intelligence: what Agentic AI is, how it works, how it differs from Generative AI and chatbots, and what its first concrete business applications are.

What is Agentic AI?

Definition

Agentic AI is an artificial intelligence system capable of receiving a goal and achieving it autonomously, taking a series of decisions and actions to do so.

What fundamentally distinguishes it from other forms of AI is that it does not wait for an instruction at every step. It plans, acts, observes the result of its action, adjusts, and continues, usually until the goal is reached or until human intervention is required.

Concretely, if you ask a Generative AI system to "write a follow-up email for this prospect", it produces the email. If you assign the same task to an AI agent, it identifies the prospects to follow up with on its own, consults their history in the CRM, writes a personalized email for each, sends it, records the action, and schedules the follow-up. All of this, without you having to intervene between each step.

Origin of the term and emergence context

The word "agentic" comes from the Latin agere, which means to act. In philosophy and cognitive sciences, agency refers to the capacity of a system to act intentionally on its environment. In artificial intelligence, the concept of an agent has existed since the 1990s, with work on reactive and deliberative agents.

What is changing today is the engine. Large Language Models (LLMs) like GPT-4, Claude, or Gemini have become capable enough to serve as the reasoning brain for an agent. They understand complex goals formulated in natural language, break down problems, choose the right tools, and adapt to unforeseen situations. It is this combination (LLM + tools + action loop) that has made Agentic AI operational on a large scale since 2023-2024.

How does Agentic AI work? The basic mechanisms

The perception → reasoning → action loop

The functioning of an AI agent relies on a continuous three-step loop:

1. Perceive. The agent observes its environment: available data, results of the previous action, current constraints...

2. Reason. It decides on the next action to take to get closer to the goal.

3. Act. It executes the action, then observes the result, which becomes the new perception.

This loop iterates until the goal is reached, an error blocks progress, or human validation is required.

What fundamentally differentiates the agent from a simple prompt is that it does not stop after producing an answer. It continues, adapts, corrects. A single prompt yields a single output. An agent pursues a goal through as many iterations as necessary.

The key components of an agent

An AI agent relies on five fundamental building blocks to function:

The LLM

The Large Language Model is the reasoning engine. It interprets the goal, decides on the actions to be taken, and generates instructions for the tools. It does not just produce text; it also reasons about what to do next.

Memory

Memory can function in the short term (the context of the current session: what the agent "knows" in the moment) and long term (database, interaction history, reference documents persisting between sessions). Without persistent memory, the agent starts from scratch with each new task.

Tools

These are the functions that the agent can call to act in the physical world: APIs, web browser, database, messaging, code interpreter, CRM... This is what distinguishes an agent from an LLM alone: the agent can change the state of the world, not just produce text.

The Planner

The planner breaks down the goal into sequenced or parallel sub-tasks. A goal like "identify the hot 10 prospects this quarter and prepare a follow-up sequence for each" requires several coordinated steps that the planner organizes.

The Evaluation Module

The evaluation module checks if the action produced has indeed achieved the targeted sub-goal, decides whether to continue, correct, or escalate to a human.

Agentic AI vs Generative AI: what are the differences?

This is the most common confusion, and the most important one to clear up.

What Generative AI does

Generative AI produces content from an instruction: text, image, code, audio. Its mode of operation is simple: a request (input), a response (output). It does not take initiative, does not-chain actions, and does not act in external systems. It only responds.

ChatGPT writing an email, Midjourney generating an illustration, GitHub Copilot suggesting code: these are uses of generative AI. They are powerful, but limited to producing a single output.

What Agentic AI does in addition

Agentic AI generally uses a generative LLM as an internal building block, but it goes much further. It uses it not to produce an answer, but to reason on what to do next, call tools, act, verify, and start over.

Agentic AI vs Chatbot: a common confusion

A chatbot answers questions within the framework of a conversation turn. It can be highly sophisticated, use a powerful LLM, appear "smart", but it remains reactive. It waits for us to speak to it.

An AI agent can act proactively, without being prompted at every step. It can be triggered by an event (new incoming lead, expiring contract, stock alert) and chain a sequence of actions without human intervention.

The distinction is not a matter of the power of the underlying model, but rather of architecture: the agent has tools, memory, a planner, and an action loop. The chatbot does not.

First applications of Agentic AI in business

What is being deployed today

Agentic AI is not a futuristic horizon. Operational applications exist today in several areas:

Automation of multi-step tasks

Qualifying leads, processing support tickets, sales follow-ups, report generation... Tasks that previously required human intervention at every step are delegated to an agent that executes them from end to end.

Complex research and synthesis

An agent can browse through dozens of sources, extract relevant information, cross-reference it, and produce a structured synthesis in a few minutes, where a human would spend several hours.

Orchestration of multi-tool workflows

The agent coordinates actions across multiple systems (CRM, messaging, ERP, calendar) without a human having to manually transfer information from one tool to another.

For concrete examples by sector, I invite you to read our article on concrete examples of Agentic AI by sector.

Agentic AI is not an evolution of Generative AI, but rather a paradigm shift. We are moving from "asking an AI to produce something" to "entrusting an AI with executing a goal".

This shift has concrete implications for businesses: repetitive multi-step tasks can be delegated, teams repositionated towards decisions and relations, systems interconnected without manual friction.

The challenge is no longer to understand what Agentic AI is. It is to know where and how to deploy it within your organization, with the right prerequisites, the right constraints, and the right governance.