Key point: agentic AI marks the transition from simple conversation to autonomous action. Unlike passive generative models, these agents plan and execute complex tasks by manipulating external tools, offering true operational delegation. This is the difference between a chatty assistant and a proactive project manager capable of managing a mission from A to Z.
Are you tired of constantly having to guide your digital tools, turning the promised automation into an endless manual supervision session? Agentic AI finally breaks this chain of dependency, moving from passive spectator to
What exactly is agentic AI?
An AI agent is a software program that actively dialogues with its immediate environment. It collects raw data to perform precise, complex tasks. Its aim is to achieve a specific objective. Above all, it remains totally self-directed in its approach.
Forget conventional software that follows rigid, fixed instructions. Here, the system itself chooses the next relevant action. It’s a clean break with traditional code.
Its technological core is often an LLM (large language model). These are often referred to asLLM agents.
The main objective: autonomous action
The ultimate goal is to carry out complex tasks with little or no human supervision. Total autonomy remains the watchword. You save precious time on execution.
Unlike traditional software that follows coded instructions, AI agents identify the next appropriate action based on the data, and execute it without constant human supervision.
The agent doesn’t simply respond to a request. It acts, decides and operates to validate its final objective.
The operating cycle: perceive, decide, act
It all starts with fine-grained perception of the immediate environment. The agent draws in information via sensors, PLCs or various digital inputs. This is its unique way of seeing.
Then comes the critical decision-making phase. He deconstructs the perceived data and cross-references it with his context to make his choice.
Finally, the agent executes the concrete task and evaluates the result. He can immediately correct himself if an error is detected. It then creates new sub-tasks to advance towards its goal.
The founding principles of agentivity
There are several principles that define a true AI agent. It’s not just a question of code, but of behavior. Let’s take a look at what makes the technical difference.
- Autonomy: Operates without continuous human intervention.
- Objective-oriented behavior: All actions are aimed at a specific goal.
- Rationality: Makes informed decisions based on data and context.
- Proactivity and Adaptability: Anticipate events and adjust strategies to deal with the unexpected.
- Continuous learning: Improves over time through experience.
Agentic vs. generative: the match for autonomy
Now that the foundations have been laid, we need to clarify a major confusion: the difference between an AI agent and what everyone else is already using.
Generative AI and co-pilots: assistants, not actors
Let’s be honest. Generative AIs, like ChatGPT, and co-pilots are fundamentally reactive. They stand idly by, waiting for your precise instruction – the famous prompt – to deign to generate content or a response. Without this impulse, nothing moves.
Their dependence on humans is total. If you don’t tell them exactly what to do, they do nothing. Their role is strictly limited to assisting you, never taking command or initiative.
Their scope of action remains confined to the production of text, images or code. They don’t touch external tools on their own.
Agentic AI: taking action
This is where agentive AI hits the mark by becoming proactive. It doesn’t just answer a question; it acts concretely, step by step, to accomplish a complete mission without being held to account.
Think of it this way: Generative AI is a gifted writer waiting for his subject. Agentic AI is the project manager who manages the entire launch. Understanding the differences with models such as ChatGPT or Gemini allows us to grasp this technological leap.
Comparison chart: a clear view
Still a little fuzzy on the nuances? This table summarizes the fundamental differences. Take a look and never confuse a simple assistant with an autonomous agent again.
| Feature | Generative AI (e.g. ChatGPT) | AI Assistant (e.g. Siri) | Agentic AI |
|---|---|---|---|
| Main role | Generate content on demand | Respond to simple voice/text commands | Achieve a complex goal autonomously |
| Autonomy | Low (reactive, waits for prompt) | Low (reactive, executes an instruction) | High (proactive, takes initiative) |
| Decision making | None (likely to generate text) | Limited to its programming | Complex and autonomous to reach goal |
| Interaction with the environment | Limited to training data | Limited (native apps, web) | Extensive (APIs, databases, external tools) |
| Planning | No | No | Yes (break down an objective into sub-tasks) |
| Sample task | “Write me an email” | “What’s the weather like?” | “Organize my trip to Lisbon: flights, hotel, activities according to my budget” |
Under the hood: how does an AI agent work?
The foundation model: the brains of the operation
At the heart of the system is always a reasoning engine. This is almost always a large language model (LLM). It’s not just a database, it’s the engine that interprets your objective. It literally “thinks” about the strategy to adopt.
Forget the chatbot that simply writes poems. Here, the model acts as a central conductor to analyze the situation. It coolly decides on the next logical step to take. This is the precise role of large language models like Google Gemini today.
Planning: the roadmap to the goal
Next, the agent activates its planning module. Imagine you ask him to “organize my vacation”. He doesn’t rush in headlong; he breaks down this massive objective into a series of manageable sub-tasks. This is a vital structuring step.
Here’s what it looks like in practice. First, it searches the web for available flights. Then it compares prices, books hotels and suggests local activities.
This ability to build an autonomous action plan changes everything. A conventional AI awaits your orders step by step. The agent, on the other hand, plots its own route to success.
Memory: don’t start from scratch every time
Memory is what prevents the agent from becoming amnesiac. Without it, he would forget what he had just accomplished the second before. It’s a vital component in maintaining the coherence of a long mission.
There are two distinct types of storage. The Short-term memory retains the immediate context of the task. Long-term memory stores lessons learned from past missions. .
Thanks to this system, the agent is constantly improving. It remembers your specific preferences. Above all, it avoids foolishly repeating the same costly mistakes.
Using tools: the agent’s hands
An LLM alone remains locked in its training data. The agent’s real power lies in Tool Use. This is what enables it to break out of its black box.
It can use a web browser to hunt for fresh info. It connects to an API to book a ticket, or queries a database to extract precise figures.
These extensions literally become the agent’s “hands and eyes”. They enable them totake concrete action and have an impact on the real digital world.
Reflection and learning: the improvement cycle
Finally, the agent has a critical reflection mechanism. After each action, he stops and analyzes the result obtained. He asks himself: “Did it work?” or “What did I learn?”.
This iterative process of action-reflection-correction is the key to continuous learning. It’s what makes the agent adaptable to the unexpected.
The different faces of agentic AI
Not all agents are created equal. There is a strict hierarchy, from the simplest to the most sophisticated, which determines their real capacity for action.
Simple reflex agents: pure instinct
This is level zero software autonomy. These programs react exclusively to what they perceive in the present moment. They foolishly apply logical if-then rules. Forget any kind of reflection or analysis of the past here.
Take a conventional thermostat to understand the concept. If the temperature drops below 19°C, the heater switches on immediately. It has no memory and doesn’t care about the history.
Model-based agents: a vision of the world
We’re taking technical complexity up a notch. These systems maintain an internal model of their immediate environment. It’s a dynamic representation of how the world really works. They are no longer blind.
This internal vision changes everything when it comes to decision-making. Finally, they manage situations where direct perception is not enough. They deduce invisible states to act intelligently. Context now counts.
Goal- and utility-based agents: strategic choice
This is where goal-based agents come in. They no longer simply react to incoming events. They calculate precise sequences of actions to achieve a desired end state. They plan for the future.
Utility-based agents take the analysis a step further. Faced with several options, they choose the one that maximizes a specific utility value. This is the most efficient route.
This is where we come to true computer rationality. The agent isn’t just looking for a solution that works. It mathematically wants the best possible solution for you.
Learning agents: continuous evolution
This is the type of AI that is most surprising today. These agents modify their own knowledge base according to their experiences. They rewrite their internal rules without being asked. They evolve on their own.
This is precisely where reinforcement learning comes in. The machine learns by trial and error.
They are the only ones able to survive in unfamiliar terrain. They adapt quickly to totally new environments. They develop new skills autonomously.
Concrete applications that are already changing the game
Theory is all well and good, but what does it actually do? Let’s see where these AI agents are already starting to make a difference.
Automated customer service and technical support
Forget basic chatbots that go round in circles. An AI agent manages a claim from A to Z. It accesses customer history, checks order status and understands the context without help.
It diagnoses the problem, proposes a concrete solution – refund or return – and executes the action directly in the system. It’s a true conversational agent, resolving tickets autonomously, without any human intervention.
Project management and administrative tasks
Imagine an agent planning a meeting. He doesn’t just find a free slot. He consults the diaries of all his colleagues, analyzes availability and understands time constraints.
Then it books the room, prepares a relevant agenda based on the last emails exchanged, and sends out personalized invitations. It’s the ultimate personal assistant that frees up your mental workload.
Financial analysis and trading
In finance, an agent can monitor markets in real time. It analyzes thousands of data sources simultaneously: stock prices, economic news and complex financial reports.
Based on a defined strategy, he decides to buy or sell assets, executes orders instantly, and adjusts his strategy according to the results obtained. He acts with cold rationality.
Supply chain optimization
An agent can proactively manage inventory. It monitors levels in real time, anticipating future demand by analyzing sales trends and even weather forecasts.
If it detects a risk of shortage, it automatically places an order with the most suitable supplier, taking into account delivery times and costs. This precision can reduce storage costs by up to 35%.
The real benefits for a company
Automate tasks and boost productivity
This is the most obvious benefit. AI agents take care of heavy, repetitive processes. These tasks used to tie up several skilled employees. Now, the software manages the execution in total autonomy.
The direct result is a sharp rise in productivity. Your human resources teams can finally move away from assembly-line work. They concentrate on high value-added tasks: strategy, creativity, customer relations. It’s a paradigm shift.
Cost reduction and process optimization
Automation means cost reduction. We stop paying human hours for simple copy-paste. Fewer expenses are spent on low-value operations. Your profitability rises mechanically.
Agents also unearth optimizations that are invisible to us. They compare thousands of options in a second. For example, they choose the cheapest carrier in real time. Humans can’t keep up with this infernal pace. This generates immediate direct savings.
Faster, more informed decision-making
An AI agent swallows gigantic volumes of data. It does so in a matter of seconds. That’s an ability beyond the reach of a human brain. No more guesswork with your business.
You’re missing out oncritical opportunities if you ignore this computing power:
- Real-time analysis: agents process information as soon as it arrives.
- Data correlation: They find links between disparate data sets.
- Fact-based decisions: Their choices are based on raw data, not intuition.
- 24/7 availability: They work without a break, ensuring constant responsiveness.
Improving the customer experience
A customer wants an immediate solution to his problem. If they get it, they stay loyal and satisfied. This is exactly what modern agents make possible. Waiting on the phone becomes a thing of the past.
The agent accesses the entire history at a glance. He acts accordingly to deliver a hyper-personalized customer experience. All this is done without any technical friction. The service remains available around the clock.
Deploying AI agents: frameworks you need to know
Convinced? Perfectly. But you can’t deploy an AI agent like you would an application. You need tools and rules.
Development frameworks : LangChain, CrewAI and others
Building these agents doesn’t mean starting from scratch. Open source frameworks such as LangChain or CrewAI provide the building blocks needed to structure artificial intelligence without having to recode all the complex decision-making logic manually.
They “chain” the essential components: the LLM, external tools and memory. This is the invisible plumbing that connects everything together, transforming a passive model into an autonomous player.
CrewAI, for example, specializes in creating teams of agents who collaborate on a task, each with a defined role. This simulates a real departmental structure within your code.
The “Agent System of Record” (ASoR) concept
It’s a concept being pushed by players like Workday to secure business use. The idea is that the agent’s every action should be traced and recorded, to avoid any opacity in the company’s critical processes.
The agent becomes a “system of record” in its own right, in the same way as a CRM or ERP system. We need to know what he did, when and why, guaranteeing total auditability of operations.
It’s a question of governance and legal responsibility. Without ASoR, we end up with an uncontrollable black box that makes decisions without a trace, which is unacceptable for a serious organization.
Multi-agents: towards collaborative AI teams
The next step is multi-agent systems that surpass isolated models. We no longer deploy a single agent, but a “team” of specialized agents capable of handling much heavier and more nuanced workflows.
A “researcher” agent collects the information, an “analyst” agent interprets it, and an “editor” agent produces the final report. This division of labor mimics human organization to gain precision and reduce hallucination errors.
This collaboration makes it possible to tackle even more complex problems, but also poses coordination challenges. We need to ensure that agents communicate effectively with each other without creating endless feedback loops.
Human supervision: the indispensable safeguard
Even the most autonomous agent needs a safety net to prevent drift. Human supervision remains a good practice, especially in the early stages, as AI still lacks fine ethical or contextual judgment.
There must be checkpoints where the agent requires human validation before a critical action, such as confirming the sending of a €10,000 transfer. This is the “human-in-the-loop” principle: the human retains final veto power to prevent costly errors that the machine cannot see.
Risks and limits: what no one dares tell you
All this sounds promising, but it would be dishonest not to mention the real dangers and challenges that lie ahead.
Systemic dependencies and failures
When several agents depend on each other, the failure of just one can cause an immediate domino effect. This is the brutal risk of systemic failure. If one link breaks, the whole chain collapses without warning.
If the agent handling payments fails, the whole automated supply chain can come to a screeching halt. Robustness is a major challenge. Without a solid architecture, your system becomes a colossus with feet of clay.
Infinite loops and hallucinations in action
Inadequate planning can quickly trap an agent in an infinite feedback loop. He tries, fails, and tries again in exactly the same way, without ever making any progress. It’s a dry and pointless waste of resources.
Worse still, an agent can “hallucinate” an action. If he mistakenly believes he has succeeded in a task, he may continue his plan on a totally erroneous basis, with potentially disastrous consequences. You risk basing an entire strategy on hot air.
Security and confidentiality: the Achilles heel
To act, an agent needsextensive access. API keys, sensitive passwords and direct access to customer databases are often required for it to function.
There are serious concerns about data confidentiality and security if agent integration is not managed with absolute rigor.
Poorly managed access turns the agent into a gaping doorway for cyber-attacks. Hackers are just waiting for this opportunity to infiltrate your critical systems.
Best practices for staying in control
To limit the risks, rules are essential. Keeping detailed activity logs for transparency is the first step. Without them, you’re sailing blind in the event of a technical incident or hacking attack.
We also need to give the user a “stop button” to interrupt the agent at any time, and track every action with unique identifiers. This is the only way to maintain real human sovereignty.
Agentient AI no longer just talks: it acts, radically transforming the way we work. While this autonomy offers immense productivity gains, it also demands heightened vigilance. So, are you ready to recruit your first team of virtual agents? Just remember to keep your hand on the “stop” button.
FAQ
What is agentic AI in concrete terms?
Imagine going from a trainee who has to be told what to do to an autonomous employee who takes the initiative. That’s what agentic AI is all about. Unlike conventional software or generative AI that waits wisely for your instructions (prompts), an AI agent is designed to achieve a goal on its own.
He perceives his environment, reasons to establish a plan of action, and uses tools (like surfing the web or using software) to carry out the necessary tasks. In short, he doesn’t just talk, he acts to achieve a concrete result, without you needing to keep an eye on him all the time.
Is ChatGPT considered an agentic AI?
Not exactly, at least not in its basic version. ChatGPT is first and foremost a generative AI: it’s an excellent conversationalist who responds to what you ask of it, but it remains passive. It waits for your impulse to produce text or code.
However, the boundary is getting thinner. When connected to external tools or executing code to solve a complex, multi-step problem, it begins to adopt “agentic” behavior. But purists still tend to classify it as an assistant (co-pilot) rather than a fully autonomous agent.
What does it mean to operate in “agentic mode”?
Switching to agentic mode means activating the “Perception – Decision – Action” loop. Instead of simply answering a question, the system will break down your request into sub-objectives. It will stop and think: “What do I need to succeed?”, go and find the missing information, and even self-correct if it fails.
It’s a paradigm shift: AI is no longer there to assist you with a task, but to take responsibility for the entire task. It becomes proactive and manages the unexpected to deliver the end result, whether it’s the complete planning of a trip or the automation of a business process.
What’s the budget for an AI agent?
That’s the famous consultant’s answer: “it depends”. If you have technical skills, development frameworks such as LangChain or AutoGen are open-source, and therefore free to install. Your main cost will then be API consumption (“tokens”), which remains very affordable for testing purposes.
On the other hand, for turnkey enterprise solutions or platforms such as the pro versions of CrewAI, rates can start at around a hundred euros a month and rise according to the volume of actions. The investment is calculated above all in configuration time, to ensure that the agent doesn’t do anything with your data!
