Summary
- AI agents, or agentic AI, represent the new generation of artificial intelligence. Capable of planning, deciding, and acting autonomously, they enrich the conversational experience and automate complex tasks. While global giants (Salesforce, AWS, n8n, Google, etc.) are accelerating their efforts in this area, France is seeking to position itself between innovation, sovereignty, and responsibility.
What is an AI agent and how can it be defined in concrete terms?
The AI agent, also known as agentic AI, marks a new stage in the evolution of artificial intelligence. An AI agent is capable of planning, deciding, and acting autonomously, adapting to its environment and the user’s needs.
From text generation to autonomous action
An AI agent no longer just generates content: it takes action.
Specifically, it can:
- understand an overall objective (e.g., “organize a customer webinar”);
- develop a multi-step strategy (book a room, send invitations, prepare a presentation);
- mobilize external tools (APIs, messaging, CRM) to execute these actions;
- and adjust in real time based on the results obtained or user feedback.
The rapid evolution of AI: from chatbots to autonomous agents
Here’s how the transition to this new era took place:
This diagram illustrates the evolution of artificial intelligence, from its beginnings to the new era of agentic AI. In 2017, simple AI relied mainly on traditional chatbots operating according to predefined scenarios. The year 2022 marked a turning point with the emergence of generative AI, capable of classifying documents and emails, generating personalized responses, summarizing conversations, and even predicting certain behaviors. In 2025, we entered the era of agentic AI, where systems orchestrate multiple intelligent agents, make decisions, and act autonomously, offering a completely redesigned, more fluid, proactive, and personalized customer experience.
Why does the definition of agentic AI remain unclear according to experts?
Defining agentic AI is no simple task, and that is precisely what makes it such a fascinating subject.
There is no official consensus: each technology player has its own interpretation.
Multiple visions depending on the player
- IBM describes AI agents as “systems capable of acting autonomously in a given environment to achieve a specific goal.”
- AWS refers to “contextual AI capable of reasoning and using tools to perform complex tasks without direct human supervision.”
- Red Hat emphasizes the collaborative dimension: “cooperative agents” capable of communicating with each other and interacting with business software.
These nuances show that the term “AI agent” covers both a technical and philosophical approach: to what extent can and should we delegate decisions to a machine?
A still-evolving concept
Agentic AI is currently more of a horizon than an established category.
It refers to a set of converging technologies (LLM, API, automation tools, decision engines) that are moving toward a common goal: giving AI the ability to take initiative.
Some experts even prefer to talk about “orchestrating AI”: not an all-powerful AI, but a system capable of coordinating several specialized intelligences.
This approach seems the most realistic and promising in the short term.
This capability is based on a continuous learning cycle often summarized in four steps:
AI agent cycle:
Perceive (analyze the situation or request)
Reason (identify the objective, plan the steps)
Act (execute the necessary action(s) via tools/APIs)
Learn (adjust its strategy based on the result or user feedback)
In other words, an AI agent “sees,” “thinks,” “acts,” and “learns.”
Thus, according to Dydu, agentic AI is not just an emerging technology: it is a logical evolution of intelligent assistance, paving the way for more fluid, proactive, and personalized user experiences, without ever relinquishing human control.
AI agents: how companies can take advantage of them without losing control
1. What opportunities do AI agents offer companies?
AI agents are ushering in a new era of intelligent automation: they plan, act, and adapt.
The benefits are numerous:
- Automation of multi-step tasks, such as planning, sending emails, generating reports, or customer follow-up.
- Time savings and operational efficiency, thanks to autonomous workflows capable of handling entire processes without human intervention.
- Increased personalization, taking into account context, history, and user preferences.
- Better use of internal data, made possible by API integrations and direct access to business tools (CRM, ERP, messaging, document databases).
Concrete examples:
- In e-commerce, an AI agent can automatically manage product returns and trigger the sending of a return label.
- In healthcare, it can coordinate appointments between multiple parties (patients, doctors, departments).
- In customer relations, it can anticipate needs: detect that a product is defective and offer a replacement without being asked.
3. What limitations and precautions should be considered?
Agentic AI is not without its challenges. Its rise raises major issues of control, security, responsibility, and ethics, which require a clear framework before any large-scale deployment.
- Human supervision and governance
The more autonomous an agent is, the more human supervision becomes essential. An agent may perform unvalidated actions or make rational decisions that lack discernment in complex or sensitive situations.
Beyond the legal question (“who is responsible in the event of an error?”), internal governance—rules of action, levels of validation, control mechanisms—is a key lever for limiting risks.
- Complexity and operational control
The proliferation of specialized agents increases the complexity of systems. Without clear orchestration rules, inconsistencies or conflicts of action may arise.
In addition, agents can place heavy demands on technical resources (APIs, data, workflows), making it essential to precisely control usage and costs.
- Security, compliance, and ethics
Data protection, decision traceability, and GDPR compliance remain fundamental requirements.
Even advanced language models can produce plausible but inaccurate responses. In an agentic environment, these errors can spread if they are not supervised. Finally, a technically optimal decision may not respect human intent, values, or implicit contextual constraints.
- In short, agentic AI should not be thought of as total automation, but as a system that is supervised, monitored, and aligned with clearly defined human, business, and ethical rules.
How does France compare to the rest of the world?
France is advancing at a steady pace, although it still lags behind the United States and China in terms of large-scale industrial deployment.
Solid strengths:
- According to the INPI, France ranks among the top 10 countries in the world for filing patents related to artificial intelligence.
- It has a recognized research ecosystem (Inria, CNRS, Paris-Saclay, Institut Prairie) and emerging champions such as Mistral AI, Hugging Face, LightOn, and Dust.
Obstacles to overcome:
- European regulations (AI Act) and the GDPR impose a strict framework on the transparency and traceability of automated decisions, which can slow down the deployment of fully autonomous agents.
- The lack of talent in AI and the slow adoption rate among SMEs remain areas of concern: according to France Digitale, less than one in two companies will have integrated advanced use of AI by 2025.
In summary, France is not lagging behind in research, but it needs to accelerate its operational adoption. Agentic AI could be a lever for transformation, provided that innovation is combined with a solid ethical framework.
What are the prospects for the evolution of agentic AI?
The global market for AI agents is estimated to be worth nearly $70 billion by 2030 (source: Gartner, 2025).
Two major trends are emerging for the coming years:
- Specialization: agents will become experts in a specific field (finance, healthcare, HR, e-commerce, etc.).
- Inter-agent collaboration: systems will be able to cooperate with each other to perform a common task, paving the way for true ecosystems of intelligent agents.
These prospects open up new horizons: proactive assistance agents, autonomous logistics flow management, predictive customer support, etc.
Conclusion: finding the right balance with agentic AI
Agentic AI is ushering in a new era for businesses: multi-step automation, proactive personalization, optimal data utilization, and more fluid interactions with customers. But this increased autonomy comes with major challenges: essential human supervision, shared responsibility, security, GDPR compliance, and ethical risks. Some complex or sensitive decisions still require human intuition and discernment, highlighting the importance of keeping control over agents.
The rise of AI agents does not signal the end of human intervention, but its transformation. By automating repetitive or operational tasks, agents allow teams to focus on complex situations, human relations, management, and decision-making.
New roles are emerging around the supervision, governance, and optimization of AI systems, placing humans at the heart of the value chain.
At Dydu, we design solutions that allow you to take full advantage of AI agents while maintaining the necessary control and security. Our modular and interoperable platforms facilitate the integration of autonomous agents into your processes, for smarter, more efficient, and more responsible interactions.
Discover our bots and the evolution of intelligent agents through Dydu solutions!
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