Agentic AI: definition, how it works, and what is at stake
Agentic AI moves artificial intelligence from “answering” to “doing”. Here, in plain terms, is what it is, how it works, and what is at stake for businesses and the public sector.
Agentic AI refers to artificial intelligence systems able to pursue a goal autonomously: they understand a request, plan the steps, use tools and carry out multi-step tasks with reduced human intervention — but under supervision. Where a chatbot answers, an AI agent acts. It is an evolution of generative AI: generative AI produces content, agentic AI uses that content to accomplish concrete tasks.
AI agent, chatbot, copilot: what is the difference?
- Chatbot — Answers questions in plain language, but does not act beyond the conversation.
- Copilot — Assists the user inside a tool (suggesting, drafting), without running anything end to end.
- AI agent (agentic) — Pursues a goal: plans, uses tools, chains actions and carries out the task, under supervision.
How does an AI agent work?
Perception and understanding — The agent interprets the request and its context (text, voice, documents).
Reasoning and planning — It breaks the goal down into steps and chooses a strategy.
Tools and actions — It calls tools, APIs and databases in order to act (read, write, trigger).
Memory and learning — It keeps the useful context from one step to the next and adapts.
Human oversight — On sensitive decisions, a human approves; everything is traced and verifiable.
Agents increasingly rely on interoperability protocols (Model Context Protocol, agent-to-agent communication) to talk to other tools or agents. Trust and security are decisive there.
Multi-agent systems and orchestration
For complex tasks, several specialised agents collaborate under the coordination of an orchestrator — each taking one step (research, drafting, checking). This “collective intelligence” widens the reach of automation, provided the governance is clear.
Use cases
Reception and enquiries, drafting and minutes, case assessment, data analysis, internal support, public procurement, technical writing (tender documents and specifications)… The uses cover business and the public sector alike.
- AI agents for the public sector — The catalogue by role and by function.
- AI agent for architects — Business offer for project management (tender documents, specifications…).
- AI agent for farmers — Business offer for farm businesses (CAP, plant protection, grants…).
- Free audit (15 min) — Let us identify your first high-impact cases.
What is at stake
- Sovereignty and compliance — Hosting and data in France, GDPR and the AI Act: a requirement, especially in the public sector.
- Trust and safety — Reliability, control of errors (“hallucinations”), cited sources, guardrails and traceability.
- Governance and oversight — The principle “the agent assists, the human decides”: no automated decision about people's rights.
- Visibility and GEO — Generative engines answer instead of listing links: you have to be understood and cited by AI (GEO), on top of search and paid search.
- Return on investment and change management — Steer use case by use case, measure the real gain, support the teams before scaling up.
- Interoperability and costs — Emerging protocols (Model Context Protocol, agent-to-agent), integration with the existing IT landscape and control of running costs.
Why now?
The maturity of the models, the arrival of interoperability protocols and the integration of agents into everyday tools are accelerating adoption. According to a Cisco study (2025), a large majority of business-to-business companies expect part of their interactions to be handled by agentic AI within three years; other projections estimate that a significant share of digital interactions will be handled by agents by 2030. (Third-party projections, to be treated with caution.) The point is not to submit to this momentum, but to make it your own — with method and responsibility.
How to start
Map — Identify the tasks with the greatest potential, judged on impact, risk, adoption and feasibility.
Pilot — Launch one agent on a concrete, measured case, hosted in France, with human oversight.
Industrialise — Scale up the agents that prove themselves, securing data and governance.
Put agentic AI to work
Frequently asked questions
What is agentic AI?
Agentic AI refers to AI systems able to pursue a goal autonomously: they understand a request, plan the steps, use tools and carry out multi-step tasks with reduced human intervention, but under supervision. Where a chatbot answers, an agent acts.
How does it differ from a chatbot or from generative AI?
A chatbot answers a question; a copilot assists inside a tool; an agentic agent chains actions towards a goal (querying a database, filling in a form, triggering a task). Generative AI produces content; agentic AI uses it to accomplish tasks.
Is agentic AI reliable and safe?
It has to be framed: guardrails, traceability, cited sources and human oversight on sensitive decisions. Well designed, it reduces repetitive errors; poorly framed, it can be confidently wrong. Hence the importance of compliance (GDPR, AI Act) and of sovereign hosting.
What is GEO (Generative Engine Optimization)?
It is optimising your visibility with generative engines and AI agents, which answer the user directly instead of displaying links. GEO complements SEO: the aim is to be understood and cited by AI (clear definitions, structured data, FAQs).
How do we get started with agentic AI?
By mapping the tasks with the greatest potential (impact, risk, feasibility), then a pilot on a concrete, measured case, before industrialising. A short audit is enough to identify the first high-return cases.
What is an audit trail for an AI agent?
An audit trail records every action of the agent in a timestamped, tamper-evident way: who did what, and when. It is what lets a decision be reconstructed after the fact, and it is essential for traceability and inspections in regulated sectors. The AI Act makes it a requirement for high-risk uses.
What happens to my AI agent if I end the contract (reversibility)?
Reversibility is about your data: if you stop, you get it back in a usable format, within thirty days. On the 100% Sovereign plan, the hardware is yours and stays on your premises; the agent's software is licensed for the term of the contract and can no longer be used once it ends, along with maintenance, updates, models and support. You leave with your data and your hardware, not with the agent — clause 21 of the general terms says so in black and white.
How is my sensitive data protected before a call to an external AI?
By an anonymisation filter: before any call to an external AI, sensitive data — names, identifiers, contact details — is masked or replaced. Protected information stays on your side; the external service sees only neutralised data. On a configuration where the model runs locally the question does not arise: nothing leaves.
What are the "tokens" an AI bills for?
AI models bill by usage, in tokens: small units of processed text, both in and out. It is the only genuinely variable part of an agent's cost. We re-invoice it to you at cost, with no margin at all, through a prepaid wallet: we earn nothing on it, so nothing gives us an interest in seeing it rise.
Move from theory to a first agent
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