Last reviewed:
What is agentic AI? Definition and difference from generative AI
Agentic AI refers to the ability of an artificial intelligence system to pursue a goal autonomously: it plans steps, uses tools (software, databases, browser) and adjusts its actions based on the results it obtains. Where generative AI produces an answer to a request, agentic AI carries a task through to completion, under a level of human oversight defined in advance.
The term "agentic AI" took hold in 2025 to describe a paradigm rather than a product. An AI agent is the concrete system; agentic AI is the way of designing AI that makes it possible: a language model that decides the next step itself, calls tools, reads the result and starts again until the goal is reached. Anthropic, in a reference article published on 19 December 2024, distinguishes two families of "agentic systems": workflows, where the model follows a path written in advance by developers, and agents, where the model directs its own process and choice of tools. The vendor recommends starting with the simplest solution and adding autonomy only when the gain is proven. The degree of autonomy is therefore a slider, not a binary state. The market followed quickly, sometimes too quickly. On 25 June 2025, Gartner predicted that over 40% of agentic AI projects would be cancelled by the end of 2027, due to escalating costs, unclear business value or inadequate risk controls. The firm also denounces agent washing: rebranding assistants, chatbots or RPA tools as "agents" without real agentic capability.
Concrete example
Illustrative case (fictitious company). An industrial supplies distributor with 120 employees near Lyon receives some thirty quote requests by email every day. With conventional generative AI, a salesperson copies the request into an assistant that drafts a standard reply; they then check stock, prices and lead time themselves. With an agentic approach, an agent reads the email, queries the ERP for stock and the customer price, calculates the delivery time, prepares the quote in the sales software and submits it to the salesperson for approval before sending. The salesperson no longer drafts: they check and decide. The project only works if the agent's access is limited (read-only on the ERP, no discount above a threshold) and if every quote keeps a record of what the agent consulted.
Comparison
| Criterion | Generative AI | Agentic AI |
|---|---|---|
| What it produces | Content: text, image, code, summary | A task carried through: chained actions in software |
| Trigger | One request, one answer | A goal, then several steps decided by the system |
| Tool access | None or limited (web search, attached files) | Central: ERP, CRM, email, browser, via API or MCP |
| Human role | Reviews and uses the content produced | Sets the goal and limits, approves high-stakes actions |
| Main risk | False content (hallucination) | Wrong action in a real system, overly broad access rights |
| Example | Draft a reply to a quote request | Check stock, price, prepare the quote and submit it for approval |
FAQ
What is agentic AI in simple terms?
It is AI that does more than answer: you give it a goal, it breaks the work into steps, uses tools (email, ERP, browser) and checks the result before moving on. The level of autonomy and the human approval points are set by the company.
What is the difference between agentic AI and generative AI?
Generative AI produces content (text, image, code) in response to a request. Agentic AI often uses a generative model as its "brain", but adds planning, tool access and the execution of actions. The first drafts an email; the second can also find the file, prepare the attachment and schedule the sending.
What is the difference between agentic AI and an AI agent?
The AI agent is the concrete system deployed (a lead qualification agent, for example). Agentic AI refers to the paradigm and capability that make such agents possible. One speaks of an "agentic approach" or an "agentic system", and of an agent as one of its implementations.
What are examples of agentic AI in business?
Preparing quotes from incoming emails, matching supplier invoices with purchase orders, triaging and answering first-level customer support, document monitoring with summaries, or coding assistants such as Claude Code that modify and test code. In each case, the agent acts in real systems, under approval rules.
Is agentic AI reliable?
It is on bounded tasks, with well-defined tools and human approval at high-stakes steps. In June 2025 Gartner predicted that over 40% of agentic AI projects would be cancelled by the end of 2027 for lack of clear value or risk control. Reliability therefore depends mostly on scoping.
See also
Further reading
Building effective agents, Anthropic (reference article on agentic systems)
Sources
- Building effective agents, Anthropic, 19 December 2024. https://www.anthropic.com/engineering/building-effective-agents
- Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027, Gartner press release, 25 June 2025. https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027