Why is this a new paradigm?
 

Moving from a reactive to a proactive model
 

• Before: Applications based on generative AI operated mainly on a reactive model: the user asked a question, and the AI responded (e.g. ChatGPT, Copilot).
   
• Now: Agentic applications are proactive, they can define goals, plan actions, and execute them autonomously.
   

From text generation to autonomous action
 

• The first generative AIs were mainly focused on content creation (text, code, images).
   
• Agentic applications add the ability to act on their environment via tools, APIs and external interfaces.
   
• For example, a legal AI assistant can not only draw up a contract, it can also check the laws in force, extract case law, interact with a document management system and send notifications to a lawyer.
   

The integration of memory and iterative learning
 

• Before: LLM models worked with limited context (small prompt size).
   
• Now: Agentic applications incorporate persistent memory, allowing them to learn and evolve over time.
   
• Example: An AI agent in charge of customer relations can remember previous exchanges and adjust its recommendations accordingly.
   

The emergence of collaborative agents
 

• Agents are no longer isolated: they can communicate with each other, share data and perform tasks in coordination.
   
• This opens the way to distributed AI ecosystems, where several specialised agents work together to achieve complex objectives.
   
• Example: In an industrial environment, an AI maintenance agent could analyse sensor data, trigger a request for intervention to a planning agent, who would in turn assign a task to a logistics agent to order the necessary parts.
   

Will this paradigm replace traditional applications?
 

Rather than completely replacing traditional software, this paradigm complements it by adding a layer of intelligence and autonomy.
 

Cases where agentic AI is an advantage
 

• Automation of complex and repetitive tasks
   
• Business process optimisation (e.g. finance, healthcare, industry)
   
• Applications requiring rapid decision-making based on dynamic data
   
• Advanced conversational interfaces
   

Where traditional solutions remain dominant
 

• Software requiring strong human control (e.g. CRM, ERP)
   
• Regulated applications where AI must remain under supervision
   
• Environments where error can be critical (e.g. medicine, defence)
   

Comparison with other IT paradigms
 

The agentic paradigm can be compared with other major developments:
 

Conclusion: a revolution in progress
 

The"Generative AI Agentic Application ’ is indeed a new paradigm, which will profoundly transform the way software is designed and used. This model introduces a new form of autonomy, where AI no longer simply responds, but acts, learns and interacts intelligently.
 

We are at the beginning of this transition, and the development of tools such as LangChain, LlamaIndex, AutoGPT and Devin AI shows that agentic applications will gradually become established in many fields. Yes, we can therefore consider the agentic application of generative AI as a new paradigm in artificial intelligence and software development. This paradigm goes beyond simple user-language model interaction and introduces concepts of autonomy, decision-making and the execution of complex tasks without constant human intervention.