---

The world of Generative Artificial Intelligence is moving fast, and the spotlight today is on agentic models. Until recently, Large Language Models (LLMs) were seen mainly as powerful engines for generating fluent text. That era is now giving way to something bigger. Agentic models go beyond producing answers. They can plan actions, retain memory, make decisions, and interact with external tools. This shift turns AI from a passive responder into an intelligent digital assistant capable of taking real operational steps.

The real breakthrough comes from RAG systems (Retrieval Augmented Generation). An agentic LLM with RAG is no longer limited to the data it was trained on. It can retrieve up-to-date information, combine it with its internal knowledge, and use it to solve complex problems. The user no longer needs to manually guide each search. The agent itself decides when to query documents, databases, or archives, and how to reframe the retrieved information to deliver more accurate, contextualized, and reliable answers.

For companies, this represents a decisive leap forward. With agentic models, organizations can manage far more than just conversations. We are talking about decision support, predictive maintenance, risk analysis, strategic planning, and the handling of complex scenarios. By integrating proprietary data, internal systems, and external sources, the agent becomes a tool capable of orchestrating information and generating insights that directly support the business.

Take the energy sector as an example, where the variables are vast and constantly shifting. An agentic LLM with RAG can monitor live data, analyze historical archives, evaluate market conditions, and suggest optimal management or energy trading strategies. Or consider the legal and regulatory world, where an agent can sift through massive amounts of documentation, summarize key content, and provide decision support with greater speed and clarity.

The competitive advantage is not only technological but also organizational. With agentic models, businesses reduce the time wasted searching for information, improve the reliability of their analyses, and benefit from adaptive tools that evolve alongside the company. AI stops being an accessory and becomes a genuine operational partner.

The future of Generative AI will be defined by the synergy between agentic models and RAG systems. Companies that embrace this shift will not only streamline their internal processes but also create new services and innovative products. This is not science fiction, it is a transformation already underway. And this is exactly where we are focusing as a startup, building solutions that turn the power of agentic Large Language Models into real value for organizations.