Today, a new approach emerges and gradually imposes itself: that of AI agent systems, whose boom should accelerate strongly this year.
French companies are struggling to pass their AI projects from the pilot stadium to a large -scale deployment. Indeed, they are hampered by persistent concerns about data confidentiality, quality of results and operating costs. However, according to Gartner, agentic artificial intelligence will be able to independently resolve 80 % of current requests in customer service, without any human intervention, by 2029. It is therefore a major turning point in the evolution of AI technologies at the service of companies, where performance is no longer based solely on the power of models, but on their ability to adapt finely to the real needs of organizations.
Targeted solutions to meet the specific challenges of companies
With AI agent systems, it is not a question of being “omniscient”, but of having an “exact knowledge”. AI agent systems are based on specialized architectures based on multiple agents. Unlike the so -called “general” AI models trained on immense corpus of data from the web and designed to answer a wide range of questions, IA agent systems adopt a more targeted approach. They rely on interconnected agents, each with their own tools, functions or linguistic models (LLM), and designed to solve problems specific to a sector, a trade or an organization. This approach meets a very concrete need. Indeed, companies now aspire to an intelligence of data – in other words, to an AI capable of delivering relevance, precision and reliability from their own business information.
Where general models aim to answer all questions – at the risk of not satisfying any expectations in a truly reliable way -, the AI agent systems are based on a modular composition. Each agent has a well -defined role and relies on specialized LLMs and configured functions to fulfill targeted tasks. Thus, an agent dedicated to customer support can perfectly collaborate with a financial forecasting agent within the same system, while maintaining optimal efficiency specific to his field.
This modularity makes it possible to provide truly tailor -made solutions, aligned with workflows, customer expectations and the sectoral specificities of each organization. It is no longer the omniscience that prevails, but the targeted precision.
Strengthen the reliability and transparency of AI systems
Many French companies still express reluctance to AI, due to its potential errors, biases or inconsistent responses. IA agent systems directly respond to these concerns by integrating human supervision and automated validation mechanisms. More and more organizations are opt for “human -in -law” devices in the loop, combined with tools that assess, control and refine the results generated before their production. This reinforced validation framework contributes to establishing greater confidence in deployed systems, an essential condition for promoting their adoption and maximizing their operational impact.
Indeed, the data constitutes the base of any system of efficient AI agents. And in a context where French companies want to really become “data-driver”, there is no shortage of challenges: heterogeneity of sources, information silos, lack of governance or even security risks.
Despite these obstacles, organizations are progressing, often through targeted experiments that demonstrate the value of AI before a rise in charge. This incremental approach makes it possible to build the skills, processes and infrastructure necessary for sustainable transformation.
The key lies in the implementation of an intelligence platform of data: a unified base to collect, govern, secure and use the data in an operational manner. Coupled with interfaces in natural language and the integration of proprietary data, this platform allows you to create tailor -made models, designed to understand and meet business needs. It also facilitates access to AI for non -technical profiles, thus democratizing its use throughout the business.
Moreover, according to a recent report by Economist Impact, almost 60 % of respondents believe that, within three years, natural language will be the main means for non -technical collaborators to interact with complex data.
The advent of AI agent systems, a revolution for companies
The future of AI in business is not based on ever larger and abstract models, but on specialized systems, orchestrated and integrated into the processes. This approach strengthens confidence, improves the accuracy of the results, and allows organizations to better meet their business challenges.
French companies can design their own AI agent systems, based on a robust data platform. By mobilizing their owner assets, they can develop intelligent applications adapted to their contexts: integration of vector bases for precise research, use of fine-tuning techniques or prompt engineering for expert reasoning, rigorous security and conformity.
The AI agent systems are not content to provide answers: they establish a new relationship with data and value. For companies ready to take the next step, the future of AI no longer rhymes with “general intelligence”, but with data intelligence – contextualized, controlled and performance oriented.
