As businesses begin to use autonomous AI systems, managing these technologies effectively has become a key part of daily operations. These systems, known as agentive AI, can interact with various applications, make decisions, and perform tasks with minimal human oversight. While these systems offer the potential for significant improvements in efficiency, they also bring new challenges in terms of control and risk management. In France and across Europe, new rules on artificial intelligence, cybersecurity, and operational resilience are being introduced to help companies better manage their digital risks and ensure transparency in their practices. The AI Act and NIS2 are two major pieces of legislation that set new requirements for AI and cybersecurity, while DORA focuses specifically on strengthening the resilience of the financial sector. Together, these regulations push companies to rethink how they manage their digital operations. However, as the use of AI agents grows rapidly, many businesses may struggle to keep up with the demands of managing these systems effectively. As a result, governance — once primarily focused on compliance — is now becoming a central part of how companies operate. AI has evolved beyond simple automation and tools that generate content. Today, companies are experimenting with agentive AI that can interact with different systems, make decisions, and take actions on their own. According to the State of AI 2026 report by McKinsey, 62 percent of companies are already testing AI agents, and 23 percent have deployed them on a larger scale in at least one area of their business. These AI agents can access more data and interact with multiple environments, allowing them to perform tasks that were previously done by humans. This shift raises an important question: how can companies balance innovation with control? The answer lies in designing systems that include access management, traceability, and compliance requirements from the very beginning. This approach allows AI agents to operate within a clear framework that defines their capabilities, data access, and the level of human oversight needed. By embedding governance into the development process, companies can ensure that AI agents act within established boundaries while still contributing to productivity and security. Despite the benefits, the growing use of AI agents also presents challenges. A recent State of Application Development study by OutSystems found that 94 percent of companies are worried about the increasing complexity of managing multiple AI models and autonomous systems. Without a unified framework, this expansion can lead to confusion around access, data usage, and accountability. According to Deloitte, only 21 percent of companies believe they have a mature system in place to manage agentive AI, highlighting the gap between the speed of AI development and the readiness of governance structures. As AI systems become more autonomous, companies must also rethink how responsibility is distributed within their organizations. A recent Capgemini report shows that trust in fully autonomous AI agents has dropped from 43 percent to 27 percent in a year. This decline underscores the need for clear guidelines on which decisions can be made by AI and which require human oversight. As AI systems interact across different departments and applications, managing these interactions becomes more complex, requiring governance mechanisms that are built into the architecture from the start. Ultimately, the rise of agentive AI challenges companies to reconsider how much control they are willing to delegate to software. As AI moves from generating information to taking actions, governance must be embedded directly into the system's design. For businesses, the success of agentive AI will not just depend on the number of AI agents deployed, but on their ability to maintain control and ensure that these systems operate within defined boundaries.