Agentic AI accelerates developers’ work, but human governance — clear roles, oversight, accountability — remains the real key to large-scale deployment.
L’artificial intelligence agentique marks a new stage in the lives of many of us. We see the most change in the area of software engineering. Unlike early generative AI tools that provided step-by-step assistance, these agents are designed to perform complete tasks: they plan, execute, chain tools, create, and test changes. This dramatically changes the speed at which we can build software, leading to high expectations. However, what we have learned from applying AI tools to software engineering is that the most structurally significant change is not technological. It is organizational and, above all, human.
Agents who execute, humans who decide
Agential AI is changing the nature of engineers’ work, but it is changing the conditions of that work even more. It allows developers to build faster and with greater confidence. It can also help us fix security vulnerabilities much more quickly. But it does not replace human judgment, contextual understanding, or responsibility for technical decisions. An agent can act, but he has neither taste nor creativity. It does not decide direction, define business priorities, or understand business trade-offs. This boundary where human judgment comes into play remains, and will always remain, essential.
Without structured processes, acceleration becomes a risk
Agentic AI changes the pace of work, but not its objective: it allows teams to move forward more quickly, but in return requires greater discipline. Indeed, without a clear process, acceleration can quickly lead to bottlenecks in the flow between idea and implementation. While each individual’s productivity is rapidly accelerated, the need for best practices that enable teams of agents and humans to work together becomes even more important as each individual gains speed. When you ignore processes, the price you pay can be increased technical debt, complexity that is difficult to control, or a loss of clarity in decision-making. Agentic AI does not work outside of business constraints: it crosses them, accelerates them, but can also put them to the test.
AI governance: the strategic issue that companies underestimate
The hardest part remains the same: the initial creative spark that takes an idea to improve a business or create a product that people need. Then, when you’re a large enterprise, you have to get those ideas to customers in a reliable and repeatable way, allowing hundreds of developers using thousands of agents to merge reliably and consistently, which requires considerable human judgment and skill. The main challenge lies in management and supervision. The more a system is capable of acting autonomously, the more strategic its governance becomes. Who sets the rules? Who validates the actions? Who can stop an agent, correct their trajectory or regain control? Human governance is what builds trust and deploy these technologies on a large scale.
Flexibility and independence: the new criteria for choosing an AI tool
With the industrial landscape rapidly changing, choice has never been more important for businesses. Currently, in the space of a few weeks, we are seeing
exponential improvements in AI capabilities. What might be the most advanced AI model or tool today could become obsolete before a standard enterprise procurement cycle is even completed. Organizations should therefore look for options that provide them with the greatest flexibility, the ability to avoid dependence on a single template provider, and the ability to work with other tools used by the business.
Deploying agentic AI: a leadership responsibility above all
This requirement goes well beyond the technical field. It is above all managerial. Recent work on AI governance shows that the difficulties encountered by organizations are rarely linked to the performance of the tools. They arise much more often from the absence of clear roles, defined responsibilities and explicit human supervision mechanisms. Without this framework, agential AI can create an illusion of efficiency: more activity, faster, but without global vision, less impact.
Final responsibility cannot be delegated to an agent. Even when AI acts autonomously, decisions about which path to take lie with individuals and organizations. While the latest generation of agential AI tools makes it possible to delegate a large part of the work, it does not delegate responsibility.
