Section 2 – Structuring
Agentic AI introduces a healthy tension into organizations. On the one hand, teams want to rapidly create their own agents to solve local problems. On the other, the enterprise needs reliable, integrated, robust and governed systems to transform its operations.
This tension is not a problem to be eliminated. It’s a dynamic to be structured.
It would be a mistake to try and decide between these two approaches. The organizations that really make progress are those that organize the coexistence of local experimentation and central capacity.
In this article
Self-service is a must
Enabling teams to create their own agents accelerates adoption. It reduces dependency on IT teams, brings out concrete use cases and develops a practical understanding of AI. As with BI self-service a few years ago, this phase is essential for bringing real needs to the surface and quickly testing ideas in the context of day-to-day work.
Local agents often serve as laboratories. They enable business teams to explore, identify irritants and understand what could be automated. In some cases, they are already replacing uses historically covered by RPA(Robotic Process Automation ) or in-house scripts. Where RPA imposed rigid rules and fixed sequences, agents can reason, adapt and handle variability.
This capacity for experimentation is healthy. It allows us to accumulate experience in the field and identify use cases that deserve to be industrialized.
But these local agents are limited in scope.
They improve team efficiency, but do not transform the organization. Without systems integration, supervision and common standards, they remain isolated. Their value is real, but local.
The limits of no-code and low-code
The no-code and low-code tools enable you to quickly create simple agents. They are useful for exploring, testing and understanding. But they have structural limitations as soon as the agent needs to act in real systems, manage complex permissions or integrate with multiple processes.
Business teams can identify opportunities, test scenarios and formulate requirements. They cannot, and should not, single-handedly support systems that make decisions, trigger actions or manipulate sensitive data on a large scale.
As impact increases, so does responsibility and complexity.
Without a framework, the multiplication of no-code agents quickly leads to a technological accumulation that is difficult to maintain, govern and evolve. The result is dozens or even hundreds of isolated agents, sometimes in duplicate or triplicate, each optimized locally but with no overall coherence.
Transformational agents require a different kind of discipline
Agents that actually change the way work is done are of a different nature. They need to be integrated into enterprise systems, orchestrated with other agents, and capable of acting reliably in real environments. They must respect business rules, permissions, security constraints and traceability requirements.
This implies global choices of architecture, governance and prioritization. These agentic systems cannot be built by isolated teams alone. They require a central capability capable of defining standards, managing integrations, ensuring security and maintaining systems over time.
The parallel with BI is useful: BI self-service has enabled teams to explore data, but it has never replaced a centralized BI function capable of structuring models, guaranteeing quality and ensuring consistency.
Agentique follows the same logic.
Don’t confuse speed of adoption with real impact
As we’ve just seen, authorizing self-service without a framework quickly leads to a multiplication of agents that are useful locally, but incoherent globally. Conversely, extreme centralization slows adoption and prevents learning in the field.
Letting everyone create agents is no substitute for industrialized agentic capacity throughout the organization. But wanting to control everything from the outset prevents the organization from learning.
The right approach is to allow local experimentation while building a central capacity to industrialize what works. Local agents then become signals. Transformational agents become levers.
The challenge is not to choose between autonomy and control, but to organize their coexistence within a structure that enables the organization to learn, industrialize and evolve.
TL;DR
- – Self-service accelerates adoption. It does not replace a structured agentic capability.
- – Teams need to be able to experiment and create local agents to learn and bring out the right use cases. But agents that really transform processes require integration, governance and common standards.
- – The challenge is not to choose between autonomy and control. It’s about organizing a clear coexistence between field experimentation and central industrialization, to avoid the accumulation of isolated agents and build sustainable capacity.
