The real challenge of AI in business is no longer just hallucination, but its management: observing, understanding and correcting agents to guarantee reliable, sustainable and controlled uses.
When we talk about artificial intelligence in business, the same worry comes up again and again: hallucinations. However, if they remain a subject for vigilance, they no longer constitute today the main obstacle to the adoption of AI. Progress made in recent months has significantly improved the reliability of the models. In many cases, the real issue now lies elsewhere.
The challenge has become operational
AI systems are capable of producing impressive results, but their effectiveness depends less on their intrinsic intelligence than on the environment in which they operate. To create value in a sustainable manner, they must be governed by appropriate processes, controls and verification mechanisms.
An analogy helps us understand this evolution. If the large language model (LLM) constitutes the brain, what we call the “harness” is the body. It brings together the tools, rules and processes that allow the model to interact with its environment: search engines, documentary bases, command interfaces, APIs and even memory systems. It also includes the safeguards that govern its behavior and secure its actions. However, in many cases, the quality of this execution framework is as important as that of the model itself. A high-performance system will produce disappointing results if deployed in a poorly designed environment. Conversely, a structured system makes it possible to significantly improve the reliability, repeatability and quality of results.
This is precisely where observability comes in.
In the world of software, observability is about understanding what a system does, why it does it, and when it malfunctions. This logic becomes essential for AI. Companies need to be able to know which sources an agent consulted, what tools they used, what path they followed and when an error occurred.
The stakes are high because the most significant failures observed today do not necessarily come from isolated hallucinations. They often result from a progressive accumulation of errors during complex autonomous processes. When an agent carries out numerous tasks, a small approximation can influence the next one, then the next one again, until it creates a significant gap with the initial objective.
From the outside, the result may appear to be a content error or a degradation in the quality of a document. In reality, the problem often finds its origin in a succession of micro-failures that go unnoticed.
Observability makes it possible to detect these deviations before they become critical. Rather than being limited to the final result, it provides access to the intermediate stages of reasoning and execution. Organizations can thus track the sources mobilized, analyze the decisions made, measure confidence levels and identify abnormal behavior before errors propagate.
This capacity for control becomes all the more important as a preconceived idea persists: it would be enough to add more autonomy to agents to improve their performance. However, facts show that autonomy, without discipline, can on the contrary amplify errors. The value lies not just in the capabilities of the model, but in the quality of the processes around it.
The example of software development agents is particularly enlightening. If they often obtain good results, it is because their productions are easily verifiable. Code can be tested, compiled and validated automatically, providing immediate feedback on its quality. This feedback loop limits errors and continually improves performance.
When rigor becomes a competitive advantage
The same principle must now be applied to all business uses: document generation, customer support, compliance, data analysis or decision support. The goal is not to blindly trust AI, but to create mechanisms to verify, measure and correct its actions.
This does not mean, however, that the human element disappears from the process. His role is evolving. Rather than constantly intervening to correct errors, he can focus his attention on risky situations or unexpected behavior. Observability then provides the visibility necessary to intervene at the right time and with the right level of information.
The future of AI in business will therefore not be determined solely by the arrival of ever more powerful models. The organizations that will derive the most value will be those that know how to combine intelligence and rigor. They will deploy efficient systems, but above all observable, transparent and controllable.
Because AI does not need to be perfect to transform businesses. Above all, it must be understandable, measurable and correctable. Like testing and monitoring in software development, observability is fast becoming an essential prerequisite for deploying AI on a large scale in a reliable and sustainable manner.
