The agents of AI assist us on a daily basis with an astonishing fluidity – a feat made possible by an operational database, invisible but essential pillar of their reactivity.
When we talk about artificial intelligence, we often imagine robots or very complex, a little abstract programs. However, the “AI agents”, these new smart assistants, are already invited discreetly into our daily lives. They answer our questions, recommend personalized products or assist us at work. And this trend is only accelerating lately.
Nevertheless, if these tools seem also natural, almost human in their interactions, it is largely thanks to a very specific, and often invisible technical infrastructure: the operational database.
Behind the screen, how do these AI agents work?
The idea of the old limited and rigid chatbot, confined to preprogrammed responses, already belongs to the past. Today, AI agents learn and react permanently, a bit like particularly attentive human assistants. Concretely, they analyze a multitude of information in real time, reflect, even anticipate needs, then make decisions independently.
To operate so fluid, these agents generally rely on powerful AI models, like large language models such as GPT. However, these complex technologies require continuous and above all ultra-fast data flows. Impossible, under these conditions, to patiently wait for long and deferred analyzes, they need immediate answers, without any noticeable deadline: this is exactly where the operational database is in play.
Practical example: an AI agent in your favorite store
Imagine a concrete situation. You are on an online store and interact with an integrated AI agent. He already knows your history and your preferences, which he draws from your user profile. In an instant it accesses an enriched product catalog – with images, videos and detailed descriptions – and can check in real time the availability of the desired item in the nearest stock. He even goes so far as to consult external sources – such as the opinions of other customers or the trends of the moment – to refine his recommendations.
In parallel, it operates unstructured documents, such as PDF manuals or tutorials, to answer your questions precisely. And above all, it includes the nuances of your requests thanks to an advanced semantic search, based on what are called embedding vectors.
Why the analytical bases are not enough
Some might believe that a classic analytical base would do the trick. After all, they are designed to manage massive amounts of data. But here it is: their logic is different. The analytical bases are designed for in -depth analyzes, often carried out over long periods, with longer treatment times.
However, AI agents operate in immediacy, they require constant, fast and simultaneous exchanges. Each millisecond counts. It is like comparing a TGV, designed to quickly cover a long distance without stopping, and a metro train, which must constantly start, stop and immediately resume its race at each station.
Simplify rather than multiply technologies
Faced with these requirements, we often see an accumulation of technological solutions: a base manages transactions, another Embedding vectors, and a third takes care of the conversational cache, for example. However, this multiplication quickly becomes problematic. Not only does it increase technical complexity, but it also slows the whole system, creating more problems than it solves.
Conversely, a unified operational base simplifies this whole process. It naturally absorbs activity peaks, supports a wide variety of formats, and offers permanent access to crucial data. Result: fluid performance, simplified maintenance and much more pleasant user experience.
A decisive choice for the future
So yes, talking about operational databases may seem very technical, even abstract. However, it is precisely this invisible infrastructure that will allow AI agents to be perceived as intelligent, reactive, and deeply human in their interactions with us.
Ultimately, the choice of an operational basis is much more than a technological choice, it is the sine qua non condition so that artificial intelligence can finally keep its promises of a fluid, intuitive and truly focused digital experience.
