AI Agents are Breaking the Glass

The world evolves at a dizzying pace, and data is no exception; the patterns we consider solid today can change tomorrow. However, we remain tied to dashboards, which in some way function as static windows toward a dynamic reality. The problem of a window is that, although it allows us to see outside, it forces us to look from a rigid angle.

Added to this static perspective is the slowness to adapt. In data analysis, each answer usually opens the door to ten new questions. By the time we manage to update a board, the context has already mutated and the analysis remains obsolete.

This cycle translates into an inefficient expenditure of time and energy:

The business user has a doubt. > The BI analyst receives the requirement. > The data engineer modifies the structure. > The changes are made in the dashboard.

By the time the changes impact (4), the user already has new needs (1). This vicious circle generates a critical uncertainty in decision-making.

Here is where an AI agent, capable of responding to queries in real time, becomes the ideal complement.

Benefit: your resources do not have to memorize anything nor strive to find the chart that answers their question but simply. Each dashboard answers a single question, and if you need to ask combined questions, you want to centralize the decision-making and the interface through which.

It is like going out and walking through the landscape instead of just looking at it through the glass. However, this paradigm shift faces us with a fundamental technical challenge: How do we get an agent to respond with precision and without hallucinations? The answer is not only in the language model, but in the semantic layer that gives it context.

If you give an expert analyst a raw table, without metadata or definitions, it will take them time to decipher it; the same happens to an AI agent. To reason correctly, the AI needs to understand what each field represents, what is its level of aggregation and, above all, what is the business logic that connects that data.

To provide the agent with this reasoning, it is fundamental to implement a Semantic Layer. This acts as a "wrapper" that describes and organizes the data, allowing the AI to interpret them with common sense and business logic. Tools like DBT (Open Source) or Looker facilitate the creation of this code that translates technical complexity into business concepts.

It is fundamental to understand that a semantic layer is not a magic wand: an agent cannot repair broken data; it will simply amplify the error. The utility of the AI depends directly on a solid database and a deep understanding of the processes. [LINK (Data warehouse)]

In Sailors, we work at the core of this challenge. Not only do we consolidate your data so that they are consistent, but we design that vital semantic layer that unites the needs of the business with the technical infrastructure. The result is the elimination of the endless back-and-forths between teams. Doubts are resolved in the instant in which they arise, allowing your organization to react with speed and confidence before a market that does not wait for a dashboard to be updated.

If you could answer any question now without errors, you have the truth before your eyes? What do you do? [LINK (activation)]

Dare to break the glass and jump outside.