“Pacho, I don’t know what to do; the AI gave me a very strange answer and I blocked myself.” This scene is repeated daily in my workshops with technical, founder and corporate teams.
Accustomed to traditional software, an error or unexpected response freezes us, since we assume that an extensive manual or an expert is required to decipher it.
My answer always surprises them: “Why don’t you ask the model itself what it means and how to solve it?” For the first time in the history of computing, we have self-explanatory technology.
Interacting with an advanced language model is more like collaborating with a colleague than querying a database. If two specialists do not understand each other at the beginning, they build a bridge through questions and mutual calibration. The mistake of many people is treating AI as just another rigid platform.
The real obstacle to adoption is not the infrastructure or the mathematics of neural networks, but a mental barrier: the limit is set by the user’s curiosity. Once this limitation is overcome, the challenge arises of conceiving the artificial mind and addressing hallucinations, which requires auditing its reasoning process.
When looking at the most advanced models, we do not see a replicated human cognition, but a different, almost alien logic, as Jakub Pachocki (Chief Scientist of OpenAI) points out in “An Alien Mind”. Their article shows that monitoring the reasoning of these systems is no longer viable as their complexity and intelligence increases.
Although the tendency of these models to invent or hallucinate has been stigmatized, those of us who work with AI know that hallucination is the engine of their creativity to formulate original solutions. So, accepting that we cannot supervise every step and that hallucination is a creative engine, the challenge is to design the architecture that guides them correctly.
Here loop engineering and autonomous execution environments become relevant. Manually auditing thousands of lines of code is unsustainable and creates a bottleneck. The new paradigm consists of delegating tasks accompanied by rigorous specifications and automated tests in an environment that allows AI to iteratively self-correct.
The agent system generates proposals, runs tests, evaluates failures and self-corrects until the acceptance criteria are met. In fact, these systems are already a tangible reality in some startups in the region, driven by advanced reasoning models and greater maturity of their users.
This dynamic of self-feedback redefined our relationship with the software: we went from being micro-managers of instructions to architects of validation systems.
By understanding that AI can calibrate itself in well-structured loops, we overcome the fear of delegating and discover a virtually infinite execution ceiling, making software development one of the most natural use cases for AI.
And you still doubt whether or not to implement AI in your company? Or are you already part of those who have advanced autonomous systems?
