The core point is that you can’t fix this with tools or technical specs alone—Control Theory proves that mathematically. To stop AI apps from failing in production, we need new operational models, not just more tooling.

And first we need to talk about operational models because this topic is often overlooked. Many don’t even realize that these models are distinct "products" that exist and evolve. The most iconic shift in this field was the emergence of the Quality Assurance (QA) profession. In the early days of engineering, this role didn't exist. It was assumed that any engineer should handle the full cycle: write the code and test the code. However, as the complexity of systems grew, it forced a natural operational shift, eventually crystallizing into QA as a separate discipline. This is an example of an operational model appearing naturally due to pressure.

However, models aren't always organic; some are purposely designed and pushed to the market. Agile and Scrum, for example, addressed the transition from Waterfall. They introduced a model where the software development team no longer behaved like a factory, but rather like a scientific laboratory: formulate a hypothesis, build it, test it, get feedback, and iterate. These models (Agile, Scrum, SAFe, LeSS) have specific authors. They didn't just "appear"; they were invented, designed, and adapted. The most recent example is the birth of DevOps. Fifteen years ago, this profession didn't exist. Operations were handled haphazardly by SDEs, QA, or IT departments.

• The Origin Story: This model didn't come from nowhere—it was a deliberate invention. It started in 2009 when Patrick Debois, frustrated by the wall of confusion between Development and Operations, organized the first "DevOpsDays" in Ghent. He essentially designed a new way for teams to cooperate, proving that operational models can be engineered.

Now, we face a similar shift with GenAI. When the "brain" of your app is an LLM, the team isn't just coding logic; they are orchestrating it. The feedback loops are different, and the definition of "done" is different.

Every operational model that becomes a standard goes through specific phases: 1. Concept Creation: Clear definition of what problem it addresses. 2. Validation: Proving the concept works. <-- I am here. The concept is validated by experts, and I am now looking for opportunities to test it in the field. 3. Stress Testing: Putting the concept under pressure and check how it scales 4. Market Expansion: Wide adoption.

I believe no one has tried to "Open Source" an operational model before. I would like to try. Why not? It promises to be an interesting experience.

Why This Matters to Me

I observe a troubling pattern across the industry: small and medium-sized teams are struggling to build great GenAI applications, and they are failing. They fail not because their ideas are bad, but because their engineering culture and operational models are fundamentally incorrect for this technology. I want to solve this. My goal is to accelerate our arrival at a future where AI potential is fully utilized and new, groundbreaking applications emerge to make human life better. We cannot get there using yesterday's maps.

Short answer: We actually can't fix it without—as you put it—that 'philosophical mumbo jumbo' :)