🤔 “𝗪𝗿𝗶𝘁𝗲 𝗙𝗶𝗿𝘀𝘁 𝗼𝗿 𝗕𝘂𝗶𝗹𝗱 𝗙𝗶𝗿𝘀𝘁? 𝗪𝗵𝘆 𝗔𝗜 𝗶𝘀 𝗥𝗲𝘄𝗿𝗶𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗥𝘂𝗹𝗲𝘀 𝗼𝗳 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁

This week I had the pleasure of listening to a presentation by Brent Smith, who highlighted the value of prototyping and empowering builders in the age of AI.

🔧 Why Prototyping Matters

Prototyping isn’t new—it’s a smart investment in any product development process. It enables early detection of design flaws, improves usability through real user feedback, and reduces costly mistakes before full-scale production. Prototyping aligns designs with manufacturing constraints, accelerates time to market, and builds stakeholder confidence by turning ideas into tangible, testable solutions.

🎙️ In a fantastic interview “How AI will change software engineering – with Martin Fowler” at the Pragmatic Engineer Podcast [1], Martin Fowler highlights the non-determinism introduced by Agentic AI as the big challenge for adoption. So big of a challenge that he compares it to the evolution from assembler code to higher-level programming languages.

🤔 Indeed, I see a lot of customers struggling with this non-determinism. What is correct? How do I evaluate a system? What about cascading effects in multi-agent systems?

𝗧𝗵𝗲 𝗔𝗜 𝗗𝗶𝘀𝗮𝗽𝗽𝗼𝗶𝗻𝘁𝗺𝗲𝗻𝘁 𝗚𝗮𝗽: 𝗔𝗿𝗲 𝗪𝗲 𝗠𝗲𝗮𝘀𝘂𝗿𝗶𝗻𝗴 𝗣𝗿𝗼𝗴𝗿𝗲𝘀𝘀 𝗼𝗿 𝗝𝘂𝘀𝘁 𝗖𝗵𝗮𝘀𝗶𝗻𝗴 𝗛𝗲𝗮𝗱𝗹𝗶𝗻𝗲𝘀?

There’s been incredible progress in #AI tools for software engineers—new agents, coding assistants, and integrated workflows are launching every week. Yet, when talking to customers, a common question keeps surfacing: are these AI investments really delivering value, or are they still more hype than help?

That’s why I found the latest Tech Lead Journal podcast with Laura Tacho (CTO at DX ) so insightful [1]. The episode dives into real-world research on AI adoption—and tackles the tough questions leaders are facing:   •  Why “acceptance rates” often mislead organizations   •  How to move past buzzwords and measure true AI impact (e.g. with DX’s practical AI Measurement Framework)   •  Which use cases save time today (surprisingly, stack trace analysis outranks code generation!)   •  Why AI should be treated as a strategic team extension, not a “magic bullet” or a replacement for developers.

Can I escape the never-ending cycle of “just” toying with new models to production?

Most of us have been there. Trying out a new thing is super interesting to many of us. We are curious to understand what we could do, how it works. But then applying to reach a goal, while interesting at first, often entails a lot of efforts, which are not particularly exciting.  While applicable to pretty much any aspect of our lifes, this is particular true in IT. Taking a proven concept into production is hard. 

RAG - just a poor engineering workaround?

My week kicked off nicely with some inspiring talks on an internal conference. In one of the talks Johannes Langer dived deep on how to build production-ready RAG systems. I answered his opening questions to the audience - “What is RAG?” - with “𝗞𝗼𝗳𝗳𝗲𝗿𝗸𝗹𝗮𝘂𝘀𝘂𝗿”, which translates to 𝗼𝗽𝗲𝗻 𝗯𝗼𝗼𝗸 𝘁𝗲𝘀𝘁 in my head.

Thinking more about this analogy, I find it is helpful to approach the question if RAG is just a workaround to overcome limitations of our current foundation models or is here to stay, one a more conceptual level. The German wikipedia article on “𝗞𝗼𝗳𝗳𝗲𝗿𝗸𝗹𝗮𝘂𝘀𝘂𝗿” talks about some of the motivations for this kind of test: huge efforts for students on memorising independent facts are eliminated, the test scope can be wider and the test is focussing more on the ability to creatively think and find new solutions approaches. In other words this approach is frugal with students resources and incentives creation of new solutions.