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Dan Patrascu-Baba

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Dan Patrascu-Baba

CTO @ Atherio | Architecting Teams, Systems & AI-Augmented Engineering

I’m a CTO and software architect with a background in designing and evolving enterprise systems across complex, multi-product environments. I’ve worked on unifying data models, modernizing legacy architectures, and helping engineering teams ship reliable software at scale using Azure and .NET. I was part of the core group introducing AI-assisted engineering workflows in a 5,000+ engineer organization, working through the real challenges of adoption, architectural constraints, and quality at scale. I later transitioned to full-time CTO of an AI-native startup, where I designed the engineering process around AI-assisted workflows from day one. By aligning architecture, domain boundaries, and development practices with AI collaboration, we significantly compressed delivery timelines compared to traditional team structures.

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Designing Architectures That AI Can Work With

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AI-assisted development doesn’t just change how we write code, it exposes how well (or poorly) our systems are structured. In many teams, AI struggles not because the tools are weak, but because the architecture is unclear: implicit domain knowledge, blurred boundaries, and inconsistent patterns. Having worked on introducing AI-assisted workflows in a 5,000+ engineer organization and later designing an AI-native engineering setup from scratch, I’ve seen a consistent pattern: AI effectiveness is directly proportional to architectural clarity. In this session, we’ll explore: - Why documentation is more important than ever - Why bounded contexts matter more than ever - How domain models influence AI output quality - Making architectural constraints explicit - Reducing ambiguity in large codebases - Practical refactoring patterns to increase AI leverage This talk is for architects who want to design systems that are not only maintainable by humans, but understandable and usable by AI.