This article explores why governance becomes more important as software engineering becomes increasingly automated. It describes the evolution from human-centric development to AI-assisted and agentic engineering, where AI systems no longer only generate code but increasingly participate in engineering decisions. The main argument is that faster software creation does not automatically lead to better software. As AI accelerates implementation, accountability, traceability, reviewability, and human approval become more important. Effective governance allows organizations to use AI capabilities without losing human responsibility. It helps make AI-assisted software engineering more transparent, more reviewable, and more trustworthy.
AI Coding Assistants, Agentic IDEs, and Privacy: From Chatbots to Operational Systems
This post compares various AI coding assistants/agents, emphasizing privacy from a developer's perspective. It highlights how modern AI systems function as integral tools in software development, shifting the privacy discourse from mere data training concerns to broader issues like data sovereignty, operational exposure, and pricing implications, particularly for individual developers.
Contextual Retrieval with Milvus: Better Retrieval, More Validation Responsibility
This post reflects on Contextual Retrieval with Milvus in RAG systems. It explains how generated context can improve chunk retrieval, but also changes the retrieval corpus. Once generated context is indexed, validation, traceability, and quality control become architectural responsibilities—not optional implementation details.
AI Conference 2026 — When Observations Become Confirmation About Real AI Systems
The AI Conference 2026 highlighted the evolution of AI discussions from theoretical capabilities to operational realities. Key themes included the importance of data quality, operational complexity, and risk management. Attendees noted a shift in software development roles towards reviewing AI outputs, emphasizing the need for understanding real problems rather than solely implementing complex solutions.
Building a Reproducible AI-Generated Project with ChatGPT, Codex, and Docling in VS Code
A structured experiment using ChatGPT and Codex in VS Code to generate a reproducible open-source Docling preprocessing pipeline with strict engineering constraints.
From first ideas to a working MCP server for Astra DB CRUD tools with IBM Bob
This blog post details the author's exploration of IBM Bob while building an MCP server for Astra DB. It emphasizes learning through experimentation in Code Mode, focusing on automation and iterative development. The author shares insights on prompt creation, workflow challenges, and the importance of documentation throughout the process, ultimately achieving a functional server setup.
