I want to share my experience in the cloud, AI, and agents development areas.
The blog post by Thomas Suedbroecker addresses the evolving governance challenges as AI becomes an active participant in engineering decision-making. It highlights the shift from human-centric to AI-assisted development and the resulting review gap. The author proposes specialized governance structures and emphasizes the importance of traceability for accountability in automated environments.
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.
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.
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.
The article continues the exploration of the AI Operational Complexity Cube, emphasizing that modern AI systems encompass software, infrastructure, and probabilistic AI components. It highlights the need for comprehensive testing approaches that consider interactions across these dimensions, as proper evaluation requires observing behaviors that emerge from integrated systems rather than isolated code.
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.
AI systems rely heavily on data quality, which is often overlooked despite modern technical architectures. Issues like outdated, incomplete, or misaligned data can undermine system reliability, regardless of the sophistication of the components. Effective AI requires both high-quality data and solid technical infrastructure to meet user expectations and ensure trust.
AI is transforming the software development lifecycle, shifting focus from coding to reviewing AI-generated systems. While AI tools simplify software generation, building trustworthy systems remains complex. Traditional review processes may no longer suffice. This raises a critical question: how can humans responsibly.
This post discusses a local setup utilizing IBM Bob to generate an agent for watsonx Orchestrate, specifically with tools from the Galaxium Travels MCP server. It explains the architecture, customization of Bob, and integration with various components, providing both learning and practical implementation value for developers.
This blog post explores the paradox of AI's growth potential versus the increasing trend toward data protectionism. It highlights how AI tools are hindered by data access limitations, posing risks to innovation. The observation implies that as data becomes more valuable, organizations may withhold it, undermining the openness that has historically fueled AI development.
Thomas Suedbroecker's Blog 