I want to share my experience in the cloud, AI, and agents development areas.
The term "agent harness" has gained prominence across major AI platforms like OpenAI, IBM, and Google, yet lacks a unified definition. Various companies utilize it differently, emphasizing its role in coordinating AI models with context and execution. This post explores the term's meaning and its significance within AI development frameworks.
AI coding agents increasingly participate in planning, implementation, review, testing, and documentation. This raises a new engineering question: who actually created the software artifact? This post explores why Git history alone is no longer sufficient, how engineering provenance could capture the role of humans, agents, models, tools, and context, and why this matters for intellectual property, accountability, and risk. The broader shift may no longer be from coding to review, but from idea to responsibility.
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 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.
The content explores the evolving role of AI in creative processes and its implications for intellectual property (IP) law. It highlights challenges around ownership, patentability, and copyright concerning AI-generated works. As AI advances, existing legal frameworks struggle to keep pace, prompting questions about creativity, innovation, and the need for new regulations.
This blog discusses the emergence of agentic AI, capable of planning and executing complex tasks autonomously, contrasting with traditional generative AI. The post emphasizes the importance of managing expectations, oversight, and ensuring transparency due to the unpredictability, including potential hallucinations associated with these systems. LangGraph is highlighted as a powerful tool for developing agentic workflows.
Thomas Suedbroecker's Blog 