Fu Quanzhi is a founding member of AgentsZone and a senior Agent practitioner dedicated to systematizing the fragmented field of Agent engineering — moving it from ad-hoc practice toward a rigorous, deployable engineering discipline.
Fu has deep roots in both industry and academia. In industry, he worked at Alibaba Cloud’s infrastructure platform on core container networking modules. In academia, he is currently pursuing a PhD in Computer Science at Virginia Tech, researching system infrastructure for the Agent era, and has conducted visiting research at Carnegie Mellon University.
He currently serves as lead editor of High-Quality Delivery at 100x Productivity, a book grounded in the real-world practices of hundreds of pioneers in the community. Starting from the fundamental differences between Agents and humans, it derives engineering challenges and their solutions layer by layer, providing a clear path to AI-driven productivity gains. (Read the early draft)
A summary of the first AgenticOS Workshop at ASPLOS 2026, exploring agents' dual role: as new users demanding fault tolerance, exploratory execution, and resource control; and as new builders enabling specialized software revival and LLM-assisted system management.
AI Coding is not a technical problem — it's a management problem. From requirements to execution to acceptance, building explicit contracts at each stage is the deterministic boundary of AI Coding. A milestone summary from the Agent Management forum.
CloudMate handles tens of thousands of failure analysis requests per week. This article dissects its evaluate-mutate-backtest loop — how to build a self-evolving Agent system that adapts to unknown failures in a constantly changing production environment.
Knowledge-based AIOps systems face a fundamental challenge — software keeps iterating and knowledge expires. CloudMate made a systematic exploration on two fronts: how to keep a knowledge base current with fast-moving code, and how to ensure software evolution doesn't break AI ops effectiveness.
Surveying recent AI operations research to answer three questions: what has AI ops already achieved, where are the current limits, and what comes next. From Microsoft's RCACopilot to NeurIPS's Stratus, a deep dive into Context Engineering and LLM Agent opportunities and challenges.
