The article explains that simply scaling large language models cannot sustain long‑running, production‑grade AI agents, and that a dedicated Agent Harness—acting as an operating system with orchestration, memory, governance, tool execution, and feedback loops—is essential for reliable, industrial‑scale automation.
This article explains why many enterprise AI agents break down in real projects, identifies four common pitfalls—including mistaking agents for chatbots, lacking schema‑level tool logic, missing memory and variable injection, and absent end‑to‑end pipelines—and offers concrete engineering solutions to build robust, task‑driven agents.
Building AI agents may seem straightforward with frameworks like LangChain, but hidden complexities in orchestration, memory management, reproducibility, and scalability turn simple demos into fragile systems, requiring systematic engineering, observability, and robust design to achieve reliable, production‑grade intelligent agents.
iQIYI’s minute‑level online deep‑learning framework overcomes stability, timeliness, compatibility, delayed feedback, catastrophic forgetting, and i.i.d. constraints through high‑availability pipelines, TensorFlow Example serialization, rapid P2P model distribution, flexible scheduling, disaster‑recovery rollbacks, PU‑loss adjustment, and knowledge‑distillation, delivering a 6.2% revenue boost.
An insightful look at how both spacecraft design and city governance exemplify complex systems, distinguishing closed versus open systems, outlining characteristics of complex and mega-complex systems, and linking these concepts to system engineering pioneers like Qian Xuesen and modern large language models.
The article explains the concept of software design patterns, their role in managing coupling and interfaces, and provides best‑practice guidelines for enterprise‑level service design, illustrating how MITRE system engineers should select and apply patterns to achieve loosely‑coupled, extensible systems.
This interview reveals how ByteDance’s STE team, a youthful group of Linux kernel engineers, identified and solved memory‑management redundancy, contributed the HVO and VDUSE projects to the open‑source community, and leveraged these advances to boost cloud‑native performance and reliability across the company.
The paper presents a holistic algorithm‑system‑hardware co‑design for advertising deep‑learning inference, combining model pruning, approximate computing, kernel fusion, scheduling and PCIe transfer optimizations with GPU and NPU upgrades, achieving up to five‑fold speed‑up and significantly higher latency‑bounded QPS for large‑scale ad services.
This article explains the fundamental differences between large and simple systems, clarifies why large-system design is more than just distributed design, and outlines a step-by-step approach covering business, system, and technology layers, including business, application, data, and technical architecture.
This article outlines the five key practices—adopting a developer mindset, mastering system engineering, gaining cloud experience, learning containers, and developing soft skills—required to become an effective DevOps engineer in today’s rapidly evolving tech landscape.
Lin Yuansheng, JD’s first post‑doctoral researcher, leverages his system‑engineering background and big‑data analytics to build models that identify black‑market accounts, improve network risk perception, and demonstrate how academic research can be rapidly applied to real‑world e‑commerce security challenges.
This talk shares the experience of a small machine‑learning team at Dangdang, describing how they built a recommendation system from scratch, the tools and processes they used, the challenges of limited personnel, and the many pitfalls they encountered while iterating toward a production‑ready solution.
