The article explains how quality‑attribute scenarios turn vague architectural quality goals into concrete, measurable descriptions by breaking them into six elements—stimulus source, stimulus, environment, artifact, response, and response measure—using a clear ATM withdrawal example.
The article outlines practical principles and concrete measures—starting with a simple coordinator‑sub‑agent pattern, evolving only when bottlenecks appear, and controlling dimensions such as agent splitting, count, roles, communication, and orchestration—to prevent complexity overload in multi‑agent AI systems, and adds runtime safeguards and a step‑by‑step deployment roadmap.
The article analyzes Anthropic’s Harness design for long‑running applications, detailing how it mitigates context anxiety and self‑evaluation bias through sprint contracts, rubric scoring, and a planner‑generator‑evaluator architecture, and evaluates its effectiveness across multiple versions.
This article examines the evolving requirements of future networks, proposing a precise‑connection architecture, flexible plug‑in forwarding, compute‑network integration, and inherent security mechanisms to support diverse quality‑of‑service demands across layers, while outlining control‑plane and data‑plane designs for scalable deployment.
