The article explains that AI-powered code writing relies on an Agent Loop—Plan, Act, Observe, Reflect—rather than a single question‑answer exchange, detailing each phase, termination rules, common pitfalls, and practical guidelines for building reliable iterative agents.
This article defines Agent Skill as a modular, file‑system‑driven knowledge asset, explains its three‑layer progressive‑disclosure architecture, outlines core features such as decision‑tree logic and dual verification, details suitable scenarios, and provides a step‑by‑step iterative workflow with concrete code snippets and tooling.
The article explains why powerful AI models still fail complex web‑testing tasks without proper business SOPs, and presents four concrete engineering lessons—limited context, explicit step definitions, checklist‑driven validation, and iterative self‑revision—to train an AI into a dependable, self‑checking S‑grade testing employee.
The article explains Agent Engineering—a systematic approach that turns nondeterministic large‑language‑model agents into reliable production‑grade applications through an iterative build‑test‑deploy‑observe‑improve loop, combining product, engineering, and data‑science thinking to address unpredictability and achieve continuous growth.
The article explains how product managers, especially in B2B finance, should adopt a user‑thinking mindset that aligns user needs with product goals, using concrete methods for requirement discovery, scenario‑driven design, proactive compliance guidance, and iterative validation to deliver efficient, accurate, and controllable solutions.
The article argues that hoarding countless prompt examples is futile and instead teaches a three‑step, practice‑driven method—drafting a raw prompt, using AI to refine it, and iteratively polishing through real‑world testing—while emphasizing top‑down planning and the importance of personal experience.
The article narrates a software team's shift from a traditional waterfall model to an iterative, value‑driven agile approach, detailing the coach’s demand‑analysis meetings, three‑week sprints, lightweight documentation, continuous feedback, and the eventual successful delivery while highlighting common agile practices.
The essence of agile lies in embracing trial‑and‑error and taking small, rapid steps, which together keep projects out of high‑risk gambles and steer them toward steady, predictable progress by continuously learning and adapting to change.
The article debunks the myth that agile eliminates planning by showing that even in complex, time‑boxed Scrum projects a flexible, continuously‑refined plan covering scope, cost, schedule, and quality is essential for managing risk and delivering business value.
The story follows Pilot Fish, a diligent developer tasked with building a customer‑service system despite receiving only vague, “just do something” feedback from department heads, leading to a demo that merely opens and closes a CD‑ROM, highlighting the pitfalls of unclear requirements and misguided iterative development.
The article compares the classic waterfall software development model with iterative development, outlines the drawbacks of the former, highlights the six advantages of iteration, and details a multi‑iteration parallel approach with QA and release‑cycle practices for improved project control and product quality.
The article explains why rapid response is essential in modern software projects, critiques the drawbacks of waterfall development, and introduces agile practices such as short iterations, user stories, continuous integration, automated testing, and close collaboration to deliver value more efficiently.
This article explains the core concepts of Agile development, compares it with traditional waterfall methods, outlines its benefits such as faster response and higher quality, and introduces practical frameworks like Scrum and XP along with key practices like TDD and continuous integration.
