I am James, focusing on AI Agent learning and growth. I continuously update two series: “AI Agent Mastery Path,” which systematically outlines core theories and practices of agents, and “Claude Code Design Philosophy,” which deeply analyzes the design thinking behind top AI tools. Helping you build a solid foundation in the AI era.
The article explains how Hermes implements the Agent Communication Protocol (ACP) to become a standard, first‑class Agent server for IDEs like VS Code, Zed, and JetBrains, detailing the protocol’s design, session management, event bridging, and IDE integration benefits.
The article examines Hermes' design that moves all agent decision rules into editable text files, explains the three‑layer stable‑context‑volatile architecture, compares it with other frameworks, and shows how this approach improves transparency, controllability, and cache efficiency for AI agents.
The article maps the evolution from Vibe Coding to Spec‑Driven Development, defines Harness Engineering as an AI‑augmented software methodology, diagnoses the Context Rot problem caused by limited windows, attention dilution, and cumulative noise, and presents three core principles—decision externalization, staged workflows, and atomic tasks—to mitigate it.
This article presents a comprehensive Harness Engineering roadmap, detailing a knowledge graph, layered learning hierarchy, four framework families, a five‑stage progression from zero to implementation, and milestone self‑assessment checklists, helping engineers understand and apply AI‑driven coding practices effectively.
The article explains the design of Hermes' terminal UI gateway, detailing why the TUI does not import the Agent directly, how JSON‑RPC over stdio or WebSocket enables local and remote modes, and how slash commands are processed through a two‑layer routing system with a dedicated _SlashWorker subprocess and a pure‑function crash‑recovery plan.
The article dissects Claude Code’s three‑layer compression system—microCompact, autoCompact, and sessionMemoryCompact—explaining how each layer mitigates the multiplicative token growth of multi‑agent workflows, the compact_boundary bookmark for resume support, cache‑friendly designs, and practical pitfalls.
This article dissects Claude Code's multi‑agent architecture, explaining why single‑agent designs hit context, serial, and failure walls, comparing leading frameworks, and detailing Claude's AgentTool recursion safeguards, Coordinator control‑data separation, UDS‑based swarms, IterationBudget controls, and the three engineering guardrails that keep multi‑agent systems reliable.
This guide explains how to use the Skill Creator tool to generate automated trigger tests, compare skill‑enabled versus baseline performance, continuously evaluate results, apply checklists, debug with a six‑step process, avoid six common anti‑patterns, and manage skill versioning and reuse so that AI skills become progressively smarter.
The article outlines a practical engineering framework for AI Skills, detailing when to modularize based on line count and workflow separation, how to improve AI readability with tables and scripts, when to choose MCP servers versus simple HTTP calls, and five non‑negotiable security rules to keep Skills reliable and maintainable.
The article explains how turning ad‑hoc prompts into reusable, structured Skills solves five major pain points, introduces a three‑level progressive loading design, and provides six concrete writing techniques that make a 500‑line Skill more effective and token‑efficient than a 1000‑line version.
