ContextEngineering

Starting from how AI coding agents execute work, this article explains why agent-native documentation has evolved from reference material into infrastructure, defines the responsibilities and organization of AGENTS.md, PRD, Architecture, Spec, and Plan documents, and shows how Context Engineering and Spec-Driven Development can produce a context-efficient system that reliably directs agent behavior.

This article systematically reviews frontline experience from OpenAI, Anthropic, HumanLayer, and other teams documenting AI coding-agent projects. It explains why entry-point files, layered knowledge bases, state-tracking files, and local documentation directly affect Agent performance, and provides an actionable path from a minimum viable documentation system to continuous maintenance.

Begin with a Comparison You ask ChatGPT, “What is a KV cache?” The model answers, and the conversation ends. You tell Codex CLI, “Add a user-authentication module to this project, including tests.” The agent begins working autonomously: read the project structure → understand the existing code → plan the implementation → write authentication logic → write tests → run tests → observe a failure → fix it → get the tests passing → open a pull request. The process may span dozens of steps without requiring your intervention. The underlying LLM may be the same, but the behavior is entirely different. The first is the traditional use of an LLM. The second is an agentic use. This article explains the nature of that transition. It is more than a stronger model. It is a fundamental shift in the way the model is used, and that shift reshapes the entire engineering system around it. ...

LangChain’s breakdown of the agent harness connects context engineering, memory, MCP, and the agent loop into one coherent map.

A long-form Prompt Engineering guide for AI application engineers, covering foundational principles, context design, task chains, injection defenses, agent prompt design, and evaluation-driven development.

A curated list of ten high-value articles on Contextual Retrieval, Context Engineering, and RAG evaluation, spanning Anthropic’s original publications, open-source implementation guides, and a review of 2025 trends.

Prompt engineering? It may not be as simple as you think.

Understand LLM prompt injection and several fundamental defensive measures.