Tag
context engineering
Context engineering is the practice of shaping what an AI system sees, remembers, and can act on: prompts, long-term memory, tool state, and access boundaries. It matters because reliable agents depend less on raw model size than on the context they are given and how it is managed.
6 articles
Loop Engineering 让 Agent 把事做完
我拆开 Loop Engineering，给你一套让 Agent 反复执行、自检、修正并做完任务的可复制模板。
Why Prompt Engineering Is Wrong About 2026
Prompt engineering is giving way to context engineering, and structured frameworks win because they reduce errors and improve repeatability.
Prompt engineering turns vague asks into usable outputs
I break down prompt engineering into practical patterns, with a copy-ready template for better LLM outputs.
How to Engineer Prompts for AI Agents
This guide shows how to design a clear prompt and system prompt for an AI agent.
Context Is the New OS: Zettlab's Agent Computer
Zettlab is betting that personal data, not raw compute, will define the next PC. Its Agent Computer aims to run bots on your own context.
Harness Engineering: From Bridle to Operating System, The Missing Link in AI Agent Reliability
Harness Engineering is the discipline of designing external control frameworks for AI Agents. By integrating context engineering, architectural constraints, and garbage collection, it transforms unreliable large models into dependable production systems.