Designing an Always-On Agent
Chatbots wait for a message. An always-on agent is single-threaded, proactive and situationally aware. Here's how we design them, and what models still lack.
Topic
#context-engineering
35 篇笔记
Software Factory Engineer: Job Description
An emerging engineering role for the transition from AI-assisted development to L4 Autonomous Software Factories.The RoleWe are looking for ...
Exploring Super-agent Design: Towards a More Autonomous Agent System
The original article is in Chinese. This is a machine translation.This article discusses architectural ideas. It is not an introduction to e...
New context engineering challenges in late 2026
It feels like everyone is converging on the same agent architecture: an LLM, running in a loop, calling tools, running in a sandbox. Claude ...
「万字总结」- 我偷来了一份成为 Agent 工程师的学习线路
我之前面试了一家做 AI + web3 业内比较厉害的公司，但是没有通过吧，面试之后面试官给我了一份《Agent工程师入门指南》。我把它整理了一下，分享出来给大家提供参考，这是一份很详细很详细的、关于如何成为 Agent 工程师的文档，并且补充上我自己的学习心得当时给我的答复是 ...
入职大厂两个月，我的 AI 开发心得
背景起因是和组里老板进行了几次 one-on-one。他对我目前使用 AI 工具、搭建个人 Harness，以及 AI Coding 的工作流很感兴趣。主要也是因为，我现在的需求做得又快又好，已经是一些项目的主 O 了。因此，我也借这个机会，整理了一下自己的日常 AI 工作流。目...
How to Cut Your Agent Bill by 90% With Jev Engineering and Kimi K3
How to find the decisions hiding inside your agents, write questions a system one model answers well, route on calibrated confidence and han...
The 5 levels of AI agents. From a single prompt to a production agent (Complete course)
There are five words people throw around about agents right now. Context engineering, loop engineering, Jev engineering, harness engineering...
Memory Is a Compiler
tl;dr: Memory preserves the past in a form meant to be useful in the future, then recompiles what that past means as new evidence and new qu...
Running a Software Factory Efficiently at Uber Scale
Post author: @udaykiranIntroductionAI tools are now embedded in every phase of software development at Uber. More than 70% of pull requests ...
How to Build a Company Brain That Gets Smarter Every Week
I am going to show you how to turn every correction, decision, and useful workflow your team gives AI into one company brain that makes ever...
How to Cut Agent Tokens by 2.7x (using an open harness)
Everything you need to understand where your agent's tokens actually go and what to do about it. It covers what a production harness owns be...
Agent 终章（Harness 成本篇）：一次百炼账单降低 88% 实战
关于优化 token，本文或许能够提供一些经验和参考
From context to dreams: architecting memory for AI agents
Have you ever felt that every conversation you have with an LLM across sessions feels like starting over from scratch? LLMs have a problem: ...
Designing Agent Context by Scope and Time
When an agent performs poorly, we often blame the model.Then we enlarge the system prompt. When that becomes unwieldy, we add another agent....
Optimizing for cost and intelligence
Balance cost and intelligence on the Claude Platform, with measured results for prompt caching, effort, model choice, budgets, and multi-model strategies.
The AI Engineering Skills Map
I am delighted to present The AI Engineering Skills Map. AI allows us to build software very differently today than in 2022, and everyone wi...
How to Build a Shared AI Harness for Your Team
I am going to show you how to turn the models, agents, skills, and automations living across your team's tools and workflows into one coordi...
Deep Agents vs LangChain vs LangGraph
Deep Agents, LangChain, and LangGraph each offer distinct approaches to building agents. In this post, we cover the key distinctions between...
Why Software Factories Fail: Turning the lights back on
This is part two of Why Software Factories FailThe talk version of this post is live on youtube: https://www.youtube.com/watch?v=Ib5GBkD555M...
Practical multi-agent orchestration in Codex
GPT-5.6 Sol gets especially interesting when it has a team to work with. Codex's new Multi-Agent V2 tools give Sol and Terra a natural way t...
The new rules of context engineering for Claude 5 models
I’ve written previously about how to best prompt the newest generation of Claude 5 models and work with them iteratively to discover what yo...
The State of Agent Wikis
In April 2026, Andrej Karpathy published a GitHub Gist describing a pattern he called the LLM Wiki.In the months since, four different teams...
数据研发Multi-Agent架构的Harness工程实践
📝 摘要 本文基于阿里云数据研发团队的 Multi-Agent 实践，系统阐述了 Harness Engineering（智能体脚手架工程）的核心理念与落地方法。文章指出 AI 工程经历了 Prompt Engineering → Context Engineering → Harness Engineering 三代演进，并提出核心公式 Agent = Model + Harness：模型决定…
A Taxonomy of Self-evolving Agents
Self-evolving agents are becoming popular. [Hermes Agent](https://hermes-agent.nousresearch.com/docs/user-guide/features/skills) enables aut...
30 Core Agentic Engineering Concepts Every Developer Should Know
There are 8 billion people on the planet.Only a fraction of developers understand how AI agents actually work.Not the demos. Not the hype.Th...
简直就是教科书级别的 AI 设计规范。
Vercel 的设计系统规范，真的是我见过的教科书级样例。今天认真学习他们的 DESIGN.md，一亮一暗两套主题，彻底把颜色、字体、间距、组件、动效、文案规则，全部描述成了一套可以执行的设计方法。感觉以后我所有产品的 design.md 都可以照这个路子来写了。具体在这里：ve...
Building Reliable Agentic AI Systems
AI helping pharmaceutical researchers query decades of information buried in PDF reports
Dreaming: Better memory for a more helpful ChatGPT
ChatGPT introduces a new memory system to better remember preferences, keeping context fresh and relevant across conversations.
A Practical Guide to Becoming an AI-Native Engineer
If AI writing everything is the answer, then why are most engineering teams shipping more bugs, more incidents, and more technical debt than...
Memory Is Purpose
LLMs compressed the internet into weights. Agents need to compress work into state.LLMs are among the most successful artificial memory syst...
Setting Up and Using the Pi Coding Agent
My experience of setting up the Pi coding agent with DeepSeek, installing packages, and using it for side projects and experiments.
State of Memory in Agent Harness
Agent harnesses are where AI software actually runs. Cursor, Devin, Claude Code, Codex: these environments handle context, orchestrate tools...
How Warp builds self-improving agents on Claude
How Warp devised a simple development pattern that anyone can use to create self-improving agents.
The AI-Native SDLC playbook | Claude by Anthropic
How to transform your software development lifecycle with AI—stage by stage.