#AI Agent engineering
3860 related articles
·2 min
Replit's AI Loops Workflow Explained: Multi-Agent Collaboration Replaces Prompt Engineering
Deep dive into Replit's AI Loops workflow: how orchestrators, parallel agents, and Computer Use Verifiers build automated closed-loop systems through multi-agent collaboration.
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·4 min
The Complete AI Agent Engineering Tech Stack: A Practical Guide to 100x Development Efficiency
Deep dive into the AI agent engineering stack: from Cursor framework, model selection to context engineering and automated review loops — a complete workflow guide to achieving 100x development efficiency.
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·4 min
Harness Engineering: A New Software Development Paradigm Where Humans Steer and AI Agents Execute
OpenAI engineer Ryan Lopopolo introduces Harness Engineering — a methodology where humans build constraint systems and AI agents handle all code implementation.
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·4 min
Core Principles of Coding Agents: How Perception and Context Engineering Determine AI Coding Effectiveness
Deep dive into why coding Agents differ: perception lets Agents understand projects first, context engineering precisely filters information within limited token budgets.
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Tutorials·2 min
AI Agent + Vue 3 Full-Stack Development in Practice: Integrating the Coze Platform with Frontend Engineering
A deep dive into integrating AI agents with Vue 3 frontend development, covering Coze platform agent building, Vue3+TypeScript+Pinia stack practices, and AI content generation features.
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Tutorials·3 min
Building AI Agents with Node.js: Replacing Prompt Engineering with Function Calls
Learn how to refactor a Node.js AI Agent using Function Calls instead of prompt engineering. Covers tool definition via JSON Schema, the Agent Loop, and key implementation details from analyzing Claude Code.
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Tutorials·3 min
Harness Engineering: A Practical Guide to AI Development — From Prompts to Mastering AI Agents
A deep dive into Harness Engineering's three-layer architecture: Information, Constraint, and Automation layers, covering Agent failure modes, OpenAI and Anthropic best practices, and AI tool selection strategies for controlled AI development.
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Tutorials·2 min
npcpy: An Open-Source Framework That Rethinks AI Agent Development with Software Engineering Principles
Deep dive into npcpy's four-layer architecture, multi-agent collaboration, knowledge graph lifecycle management, and deployment strategies for building stable, controllable AI Agent systems.
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Deep Dives·1 min
Harness Engineering Explained: The Underlying Principles and Practical Methods for Controlling AI Agents
Deep dive into Harness Engineering: controlling AI Agents through rules, tool configuration, and workflow design, plus feedback mechanisms and Lifelong AI Agent practices.
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Tutorials·2 min
OpenAI Codex Hands-On Tutorial: A Complete Guide to Prompt Engineering, Agent Skills & Cloud Automation
Deep dive into OpenAI Codex's three core capabilities: prompt engineering for better code generation, agent skills for autonomous programming, and cloud automation for end-to-end CI/CD pipelines.
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Tutorials·1 min
Andrew Ng's New Course Explained: Agent Memory Engineering from Basics to Practice
Deep dive into Andrew Ng's Agent Memory course with Oracle: covering memory engineering concepts, memory-first architecture design, and building AI agents with persistent memory.
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Deep Dives·1 min
Harness Engineering Explained: Core Methodology and Implementation Guide for Industrializing AI Agents
Deep dive into Harness Engineering methodology: Agent=Model+Harness formula, the Prompt→Context→Harness evolution path, and a developer implementation guide.
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Tutorials·2 min
Harness Engineering Explained: A Guide to Building a Four-Step Closed-Loop AI Agent Work System
A deep dive into the Harness Engineering four-step closed-loop principle (Goal, Action, Verification, Memory), clarifying its relationship with Prompt Engineering, Context Engineering, and MCP.
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Tutorials·2 min
Engineering AI Agents for Production: Four Key Challenges and a 12-Week Implementation Roadmap
90% of AI Agent projects stall at the demo stage due to insufficient engineering. This article breaks down four core challenges and provides a 12-week actionable roadmap to production.
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Tutorials·4 min
Harness Engineering in Practice: A Three-Layer Architecture for Taming AI Agent Development
A detailed guide to Harness Engineering's three-layer architecture for controlling AI Agent code generation quality, covering the Information, Constraint, and Automation layers with practical setup and pitfall avoidance tips.
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Deep Dives·3 min
Harness Engineering: The Core Engineering Methodology for Taking AI Agents from Demo to Production
Deep dive into Harness Engineering: its definition, six core components, and production practices. Learn why Prompt and Context Engineering aren't enough for production-grade AI Agent systems.
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Tutorials·3 min
AI Agent Prompt Engineering: A Practical Guide to the Three-Layer Architecture
A practical guide to AI Agent prompt engineering using a three-layer architecture: System Layer, Input Layer, and Action Layer — with n8n examples.
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Research·4 min
Skills-Oriented Programming: Domain Knowledge Engineering for Efficient Code Generation by Code Agents
Alibaba Mama's Skills-Oriented Programming methodology uses three-layer Skill structures, progressive disclosure, and four-layer anti-corruption systems to achieve 90%+ code generation accuracy for Code Agents in complex enterprise codebases.
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Deep Dives·4 min
Harness Engineering Explained: The Third-Generation Development Paradigm for Taming AI Agents
Deep dive into Harness Engineering: the third-gen AI development paradigm. Learn its three-layer architecture for effectively harnessing AI Agents to complete complex development tasks.
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Industry Insights·2 min
Uber's AI Agent Platform Now Contributes 11% of PRs — The Engineering Role Is Transforming
Uber reveals its internal Agentic Developer Platform now generates 11% of all PRs. Learn how Uber moved from AI-assisted to AI-autonomous coding and how engineering roles are transforming.
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