#AI agent engineering
3170 related articles
·5 min
From Prompts to Harness Engineering: The Evolution of AI Agent Development Paradigms
Why has AI engineering methodology evolved from prompts to context engineering and now Harness engineering? This article examines three paradigms, key bottlenecks, and the Agent = Model + Harness formula.
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·6 min
Rust AI Agent System Prompt Engineering in Practice: A Seven-Dimensional Design Guide
An in-depth analysis of the four core roles of AI Agent system prompts and the four-step tool-calling method, covering behavioral boundaries, autonomous execution principles, and trigger pattern design—helping Rust developers build high-quality agents.
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·4 min
Harness Engineering: The Third Phase of AI Coding and a Practical Guide to agents.md
A deep dive into Harness Engineering — the third phase of AI coding. Based on research across 2,853 GitHub repos, explore agents.md, Skills, MCP, and eight configuration mechanisms to control your AI coding assistant.
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·7 min
Harness Engineering: The Six-Layer Engineering Framework for Enterprise AI Agent Deployment
From Prompt Engineering to Harness Engineering, a deep dive into the core challenge of truly deploying AI Agents in enterprises. This article breaks down the six-layer architecture and shares real-world Hermes Agent practice.
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·3 min
AI Agent Architecture Decoded: An Engineering Guide from Conversations to Automation
A deep dive into the four-layer engineering design of AI Agents: planning, memory, tool use, API cost optimization, MCP protocol integration, and Skill encapsulation.
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·3 min
Agentic AI in Practice: Core Principles, Engineering Challenges, and Practical Recommendations
A deep dive into Agentic AI: core components (planning, tool calling, memory), engineering challenges (reliability, cost, safety), and practical development recommendations for production deployment.
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·5 min
Loop Engineering: A Comprehensive Guide to AI Agent Loop Design
A deep dive into Loop Engineering for AI Agents — what loop feedback mechanisms are, how they differ from Harness Engineering, and a complete guide from principles to production implementation.
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·6 min
The New Normal of AI Programming: How Autonomous Agent Development Is Reshaping Software Engineering
AI Agent autonomous programming is evolving from niche experiments to the industry default. This article analyzes the three stages of AI-assisted programming, its impact on developer skills, process restructuring, and key challenges.
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·2 min
Harness Engineering Architecture Explained: An Essential Skill for AI Agent Developer Roles
Harness Engineering is becoming a must-have skill for AI agent developer roles. Learn the architecture, how top agent products use it, and how to practice with LangChain DeepAgents.
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·3 min
Loop Engineering Explained: An Engineering Approach to Making AI Coding Agents Run Autonomously in Loops
A deep dive into Loop Engineering and the Rhythmic framework: how closed-loop systems replace repetitive prompting to enable autonomous AI coding agents with state management and budget control.
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·2 min
Getting Started with Loop Engineering: Making AI Agents Run Workflows Autonomously
A deep dive into Loop Engineering: core concepts and hands-on setup including Codebase Harness, shared file systems, triggers, and Loop Contracts to make AI agents run autonomously.
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·4 min
Loop Engineering Explained: Agent Loop Mechanisms and the New Paradigm of AI Agent Development
Deep dive into Loop Engineering: from Agent Loop principles and While loops to Graph structures, covering loop efficiency optimization and termination strategies for AI agent development.
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·3 min
Agent Harness: A New AI Agent Paradigm from Prompt Engineering to Execution Environment Orchestration
Deep dive into Agent Harness Engineering: how loop execution and context isolation overcome the bottlenecks of prompt and context engineering in modern AI coding agents like Cursor.
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·4 min
Harness Engineering in Practice: Taming AI Agents with Hermes Agent
Deep dive into Harness Engineering: using the open-source Hermes Agent framework's four-layer memory system and Skill evolution to build controllable, evolvable AI agents.
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·2 min
Struggling to Deploy AI Agents? Engineering Is the Key to Going from Demo to Product
57% of projects have deployed AI Agents, but 40% will be killed. This article analyzes the engineering methodology for taking AI Agents from Demo to enterprise product, covering the full process from requirements to deployment.
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·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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