#Agent engineer
3850 related articles
·4 min
A Complete Breakdown of the Engineering Challenges Behind RAG and Agent Interview Questions
Resume full of RAG and Agent but keep failing interviews? The issue is you only run demos and can't explain production engineering challenges. This article breaks down data cleaning, hybrid retrieval, hallucination protection, and agent loop breakers.
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·6 min
A Deep Dive into AI Agent Harnesses: The Core Engineering Behind Agent Frameworks
What is an AI Agent's harness? This article systematically dissects the core components of agent frameworks: context management, tool use, control loops, and caching strategies—revealing why the same model performs so differently across harnesses.
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·4 min
Why Do AI Agents Crash the Moment They Go Live? A Practical Guide to Harness Engineering for Runtime
A deep dive into the Claude Code source code, systematically analyzing the five-layer Harness Engineering architecture: environment, tool, control, memory, and evaluation. Build a stable runtime system for production AI Agents.
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·2 min
AI Agents for SGLang Kernel Optimization: Real-World Performance Gains and Engineering Methodology
SGLang's team converted expert knowledge into agent skills, achieving 71.4% throughput gains, TTFT reduced from 456ms to 168ms. A deep dive into agent-assisted kernel optimization methodology.
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·2 min
Agent Skills: Bringing Production-Grade Engineering Capabilities to AI Coding Agents
Google engineer Addy Osmani's open-source project agent-skills (76k+ GitHub stars) injects production-grade engineering skills into AI coding agents, covering performance, security, and code quality.
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·4 min
The OpenAI Codex Product Philosophy: How AI Engineers Are Shifting from Writing Code to Managing Agents
OpenAI Codex isn't about replacing engineers—it's about empowering them. A deep dive into the AI Engineer conference talk: from code completion to long-horizon agents, Value Maxing, and managing agent teams.
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·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
The Agent Engineer Transition Roadmap: A Deep Dive into Three Core Skill Systems
Want to become an Agent engineer? This article systematically covers three core skill tracks—LLM fundamentals, LangChain architecture development, and enterprise deployment—to help you avoid detours.
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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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·4 min
6-Week Agent Engineering Roadmap: From Beginner to Enterprise Deployment
A structured 6-week roadmap for enterprise Agent deployment covering LangChain, LangGraph, MCP, and RAG — from planning and memory to multi-agent collaboration and production deployment.
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·3 min
Building LangGraph from Scratch in TypeScript: A Complete Agent Development Guide for Frontend Engineers
How can frontend developers get into AI Agent development with TypeScript? This guide covers a four-stage path from API calls to building LangGraph from scratch, including Zod, state management, and node-edge design.
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·6 min
From Frontend Engineer to AI Agent Developer: A Complete High-Paying Skill Tree Breakdown
Pure frontend roles are shrinking; AI Agent development is the high-salary divide. This guide breaks down the full skill tree for frontend engineers pivoting to AI: TypeScript, frameworks, AI productivity, and Agent core concepts (MCP, Tool Calling, Skill).
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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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Three Role Shifts for Engineers in the…
·4 min
Three Role Shifts for Engineers in the AI Agent Era: From Coders to System Designers
As AI Agents handle long-horizon autonomous tasks, engineers are shifting from writing code to setting direction, reviewing output, and designing systems around models.
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·3 min
Harness Engineering in Practice: A Guide to Enterprise-Grade Multi-Agent System Architecture
A deep dive into Harness Engineering architecture: building an AI procurement assistant on ERP systems, covering multi-agent orchestration, MCP protocol, ASGI deployment, and sandbox isolation.
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·4 min
Spotify's AI in Practice: How 3,000 Engineers Are Reinventing R&D with Agents
Spotify's real engineering story: 99% of engineers use AI coding tools, PR frequency up 76%, 2.5M automated maintenance PRs merged. How Honk, FleetShift, and Backstage work together to drive AI Agent adoption at scale.
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