#Agent engineer
3241 related articles
GitHub Daily · July 18: 3D Reconstruct…
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GitHub Daily · July 18: 3D Reconstruction Foundation Model Tops the Charts, AI Engineering and CLI Agents Explode Collectively
July 18 GitHub Daily: 3D reconstruction foundation model lingbot-map tops the charts, with AI engineering tooling, CLI Agents, and the MCP ecosystem exploding across the board.
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AI Agents Automating JS Reverse Engineering: A Deep Dive into the New Paradigm for Web Scraping
AI agents are revolutionizing JS reverse engineering. This deep dive covers built-in tool chains, automation modes, prompt engineering for e-commerce, and full pipeline automation from parameter extraction to database storage.
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The Complete Guide to Looping Engineering: Building Efficient Agent Loops from Scratch
A deep dive into Looping Engineering — covering the five core loop elements (Trigger, Goal, Judgment, Feedback, Memory), when to use loops, and a step-by-step guide to building a topic-selection loop with Claude Code.
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Four Paths for Frontend Engineers to Level Up in AI: From RAG to Vertical Agents
How can frontend engineers transition into AI development? This guide covers four agent development directions: RAG, workflow agents, vertical agents, and general-purpose agents — with framework picks like LangChain.js.
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Agent Performance Evaluation: A Three-Layer Framework and Engineering Implementation Guide
A deep dive into the three-layer AI Agent evaluation framework — outcome, process, and system layers — covering trajectory evaluation, tool call accuracy, automated testing, and key engineering challenges.
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Unlocking Claude Code Cloud VMs: Context Engineering and Parallel Agent Orchestration
Deep dive into Claude Code cloud sessions (Claude Cowork): build context repositories, assign minimum-privilege credentials, and orchestrate parallel AI agents as an engineering team.
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Production-Grade LangGraph Template: A…
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Production-Grade LangGraph Template: A Practical Guide to Engineering AI Agents for Production
A deep dive into production-grade LangGraph templates covering state management, observability, error handling, and containerized deployment to bridge the gap from demo to production.
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Building a Slack-Native Coding Agent: Architecture Design and Engineering Methodology
A deep dive into embedding a coding agent in Slack: core architecture, permission design, async task handling, and RAG context management for AI-powered dev workflows.
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SJTU Professors Open-Source Agent Tutorial: A Four-Stage Path from Zero to AI Engineer
SJTU professors open-source a 4-stage Agent tutorial on GitHub, covering LLM basics, ReAct, multi-agent systems, and real-world projects — a practical path to AI engineering.
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A Deep Dive into OpenAI Codex: How AI Engineers Are Reimagining Software Development with Agents
OpenAI Codex is redefining how AI engineers work: from code completion to autonomous Agents, from single-threaded to parallel Value Maxing. A deep dive into the Codex App architecture, open ecosystem, and Manager of Agents practice.
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Assess Your Engineering Team's AI Agen…
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Assess Your Engineering Team's AI Agent Maturity in 5 Minutes: A Tiered Framework and Key Dimensions
How to quickly gauge your engineering team's AI Agent adoption level? This article breaks down a four-tier AI maturity framework covering tool adoption, workflow integration, governance, and measurement.
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Deep Dives
Deep Dive into AI Agent Skill Design: …
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Deep Dive into AI Agent Skill Design: Engineering Practices from Anthropic and Perplexity
A deep dive into Skill design philosophy from Anthropic's Claude Code team and Perplexity's Agent team, covering the Tax Test, Gotchas Flywheel, progressive disclosure, and Eval-First practices for building high-quality AI Agent skill systems.
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Migrating a Production AI Agent to GPT…
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Migrating a Production AI Agent to GPT-5.6: 2.2x Faster, 27% Cheaper — Engineering Lessons Learned
A production AI Agent migrated to GPT-5.6 achieved 2.2x faster speed and 27% lower cost. Deep dive into prompt compatibility, eval frameworks, and migration best practices.
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·1 min
Agentic Coding in Practice: Test Feedback Loops, LLM Benchmarks, and an Engineering Adoption Guide
An in-depth look at agentic coding: how test-driven loops enable AI self-correction, the real limits of LLM benchmarks, and key engineering lessons on context management and human-AI collaboration.
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LangChain V1.3 Hands-On Tutorial: A Complete Breakdown of RAG, Multi-Agents, and Engineering Mindset
An in-depth look at LangChain V1.3's core philosophy: from RAG to multi-agent workflows. Master LangGraph, Chain, and DeepAgent, learn token control and Human-in-the-loop, and become a true master of AI app development.
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Reverse-Engineering Web Apps: A New Ap…
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Reverse-Engineering Web Apps: A New Approach to Breaking Through the API Barrier for AI Agents
Explore a new approach to AI Agent tool integration: reverse-engineering web apps to turn API-less pages into callable Agent tools, with analysis of MCP synergy and challenges.
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4-Step Roadmap to Becoming an AI Agent Engineer: From Zero to a $50K LLM Role
A proven 4-step roadmap to becoming an AI Agent engineer: stable LLM calls, tool use (RAG + Function Calling), production engineering, and resume optimization.
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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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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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