#Agent engineer
2908 related articles
·5 min
Complete Breakdown of Core Competencies for AI Agent Engineers: From Demo to Production-Grade Systems
Deep breakdown of 4 core AI Agent engineer competencies: business decomposition, multi-Agent architecture, quantitative evaluation, and engineering delivery—bridging the gap from Demo to production.
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·3 min
Agent Intent Recognition: Engineering Practices Behind the Three-Layer Funnel Architecture
An in-depth look at the three-layer funnel architecture for agent intent recognition: rules for fast interception, context for routine intents, and LLM as fallback. Exploring the engineering trade-offs of accuracy, latency, and cost.
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·1 min
Becoming an AI Agent Engineer: A Detailed 4-Week Hands-On Learning Roadmap
Want to become an AI Agent engineer? This article breaks down a 4-week roadmap: from core agent architecture and ReAct, to multi-agent collaboration and real projects.
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·3 min
Pinterest Medic: Engineering an AI Agent for Automated Spark Failure Diagnosis
How Pinterest engineers built Medic for Apache Spark — a multi-agent auto-diagnosis tool — covering the evolution from a single ReAct agent, observability, log denoising, and end-to-end testing.
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·3 min
Enterprise Long-Running Agent Engineering: A Complete Guide from Demo to Production
A deep dive into enterprise Agent engineering: long-running execution, HITL safety approvals, and event sourcing — with two real-world commercial projects for content ops and SRE.
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·7 min
Context Engineering: A Systematic Approach to Stop AI Agents from 'Talking Nonsense'
Context engineering is the core methodology for building efficient AI Agents, covering query enhancement, RAG retrieval, prompt design, memory management, and tool invocation. Master Write, Select, Compress, and Isolate to solve LLM hallucination at its root.
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·7 min
The Complete Guide to LLM Application Engineer Interviews: Six High-Frequency Topics in the Agent Domain
A focused guide to the core interview topics for LLM application engineers, covering agent architecture, Multi-Agent, Langfuse evaluation & tracing, security, and RAG optimization.
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·7 min
The Complete Guide to LLM Application Engineer Interviews: Six High-Frequency Agent Topics
A focused guide to core LLM application engineer interview topics, covering agent architecture, Multi-Agent, Langfuse evaluation, security, and RAG optimization.
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·7 min
From Chat to Agent Platform: A Practical Guide to Engineering AI Applications
A deep dive into engineering AI applications: from a simple chat page to a multi-layer Agent platform, covering RAG knowledge bases, Workflow scheduling, multi-model management, and run tracing.
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·3 min
AI Development for Java Engineers: A Complete Guide to LangChain4j Agents
Java developers can build AI apps too! Learn LangChain4j fundamentals including RAG, Agents, Function Calling, and hands-on projects — no Python required.
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Multi-Agent Collaboration: A GPT Team …
·2 min
Multi-Agent Collaboration: A GPT Team Engineering Paradigm and Practical Guide
Explore multi-agent collaboration architecture: role division, communication protocols, coordination mechanisms, and how Workbench templates help developers build efficient AI agent teams.
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·3 min
Agent Infinite Loop Engineering Guide: Detection, Breaking, and Root Cause Analysis
How to handle Agent infinite loops? This guide covers three-layer loop detection, four strategy-switching techniques, root cause analysis, and multi-layer fallbacks for building stable, production-grade Agent systems.
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·2 min
Loop Engineering Explained: The New Development Paradigm of Closed-Loop AI Agent Iteration
Loop Engineering is an emerging AI dev paradigm where Agents iterate in controlled loops instead of one-shot outputs. Learn the 4-year evolution and what it means for developers.
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Claude Is Mr. Meeseeks: The Disposable…
·2 min
Claude Is Mr. Meeseeks: The Disposable Nature of AI Agents and Its Engineering Implications
Using Rick and Morty's Mr. Meeseeks to explain Claude and AI agents: stateless execution, task atomicity, and multi-agent recursive failure risks. A deep dive for developers building better AI workflows.
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·4 min
Stop Shipping Skills Blindly! A Google DeepMind Engineer's Hands-On Guide to Agent Evaluation
Google DeepMind engineer Philip reveals: almost everyone uses coding agent Skills, yet almost no one writes evals for them. A deep dive into Skill evaluation methods, 8 actionable tips, and a real Gemini API case study.
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·3 min
LangSmith Triple Release: Harness, Sandboxes, and Eval Complete the Agent Engineering Toolchain
LangChain launches Harness, Sandboxes, and Eval integrated into LangSmith, creating the first complete Agent engineering toolchain from development to acceptance testing.
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·4 min
Loop Engineering in Practice: Designing and Deploying a Multi-Agent Collaborative Development System
A deep dive into Loop Engineering: how multi-agent collaborative dev systems achieve automated coding loops through workflow scheduling, step isolation, and validation.
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GitHub Daily · July 18: 3D Reconstruct…
·3 min
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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·4 min
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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·3 min
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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