#observability
556 related articles
·3 min
Four-Layer Funnel Architecture: A Design Guide for Production-Grade Agent Intent Routing Systems
Deep dive into a four-layer funnel intent routing architecture (regex, vector routing, LLM FC, safety net) solving intent confusion, multi-intent gaps, emotional hijacking, and routing avalanches.
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·3 min
Firebase AI Logic Deep Dive: Hybrid Inference, Prompt Templates & Multi-Layer Security Deployment
Deep dive into Firebase AI Logic: server-side prompt templates to prevent leakage, Cloud Function triggers, four-layer security defense, AI monitoring with context caching for cost control, and cross-platform hybrid inference.
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·2 min
Agent Middleware: Adding Interceptors to Model Calls
Learn how AI Agent middleware works through two practical examples — logging and security checks. Master the Observer and Guardian design patterns to build extensible, production-grade Agents.
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·2 min
The 4-Stage Roadmap for AI Application Development: A Career Transition Guide from Beginner to Senior Engineer
A 4-stage roadmap for AI application development: from Python and RAG basics to Agent cluster architecture, covering the core skills needed for career growth.
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·3 min
AI Agent Hands-On Learning Path: A Complete Guide from Beginner to Enterprise-Level Development
A systematic AI Agent development learning roadmap covering prompt engineering, RAG, multi-Agent collaboration, tool calling, and more—with phased learning advice and 28 hands-on project references.
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·3 min
Fed Up with Every AI Coding Assistant, a Veteran Game Developer Built His Own Coding Agent
Veteran game dev Mario tried every AI coding tool including Claude Code, found them all lacking, and built Pi — a minimalist, extensible coding agent framework centered on developer control.
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·3 min
Complete Guide to LangChain 0.3: Module Architecture & Core Development in Practice
A deep dive into LangChain 0.3's module architecture, message abstraction, prompt templates, output parsers, LCEL chains, LangSmith tracing, and LangGraph for mastering LLM application development.
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·3 min
Loopcraft Explained: Loop-Stacking Architecture Design in AI Agent Development
Deep dive into Loopcraft loop-stacking architecture for AI Agent development, covering retry, self-validation, and meta-learning loops to boost reliability.
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·4 min
AI Full-Stack Development Architecture: A Three-Layer Progressive Path from Prototype to Production
A detailed guide to AI full-stack development architecture covering Node.js+TypeScript+Monorepo engineering, Docker CI/CD deployment, and AI engine design with interview tips.
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·3 min
AI Agent Learning Roadmap: A Four-Step Practical Guide from Zero to Job-Ready
A proven AI Agent learning roadmap covering four core elements, mainstream architecture patterns, multi-agent collaboration, and hands-on projects to go from zero to job-ready in three months.
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·4 min
Claude Code Codex Plugin Integration: A Practical Guide to Dual-AI Adversarial Review for Better Code Quality
Learn how to install and configure the Codex plugin in Claude Code, leveraging dual-AI adversarial review to uncover code vulnerabilities across seven attack surfaces.
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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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·4 min
AI Agent Development: A Complete 6-Week Systematic Learning Roadmap
A 6-week systematic learning roadmap for AI Agent development, covering core architecture, ReAct principles, multi-agent collaboration, RAG integration, and deployment.
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·2 min
A Complete Guide to LLM Infrastructure: Core Challenges from GPU Clusters to Inference Optimization
A deep dive into core challenges and key technologies for LLM infrastructure, covering GPU cluster management, inference optimization, distributed training, cost control, and observability.
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·3 min
Hands-On with Tencent Cloud ADP 4.0: How an Enterprise-Grade Agent Development Platform Tackles the Agent Commercialization Challenge
Hands-on review of Tencent Cloud ADP 4.0: testing its full-lifecycle Agent management — from rapid creation and enterprise integration to automated evaluation and Skill governance for real-world deployment.
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·2 min
Java Backend Interview Prep Roadmap: A 6-Week Sprint Guide Integrating AI Agent Development
A systematic 6-week Java backend interview prep roadmap covering JVM internals, Spring Boot, Redis, microservices, plus Spring AI, LangChain4j, and RAG for AI Agent development.
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·4 min
A Beginner's Guide to Deep Agents: The Complete Evolution from Basic Agents to Deep Agents
A systematic guide covering the evolution from traditional AI agents to Deep Agents, including core architectures, four development stages, technical features, and practical developer guidance.
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·4 min
AI Agent in Practice: Building a Commercial-Grade Programming Agent from Scratch
A deep dive into AI Agent development, from the core principles of perception-decision-action to a Vue3 auto-creation demo, covering LangChain, LangGraph, MCP, and the full tech stack.
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From Prompt Engineer to Loop Architect…
·2 min
From Prompt Engineer to Loop Architect: The Paradigm Shift in AI Programming
Explore the paradigm shift from prompt engineering to loop architecture in AI programming. Learn about Anthropic's Routines, the six elements of mature coding loops, and token cost strategies.
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·3 min
Nexent Open-Source Platform: Zero-Code Auto-Generation of Production-Grade AI Agents
Deep dive into Nexent's open-source platform for zero-code production-grade AI Agent generation, covering Harness Engineering, built-in controls, use cases, and comparisons with AutoGen and CrewAI.
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