#Agent architecture
4198 related articles
·5 min
Getting Started with AI Agents: Concepts, Architecture, and Core Components Explained
What is an AI Agent? Starting from Bill Gates' claim about the computing revolution, this article explores AI Agents' intuitive concepts, four core components (LLM+Planning+Memory+Tools), and what Agent development means for programmers.
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·7 min
Turning Any AI Agent into an Orchestration Hub: A Deep Dive into Multi-Agent Collaboration Architecture
An in-depth analysis of the "any Agent as an orchestrator" design philosophy, exploring the technical implementation of multi-Agent collaboration, context management, and workflow automation.
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·3 min
Deep Dive into LangChain's Three Core Modules: A Complete Guide to Agent Architecture Thinking
Deep dive into LangChain's three modules: Chain pipelines, LangGraph state graphs, and autonomous planning Agents. From RAG to ReAct — build your AI architecture thinking.
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·3 min
Files over Tools: Rethinking AI Agent Architecture with a Virtual Filesystem
Why give AI Agents a virtual filesystem and bash instead of more tools? A deep dive into the "Files over Tools" design philosophy, tool bloat, Unix principles, security sandboxing, and hybrid architectures.
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·5 min
Agentic MapReduce: The Key Architecture for AI Agents to Achieve Distributed Reasoning Across Entire Codebases
Cognition's Agentic MapReduce architecture combines classic distributed computing with autonomous agents to break LLM context window limits, enabling multi-Agent parallel reasoning across entire codebases.
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·4 min
Agent Skills Deep Dive: A Complete Guide to Modular Agent Development Architecture
A deep dive into Anthropic's Agent Skills architecture — clarifying its difference from MCP, and how modular Skill packages enable scalable, maintainable enterprise agent development.
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·6 min
LangChain 1.3 in Practice: A Complete Guide to DeepAgent Architecture and the Harness Philosophy
An in-depth look at LangChain 1.3's core modules and DeepAgent architecture—covering the Harness philosophy, LangGraph internals, HITL, memory management, and guardrails to master production-grade AI Agent development.
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·6 min
Deep Dive into GPT-5.6 Ultra's Sub-Agent Architecture and Ten Key AI Industry Trends
In-depth analysis of GPT-5.6 Ultra's sub-agent collaborative reasoning, the global rise of Chinese AI models, world-model evaluation gaps, and AI's real-world deployment challenges and bubble warnings.
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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
Harness Architecture Explained: The Next-Generation Design Paradigm for Agent Development
A deep dive into Harness Architecture — the next-gen Agent design paradigm. Covers its evolution from prompt engineering and context engineering, multi-agent collaboration, sandbox security, feedback loops, and why it's a must-have for LLM developer interviews.
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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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·6 min
What Is Vibe Coding? A Complete Breakdown of the LLM + Agent + Workflow Three-Layer Architecture
Vibe Coding, coined by ex-Tesla AI Director Karpathy, redefines AI programming. This article breaks down the LLM + Agent + Workflow three-layer architecture.
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·3 min
Building an Automated Penetration Testing System with Claude: A Deep Dive into Memory, Skills, and Agent Architecture
A security researcher demonstrates how to customize Claude into an automated penetration testing system with memory, skills, and a knowledge base — successfully compromising CTF targets and finding real Bug Bounty vulnerabilities.
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·7 min
Getting Started with LangGraph Multi-Agent Development: Architecture Analysis and Three Practical Case Studies
Learn LangGraph multi-agent development covering Supervisor and Collaboration architectures, with three hands-on projects: code assistant, prompt assistant, and WebRTC digital human.
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·8 min
MCP Server or Agent Skills? A Clear Guide to Architecture Decision-Making
MCP Server vs Agent Skills: how to choose? This article systematically outlines an AI Agent architecture decision framework across three dimensions—essential differences, applicable scenarios, and judgment criteria.
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Unified MCP Endpoint: Building Agent A…
·2 min
Unified MCP Endpoint: Building Agent Architecture with Progressive Disclosure
A reference architecture for AI agents: converge Skills, Files, Memory, and Generation into a single MCP endpoint using progressive disclosure, unified API keys, and a shared credit balance.
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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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·4 min
AI Agent Memory System Architecture Breakdown: A Practical Selection Guide from 16 Open-Source Projects
Deep analysis of memory system architectures across 16 open-source AI Agent projects including Hermes, Goose, NanoClaw, and Deerflow, covering FTS5 vs vector search, multi-tenant isolation, and LLM auto-extraction.
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·4 min
OpenAI Agents SDK in Practice: Harness and Sandbox Separation Architecture Explained
Deep dive into OpenAI Agents SDK updates covering Harness-Compute separation, Codex-style orchestration, sandbox snapshots, skills system, and multi-agent collaboration with practical demos.
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·3 min
Agent + Skill Architecture: A Practical Guide to AI-Driven End-to-End Automated Testing
Learn how to build an AI-driven API automation testing framework using Agent+Skill architecture with Claude Code, covering test case generation, script execution, and report output.
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