#Agent architecture
4176 related articles
·2 min
Voice AI Agent Architecture: Pipecat Flows vs Vapi Squad — A Deep Comparison
A deep comparison of Pipecat Flows and Vapi Squad for voice AI agent architecture — covering latency, accuracy, multi-agent handoffs, and when to use each.
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·3 min
AI Agent Development Landscape: From Architecture Design to Production Deployment
A systematic overview of the AI Agent tech stack: RAG retrieval, Agent planning, MCP protocol, AI Gateway, and observability — helping developers build production-grade AI systems.
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The Complete Guide to AI-Native Architecture: RAG, Agents, and MCP
A comprehensive guide to AI-native application architecture: LLM inference, RAG retrieval (vector DB/knowledge graph/BM25), Agents, MCP tool calling, AI gateways, and observability — end-to-end.
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From Workflow Graphs to Distributed Protocols: Dissecting Multi-Agent System Architecture Paradigms
How does Cosmonapse replace LangGraph's graph structure with event-driven distributed protocols? A deep dive into two multi-agent architecture paradigms, their trade-offs, and when to use each.
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Enterprise AI Deployment: The Core Decision Between Ontology and Agent Architecture
A systematic guide to enterprise Ontology: its core value, tools like OntoFlow and FIBO, when to build one, and how to deploy business-domain-level AI Agents.
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LangGraph Multi-Agent Architecture in Practice: From Core Components to Production Deployment
A deep dive into LangGraph multi-agent architecture — covering hierarchical, network, and pipeline patterns with three hands-on projects using LangGraph 0.3.
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Coze Deep Dive: Multi-Agent Collaboration Platform Architecture and Usage Guide
Deep dive into Coze's three core capabilities: multi-person multi-AI collaboration, customizable agents, and cross-platform project management. Covers credits, Dify comparison, and a practical learning path.
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GPT-5.6 Deep Dive: Tiered Models + Multi-Agent Architecture, LLMs Evolve into Workflow Engines
GPT-5.6 isn't just a capability upgrade — it's an architectural shift: tiered models, programmatic tool calling, and multi-agent collaboration turn LLMs into workflow engines for production systems.
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·3 min
From RAG to Agent: A Complete Architecture Guide for AI-Native Systems
A comprehensive guide to modern AI-native system architecture: LLM reasoning, three RAG paradigms (vector/knowledge graph/BM25), Agents, MCP tool calling, AI gateways, and observability for enterprise AI.
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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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Smart Proxy: The Key Architecture for …
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Smart Proxy: The Key Architecture for Running AI Coding Agents Safely and Autonomously
Too much AI permission is risky; too little kills productivity. A smart proxy acts as a controllable middleware layer — intercepting, auditing, and policy-gating AI agent actions in tools like Claude Code and Cursor for safe autonomous operation.
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Agentic Loop Explained: The Three-Loop…
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Agentic Loop Explained: The Three-Loop Architecture of Reasoning, Tool Use, and Orchestration
A deep dive into the Agentic Loop — breaking down the three-layer architecture of reasoning, tool use, and orchestration to help developers build and debug reliable AI agent systems.
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When Does Multi-Agent Architecture Slow Down Your Workflow? A Single vs. Multi-Agent Comparison
Multi-agent architecture isn't always better. This article analyzes how splitting tasks across agents introduces latency, token costs, and error propagation — with a practical framework for deciding when to use single vs. multi-agent design.
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Deep Dive into ChatGPT Ultra's Parallel Sub-Agent Architecture: Parameters, Configuration, and Best Practices
A deep dive into ChatGPT Ultra mode's parallel sub-agent architecture, covering concurrency limits, delegation depth, token optimization, and configuration best practices for GPT-5.6.
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·3 min
Complete Guide to LangChain 1.3: From Models and Agents to DeepAgent Architecture
A complete guide to LangChain 1.3: LLM invocation, Agent tool calling, Harness architecture, LangGraph, RAG, and DeepAgent — build a clear, modern Agent development knowledge base.
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MCP vs. Skills: A Clear Breakdown of AI Agent Architecture Layers
MCP and Skills aren't alternatives — they occupy different layers of AI Agent architecture. This article breaks down Function Call, MCP, and Skills to clarify each layer's role.
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Harness Architecture in Practice: A Deep Dive into Enterprise-Level Agent Projects
A deep dive into Harness architecture in enterprise Agent projects, covering MCP protocol, sandbox isolation, multi-model scheduling, and ASGI deployment — key topics for LLM job interviews.
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·4 min
GPT-5.6 In-Depth Analysis: Model Architecture, Performance Benchmarks, and a Complete Guide to the Agentic Era
A comprehensive analysis of GPT-5.6: the Sol/Terra/Luna product-line positioning, MoE architecture, 1.5M token context, coding agent benchmarks, and the shift from Chatbot to Agent.
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·3 min
Langchain.js Agent Development: A Guide to Deconstructing the OpenClaw Engine Architecture
An in-depth analysis of AI agent development based on Langchain.js—comparing workflow agents and Agent Loops, deconstructing the TypeScript implementation path of an OpenClaw-like engine, covering structured output, MCP, and LangGraph.
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Multi-Agent Collaboration with Claude Code and Codex: A Guide to Architecture Design and State Management
A deep dive into Claude Code and Codex multi-Agent collaboration: architecture design, state-driven coordination, private/public zone division, and building efficient AI programming teams.
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