#agent communication
26 related articles
A Practical Guide for Agent Engineers:…
·3 min
A Practical Guide for Agent Engineers: The Three-Phase Core Methodology for AI Programming
Master the three-phase methodology for Agent engineers: Ideation, Iteration, and Evolution. Build reliable AI programming systems without over-engineering.
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·2 min
Cosmos Unified Agents Platform: A New Paradigm for AI Agent Collaboration Through Shared Context and Memory
Deep dive into how the Cosmos Unified Agents Platform solves multi-AI Agent collaboration challenges through shared context and memory mechanisms, and its positioning in enterprise multi-Agent orchestration.
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·2 min
Andrew Ng's Agent Skills Course Explained: Building Reusable Professional Skills for AI Agents
Deep dive into Andrew Ng's Agent Skills course with Anthropic, covering Skills architecture, progressive loading, MCP integration, and hands-on examples for building reusable AI agent skills.
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·4 min
7 Core Features of Hermes Agent Explained: The Real Reasons Users Are Abandoning OpenCore
Deep dive into Hermes Agent's 7 core features including Kanban multi-tasking, /goal deep execution, and multi-agent architecture, compared with OpenCore's stability and performance issues.
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Tutorials·2 min
MCP Transport Protocols Explained: SSE vs. Streamable HTTP vs. STDIO
Explore MCP's three transport protocols — SSE, Streamable HTTP, and STDIO — their differences, use cases, and configuration tips for developers.
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Tutorials·3 min
Complete Guide to Building AI Agent Projects with WindSurf: Practical Tips & Techniques
Complete practical guide to building AI Agent Frameworks with WindSurf, covering technology selection, component generation, code refactoring, debugging, and deployment tips.
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Product Reviews·2 min
JCode: Deep Dive into a Multi-AI Agent Collaborative Programming Framework
Deep dive into JCode, an open-source Coding Agent Harness designed for multi-Agent collaboration. Features Agent Memory, Swarm collaboration, multi-Provider access, and self-evolution with just 14ms first-frame latency and 117MB for 10 sessions.
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Product Reviews·4 min
OpenClaw vs Hermes: In-Depth Comparison and Selection Guide for Open-Source AI Agent Frameworks
In-depth comparison of OpenClaw and Hermes open-source AI Agent frameworks covering architecture, memory systems, auth, plugins, and channel distribution to guide developer selection.
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Deep Dives·4 min
Is Context Engineering the Core of Agent Development? A Deep Dive into Architecture and Practical Solutions
Deep dive into context engineering as the core of Agent development, covering five context modules, four pain points, and dynamic assembly solutions including compression, hybrid retrieval, multi-Agent architecture, and state machine control.
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Tutorials·1 min
Getting Started with Agent Development: 3 Agent Types × 8 Core Mechanisms Fully Explained
A complete breakdown of AI Agent development: 3 agent types (autonomous, collaborative, orchestration-based), 8 core mechanisms, a 5-stage learning path, and framework selection guidance.
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Deep Dives·3 min
How Multi-Agent Teams Solve AI Hallucination and Make AI Reliable
Deep analysis of how multi-agent architecture solves AI hallucination. From context rot to adversarial debate mechanisms, see how Anthropic, xAI, and Kimi reduce hallucination rates from 12% to 4.2%.
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Product Reviews·3 min
Deep Comparison of AI Coding Tools: Why OpenClaw Is Not Suitable for Writing Code
Deep comparison of AI coding tools like VS Code and Google IDE, revealing why OpenClaw is just a gateway not a coding tool, with analysis on model binding issues.
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Tutorials·3 min
OpenClaw + Claude Code Token-Saving Guide: Zero-Polling with Hooks Callbacks
How to solve high Token consumption when OpenClaw calls Claude Code. Achieve zero-polling with Stop Hook and Session End dual callbacks, combined with Agent Teams for fully automated dev workflows.
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Product Reviews·3 min
Mavis Hands-On Review: Multi-Agent Collaboration vs. Single Agent — A Comprehensive Comparison in Academic Research and Web Development
In-depth review of Mavis multi-agent platform across academic retrieval, literature review, and web development. Multi-agent mode significantly outperforms single agents in accuracy and reliability.
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Tutorials
MCP Protocol Practical Guide: The Stan…
·2 min
MCP Protocol Practical Guide: The Standard Interface for Connecting LLMs to Everything
Deep dive into MCP (Model Context Protocol) principles and practical applications. Learn how LLMs connect to external tools via MCP to become agents, covering Java tech stacks, MCP Server ecosystem, Cherry Studio demos, and A2A protocol comparison.
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Tutorials
WenzAgent Open-Source Framework: A Pra…
·2 min
WenzAgent Open-Source Framework: A Practical Tutorial for Multi-Agent Collaborative Management on LAN
A detailed guide on deploying WenzAgent, an open-source multi-Agent management framework under Apache License, supporting LAN-based multi-device AI agent collaboration with Server-Client architecture.
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Tutorials·2 min
Claude Code Multi-Agent Collaboration in Practice: Subagent Division of Labor and a Lite TARS Concept
Explore Claude Code multi-Agent collaboration with Planner+Packager Subagents for semi-automated project iteration, plus a lite TARS orchestration concept revealing Agent inter-calling limits.
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Deep Dives·3 min
Single-Agent vs Multi-Agent: A Three-Step Decision Framework for Choosing the Right Architecture
Based on Anthropic's engineering practices, a detailed three-step decision framework for single-agent vs multi-agent architecture: bottleneck identification, technical feasibility, and business value filtering.
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Deep Dives·3 min
Claude Sub-agents vs Agent Teams: How to Choose Between Two AI Multi-Agent Collaboration Patterns
Deep comparison of Claude sub-agents vs agent teams: architecture differences, use cases, and real-world results. A Pokémon RPG case study shows how agent teams achieve 95%+ feature completeness.
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Tutorials·2 min
Claude Code Sub-Agent: A Practical Guide to Multi-Agent Collaboration for Enhanced Development Efficiency
Deep dive into Claude Code Sub-Agent mechanism with a practical blog writing + Git commit case study, showing how multi-agent collaboration solves instruction loss and context bloat issues.
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