This guide explains the concept of AI Agent Skills, walks through creating, installing, and managing a Skill—including file structure, YAML metadata, progressive loading, platform-specific considerations—and introduces a one‑stop development assistant that streamlines Skill development and deployment.
The article argues that the performance gap of AI agents stems from the missing or poorly designed Harness layer, and explains how systematic engineering of prompts, tools, context strategies, hooks, sandboxing, and feedback loops can turn a raw model into a reliable, high‑performing autonomous agent.
The article dissects the concept of an Agent Harness—a comprehensive software infrastructure that wraps large language models to enable autonomous agents—detailing its three engineering layers, twelve production‑grade components, benchmark improvements, implementation patterns across Anthropic, OpenAI, LangChain, and design trade‑offs such as orchestration loops, tool integration, memory, context management, error handling, and safety.
The article demonstrates how combining three simple tools—fetch_url, write_file, and run_python—allows an AI agent to autonomously research web content, generate Python code, execute and debug it, and finally store the results, all within a single conversational loop.
This article documents a complete hands‑on experiment linking OpenSpec and Superpowers, showing that while the initial spec proposal works, three critical integration points break—two fail outright and one never triggers—leaving the envisioned seamless, spec‑driven development pipeline unachievable.
The article explains that large‑model agents differ from traditional chatbots by perceiving goals, planning steps, invoking tools, and executing actions autonomously, covering their definition, core modules, ReAct reasoning‑acting loop, single‑ versus multi‑agent systems, current industry trends, and the reliability, safety, observability, and cost challenges they face.
The article explains how Agent Harness structures the interaction between user intent and LLM output, detailing its components, long‑conversation handling, layered memory, tool integration, and a four‑stage pipeline demonstrated by an Essay Harness prototype, highlighting design trade‑offs and practical implementation details.
The article explains that an AI Agent combines a large language model, a clear goal, and callable tools in a multi‑step reasoning loop, detailing its perception‑plan‑act architecture, differences from plain chat, common misconceptions, and practical questions for evaluating such systems.
OpenAI’s GPT‑5.5 delivers higher performance on complex coding, tool use, and professional workflows, but its token price is roughly twice that of GPT‑5.4; developers should adopt it for demanding, multi‑step tasks while keeping GPT‑5.4 for stable, cost‑sensitive workloads after real‑world testing.
This article walks through a practical, step‑by‑step construction of a LangChain agent—from basic concepts and a simple weather‑query agent to a more complex market‑research agent, adding memory and RAG capabilities, and finally comparing LangChain with LangGraph.
The article explains how Function Calling enables large language model agents to overcome knowledge staleness and hallucination by invoking external tools—such as search, email, code execution, and databases—to fetch real‑time data, perform actions, and deliver verifiable, multi‑step responses.
This article breaks down an AI agent's workflow—perception, planning, action, and memory—using a product‑launch example, explains reasoning methods like Chain‑of‑Thought and ReAct, details tool integration, memory types, common failure modes, and why planning and tool ecosystems are essential.
The article explains Anthropic’s 2025 Agent Skills standard, how it enables AI to perform actions such as database queries and API calls, and provides a detailed guide covering its definition, modular design, industry adoption, and practical usage scenarios.
The article compares three ways agents connect to external systems—direct API calls, CLI tools, and the Model Context Protocol (MCP)—and explains how MCP provides a standardized, scalable layer with rich semantics, authentication, and context‑saving techniques that enable production‑grade cloud agents.
The article introduces agentic‑stack, a portable .agent folder that lets eight AI coding tools share a unified memory, skill, and protocol system, detailing its four‑layer memory model, progressive skill disclosure, shim‑based adapters, review protocols, practical team scenarios, installation steps, and architectural design.
Introducing MCP (Model Context Protocol), an open standard released by Anthropic that unifies AI model interaction with external tools, databases, and services through a USB‑like interface, the article dissects its design goals, architecture, message types, Python SDK implementation, client integration, production best practices, and future roadmap.
The article explains how conventional "smart" apps merely react to user commands, while AI Agents combine large language models, tool‑calling capabilities, and explicit goals to autonomously plan, act, and iterate, offering a new software paradigm with both promising use cases and current limitations.
This article provides an in‑depth, eight‑dimension comparison of Hermes Agent and OpenClaw, examining their core philosophies, learning abilities, integration options, deployment ease, security, standout features, overall strengths, and guidance on selecting the right AI agent for different user scenarios.
The article walks through building a minimal 20‑line agent loop, explains each step—from reading a task to invoking tools and feeding observations back—then shows how real systems like Claude Code, OpenClaw and Pi add layers of harness, memory, permission and validation to make the loop safe and reliable in production.
The article dissects the Agent Harness concept—covering its definition, three engineering layers, twelve production‑grade components, detailed orchestration loops, context‑management tricks, verification strategies, and how frameworks like Anthropic, OpenAI, LangChain, CrewAI and AutoGen implement these patterns, revealing why the same model can yield wildly different results.
The article explains that an Agent Harness is the complete runtime system surrounding a language model—handling the main loop, tools, context, state, permissions, and validation—and shows why this engineering layer, not the model itself, determines the stability and scalability of AI agents.
The article examines the rise and decline of the Model Context Protocol (MCP), outlines its four critical flaws—including context bloat, architectural complexity, security risks, and passive tool design—while presenting command‑line interfaces (CLI) as a more efficient, secure, and debuggable alternative for AI agents, and discusses hybrid approaches and practical implementations.
This tutorial walks through creating a LangChain‑based AI agent by covering model integration, tool definition with @tool, short‑ and long‑term memory handling via checkpointers and vector stores, and assembling everything with create_agent, middleware, and code examples for a functional travel assistant.
An AI Agent is a large‑model system that can perceive its environment, plan steps, invoke tools, and remember past interactions to autonomously achieve user‑specified goals, distinguishing it from simple chatbots that only answer questions.
Anthropic’s Claude Managed Agents provide a cloud‑based API that lets you build, deploy, and orchestrate long‑running AI agents without handling sandboxing, state management, or error recovery, while offering versioned agents, configurable environments, streaming events, custom tools, pricing details, and real‑world use‑case examples.
This article explores six practical AI Agent design patterns—ReAct, Tool Use, Reflection, Planning, Multi‑Agent, and Human‑in‑the‑Loop—detailing their principles, Java Spring AI implementations, advantages, drawbacks, and suitable scenarios, and provides guidance on selecting and combining them for robust AI applications.
This article provides a detailed, side‑by‑side analysis of the Hermes and OpenClaw AI agent frameworks, covering their design philosophies, runtime flows, tool ecosystems, memory and skill systems, deployment options, and practical selection guidance so developers can choose the right solution without months of trial and error.
This article provides a step‑by‑step, non‑technical walkthrough of Hermes, the self‑evolving AI agent from Nous Research, covering its core AIAgent brain, capabilities, one‑line installation, multi‑platform entry points, detailed architecture layers, context handling, SQLite‑based memory, and runtime flow, all illustrated with diagrams and commands.
This tutorial walks through the MCP architecture, shows how to install @langchain/mcp-adapters, connect real Amap and Playwright MCP servers to a LangGraph ReAct agent, combine multiple servers, choose between stdio and HTTP transports, and avoid common production pitfalls.
This guide explains what an AI Agent is, how it differs from a chatbot, the importance of tools and prompt design, common pitfalls, multi‑agent coordination, and practical steps to build, monitor, and deploy production‑grade agents.
This article walks through the practical differences between simple Retrieval‑Augmented Generation and a full Deep Research Agent, explains the four pillars that support such agents, demonstrates a minimal ReAct implementation with robust error handling, and shares interview tips for showcasing these systems.
This article provides a comprehensive, engineering‑focused breakdown of large language models, covering their Transformer roots, tokenization, context windows, prompt engineering, tool integration via MCP, and autonomous agents, while offering practical examples and actionable insights for developers.
This article introduces OpenHarness, an open‑source Python implementation that simplifies building production‑level AI agents by providing lightweight core infrastructure, detailed feature breakdown, architecture overview, and sample code to help researchers and developers understand and create custom intelligent agents.
This article analyzes the architecture of coding agents and their harnesses, detailing six essential components, how they interact with real‑time repository context, prompt caching, tool validation, context‑bloat control, structured memory, and delegation, while providing concrete Python examples and visual diagrams.
The article systematically breaks down the six essential building blocks of a programming agent—live repository context, prompt shape and cache reuse, structured tool access and validation, context reduction, structured session memory, and bounded sub‑agent delegation—illustrated with a Mini Coding Agent implementation and comparisons to Claude Code, Codex, and OpenClaw.
Google’s new Java Agent Development Kit 1.0 provides a structured, plugin‑based framework that lets Java backend teams embed large‑language‑model agents, manage context and token limits, integrate secure tools, persist state, and enable cross‑language Agent2Agent collaboration without rewriting existing architectures.
The article explains how the ReAct reasoning‑acting loop transforms a generic AI troubleshooting agent into a precise fault‑diagnosis tool, boosting accuracy from about 30% to over 90% and even out‑performing human engineers, while providing a step‑by‑step Spring AI implementation.
This article presents an eight‑question quiz covering Claude Code’s tool system limitations, GitHub integration permissions, planning vs. thinking modes, Claude.md file types, custom command creation, hook behavior, and hook purposes, followed by the correct answer key for self‑assessment.
This tutorial walks you through creating a functional AI agent that can receive goals, plan steps, invoke tools, and iterate until task completion, covering environment setup, core loop implementation, tool integration, error handling, and testing without requiring prior programming experience.
The article explains ReAct—a Reason‑Act framework for large language model agents that observes, reasons, takes actions via tools, receives feedback, and iterates—highlighting its distinction from plain QA, its step‑by‑step workflow, practical importance, and a weather‑query example.
The article explains the built‑in Claude Code /simplify command, how it uses three parallel AI agents to review and automatically fix code, demonstrates real‑world bugs it uncovered in Java projects, compares it with traditional linters, and offers practical tips and integration guidance.
This comprehensive tutorial walks you through installing, configuring, and using Claude Code, covering its tool‑use mechanism, context management, command shortcuts, custom MCP servers, and practical tips for integrating the assistant into real‑world development workflows.
This article explains the fundamentals of large‑model function calling, why it’s needed to bridge language models with real‑world tools, and provides a step‑by‑step implementation in Python—including tool definition, intent extraction, local execution, and result integration—complete with code samples and diagrams.
GLM-5.1 is now available to all Coding Plan subscribers, including the $10/month Lite tier, scoring 45.3 on SWE‑bench—just 5.4% below Claude Opus 4.6’s 47.9—while offering 20+ tool integrations and a manual switch from the default GLM‑4.7 model.
From a simple black‑box view where an agent receives a user request and returns an answer, the article breaks down modern AI agent designs—detailing the pure Chain‑of‑Thought reasoning loop, the ReAct reasoning‑acting cycle, tool integration, iteration tuning, and how to choose the optimal architecture for production.
This article explains the 2026 definition of Agentic Workflow, outlines its four core components, presents a five‑step execution loop, shares real‑world productivity data, and provides ready‑to‑use prompts and tool recommendations for instantly applying the concept in the workplace.
OpenClaw is an AI Agent framework that bridges chat platforms and large language models, enabling automated tasks through context‑engineered prompts, tool usage, memory management, sub‑agents, and security controls, while illustrating practical examples, workflow steps, and mitigation strategies for potential shell‑command exploits.
This article explains what AI agents are, how they differ from ordinary language‑model interfaces, and walks through OpenClaw’s workflow, tool usage, security challenges, memory handling, and advanced features such as sub‑agents and context compaction, offering practical insights for building safe autonomous AI systems.
OpenClaw distinguishes itself from ordinary chatbots by employing an Agent Loop—a task‑driving execution chain that normalizes inputs, assembles context, makes model‑based decisions, suspends for tool results, and writes back state, enabling continuous task progression rather than single‑turn replies.
The article explains why an AI Agent is more than a simple LLM API call, outlines its four essential modules—memory, planning, tool use, and feedback—shows how they differ from ordinary models, and offers practical steps and common pitfalls for building a production‑grade single‑agent system.
Deep Agents, an open‑source framework built on LangGraph, bundles planning, file‑system tools, sub‑agent coordination and context management into a ready‑to‑run AI agent that can be launched with three lines of Python code and fully customized for diverse applications.
The article explains the four essential components of AI Agents—brain, memory, tool, and planning layers—illustrates their implementation with Python code, compares planning strategies, shares a real-world OOM fault‑diagnosis case, and lists common pitfalls to help newcomers build functional agents.
This article introduces OmniGAIA, a challenging full‑modal benchmark with 360 real‑world tasks, and OmniAtlas, a training framework that equips multimodal agents with active perception and tool‑integrated reasoning, showing substantial performance gains over existing open‑source models through extensive experiments and analysis.
This article explains how Qoder’s slash commands can eliminate unnecessary project scans and web searches, showing side‑by‑side comparisons, command file structures, customization tips, and best‑practice recommendations to speed up AI‑assisted coding while saving tokens.
The article explains that AI coding agents operate as a simple while‑loop driven by context engineering, details their core control flow, compares various tools, and provides a step‑by‑step Python implementation demonstrating how to define tools, system prompts, and the ReAct loop for practical use.
OpenAkita is an open‑source multi‑Agent AI assistant that automatically splits tasks among specialized agents, offers 89 built‑in tools across 16 categories, supports 30+ large models and six IM platforms, provides a zero‑CLI graphical setup, and includes a three‑layer memory system with self‑evolving capabilities.
This tutorial walks through constructing OpenClaw from scratch, covering persistent JSONL sessions, SOUL.md persona files, tool definitions and an agent loop, permission checks, gateway architecture, context compression, long‑term memory, command queuing, scheduled heartbeats, and multi‑agent routing, all with concrete Python code examples.
This article introduces LangChain's DeepAgents framework, explains its design goals, compares it with LangChain and LangGraph, and provides a step‑by‑step code walkthrough that demonstrates task planning, sub‑agent delegation, tool usage, and result generation for building complex AI agents with just a few lines of code.
This comprehensive guide walks you through five development stages of AI agents—core concepts, advanced planning, memory management, tool integration, and enterprise deployment—answering 100 practical questions that reveal definitions, architectures, best‑practice patterns, safety measures, and performance‑optimisation techniques for production‑grade agents.
OpenAI’s new WebSocket mode for the Responses API keeps a persistent connection, sending only incremental inputs and previous response IDs, which cuts overhead and can boost end‑to‑end speed by 20‑40% for workflows that involve many tool calls.
Learn how to connect PHP-based AI agents to the Model Context Protocol (MCP) using the open‑source Neuron AI framework, covering MCP fundamentals, server setup, tool integration, and example code for creating custom agents that can invoke external APIs, databases, and web content.
This tutorial explains how the Model Context Protocol (MCP) standardizes AI agent integration by replacing custom tool code with a JSON‑RPC based, auto‑discovered ecosystem, walks through configuration, core loading logic, code implementation, a runnable example, and compares MCP with traditional tool use.
This tutorial walks through adding custom, pre‑built, and server‑side tools to a LangGraph AI agent, demonstrates a ReAct workflow, implements conditional edges for web search, enforces structured output for intelligent shutdown, and shows how to monitor token usage with callbacks, all with runnable Python code.
OpenClaw is an open‑source, local‑first AI agent platform that acts as a digital employee capable of autonomously executing tasks on your computer, offering multi‑channel interaction, persistent memory, and a modular architecture that bridges the gap between conversational AI and real‑world operations.
This comprehensive guide walks Java backend developers through the fundamentals of AI agents, comparing agents with traditional workflows, detailing core components such as LLMs, tools, and memory, and exploring practical patterns, frameworks, and code examples to help them successfully shift into AI agent development.
This article traces the evolution of large‑model capabilities from early plugins to the standardized Agent Skills framework, explains the core concepts, technical composition, and progressive disclosure mechanism, and provides a step‑by‑step practical guide for building, configuring, and deploying Skills across ecosystems.
Clawdbot, an open‑source AI Agent gateway with 54.6k GitHub stars, offers persistent three‑month memory, 50+ built‑in tools, and multi‑model channel management; built on TypeScript/Node.js, it delivers strong automation but incurs notable API costs and a learning curve, making it ideal for long‑term AI‑driven projects yet less suited for casual users.
The article explains how fragmented AI‑coding Skills across tools like Cursor, Claude, and Codex cause version drift and storage waste, and introduces the open‑source Skills Hub, which centralizes Skills in a single repository and synchronizes them to all supported editors via symlinks.
This guide explains Claude Code’s agent loop, model choices, built‑in tool categories, project access scope, session handling, checkpoint and permission controls, and practical tips for efficiently using the AI‑driven terminal assistant to write, test, and refactor code.
This guide distills twelve core best‑practice secrets—derived from 720,000 paying users and a billion lines of daily code—on how to make AI agents obey prompts, plan before coding, manage context, use rules, custom commands, hooks, multi‑agent parallelism, and test‑driven development for reliable, high‑productivity outcomes.
The article outlines a five‑tier taxonomy of intelligent agents—from a standalone language‑model reasoning engine lacking real‑time perception, through tool‑enabled problem solvers, context‑engineered planners, collaborative multi‑agent teams, up to self‑evolving systems that can create new tools or agents to fill capability gaps.
This whitepaper explains how modern AI agents evolve from simple language models to autonomous, multi‑step systems, detailing their core components, five‑step reasoning loop, classification levels, design patterns, deployment options, observability, security, and continuous learning with concrete examples.
The article explains Android Studio’s latest AI Agent, covering its three core concepts—Tools, Context, and Model Context Protocol—while showing practical examples of built‑in tools, knowledge‑base integration, Figma linking, and a full UI‑generation workflow that lets developers create, refine, and fix Jetpack Compose apps using natural language prompts.
Transitioning from simple chatbots to sophisticated agents, this article explains how expanding context becomes a core variable, detailing the evolution from prompt engineering to context engineering, the challenges of managing growing context, and practical solutions like structured context, tool integration, and the MCP framework for reliable AI systems.
The article analyzes six emerging trends in Chinese AI coding tools—model capability parity, open tool integration, spec‑driven development, lower entry barriers, self‑validation, and full‑stack automation—arguing that future success depends on end‑to‑end engineering reliability rather than mere code generation or emotional support.
This article explains how to bring Anthropic's Skills concept into the open‑source LangChain DeepAgents framework by detailing the discovery, system‑prompt injection, progressive loading, and execution phases, and provides a complete code‑driven example using a web‑research Skill.
This article introduces the Model Context Protocol (MCP), a universal standard that lets large language models seamlessly connect to databases, APIs, local files, and third‑party services, explains its architecture, core primitives, practical Python implementation, trade‑offs, security considerations, and how it compares with other integration approaches.
In a live discussion hosted by Prof. Yang Jian with experts Zhang Xi and Cui Chen, the panel explores the essential abilities of mature GUI agents, the role of multimodal models in visual understanding, the transfer of code‑agent techniques to GUI tasks, edge‑device performance trade‑offs, complex planning, tool ecosystems, deployment challenges, and future breakthrough scenarios.
This article explains how Anthropic's Skills and Model Context Protocol (MCP) servers complement each other to let Claude agents follow specific workflows, access external tools, and produce consistent, reliable outputs, illustrated with real‑world use cases and a quick reference guide.
Function Calling lets large language models invoke external tools or APIs during a conversation, transforming them from passive responders into proactive assistants; this guide explains the concept, workflow, and practical implementations with weather, parallel queries, and stock price examples using OpenAI’s Python SDK.
This guide explains the concept of human‑in‑the‑loop (HITL) interruptions in LangGraph, outlines the core mechanisms such as persistent state and dynamic/static interrupts, and provides detailed Python examples for four classic patterns—approval/rejection, state editing, tool‑call review, and input validation—plus advanced topics like parallel interrupts and MCP‑based tool integration.
This article details how we built an AI‑tool ecosystem by evolving a basic online JS cloud‑function platform into a unified, reusable capability layer that integrates with Flowise, LangChain StructuredTool, and the Model Context Protocol (MCP) to provide secure, cross‑platform tool calls for agents.
This article explains the MCP protocol for AI agents, detailing why a universal tool‑calling standard is needed, how it solves the M×N integration nightmare, the roles and execution stages involved, and demonstrates its use with Cherry Studio while highlighting current limitations.
Over recent months of extensive agent reinforcement‑learning experiments across search, data‑analysis, and multi‑source scenarios, the author shares twelve practical insights covering stability, environment‑reward‑algorithm priorities, tool‑call reliability, reward hacking pitfalls, evaluation alignment, and scaling tricks for larger models.
This comprehensive guide explores the concept of AI agents, detailing their definitions, classifications, and core interaction loops, then walks you through building a functional LLM‑driven travel assistant with step‑by‑step code, tool integration, and practical insights on agent versus workflow paradigms.
DeepAgent is a new end‑to‑end reasoning agent that unifies autonomous thinking, dynamic tool search, and execution, handling over 16,000 real APIs, supporting embodied environments and research assistance, and achieving state‑of‑the‑art results across multiple benchmarks through its unified reasoning core, memory‑folding mechanisms, structured memory, and the ToolPO training framework.
This article explains the concept of Claude Skills, walks through a Go library that parses skill packages, demonstrates a CLI inspector, shows how to run skills with Deepseek‑v3 via an OpenAI‑compatible API, and outlines future security enhancements.
This article walks through setting up MCP servers in both stdio and SSE modes, shows how to load their tools into LangChain agents using the new create_agent API, compares the two communication methods, and provides complete runnable Python examples.
This article explains the concept of AI agents, contrasting them with chatbots, detailing their ability and structural layers, summarizing academic surveys and whitepapers, and illustrating how agents plan, perceive, and act to autonomously accomplish user‑defined goals.
This article explains why modern LLM‑based applications need agent capabilities, introduces LangGraph’s core features such as stateful execution, graph‑based orchestration, tool integration, human‑in‑the‑loop and multi‑agent support, and provides a step‑by‑step Python example that builds a simple chat‑bot agent.
This article explores the rise of AI agents—from their definition as intelligent digital assistants powered by large language models to their evolution through planning, memory, and tool use—highlighting real‑world applications, core technical mechanisms, code implementations, and future trends such as autonomy, multimodal fusion, standardization, and safety considerations.
This article introduces the Model Context Protocol (MCP) and Large Language Models (LLM), explains their core concepts, transmission mechanisms, lifecycle, and essential modules, and provides step‑by‑step code examples for creating an MCP server, adding tools, resources, prompts, and debugging workflows to accelerate AI‑driven development.
This article explains the core concept of LangChain agents—combining an LLM, a set of tools, and a reasoning‑action loop—to enable dynamic decision‑making, tool invocation, and iterative observation for solving complex, multi‑step tasks.
This comprehensive AI agent development course covers 18 chapters, ranging from fundamental concepts and architecture to large‑model integration, tool and browser control, memory, RAG self‑learning, sandboxing, database manipulation, multi‑agent architectures, code assistance, and a real‑world frontend automation project, complete with source code and documentation.
This presentation explores how large language models can transcend their general knowledge limits by developing domain‑specific deep thinking abilities, addressing challenges such as complex instruction execution, expert reasoning gaps, and tool integration, and proposes reinforcement‑learning‑driven frameworks, structured thinking pipelines, and tool‑calling mechanisms to achieve rational intelligence.
This article provides a comprehensive overview of OpenManus, an open‑source, general‑purpose AI agent framework, covering its installation, configuration, core architecture—including BaseAgent, ReActAgent, ToolCallAgent, and Manus—its extensive tool collection, execution logs, and detailed code analysis for developers and AI researchers.
This article explains how function (tool) calling lets large language models like GPT or Gemini invoke external APIs, walks through defining tools with LangChain, and demonstrates a complete Python example that fetches real‑time weather data and returns a natural‑language answer.
This article explains how AutoDev adds support for Google’s Agent‑to‑Agent (A2A) protocol, detailing its architecture, integration with the Model Context Protocol (MCP), configuration steps, debugging tools, and the benefits of a modular, open‑source AI programming ecosystem.
This article explains the architecture, core components, and step‑by‑step implementation of a tool usage module that enables AI agents to standardize, select, execute, and transform external tools, illustrated with a sales data analysis case and detailed code snippets.
This tutorial walks you through why LangGraph outperforms LangChain by showing how to build a basic chatbot, integrate tool calls, and add both short‑term and long‑term memory, complete with code examples and visualizations for creating flexible, state‑driven AI agents.
