#MCP Agent testing
480 related articles
·4 min
7 Core Components for Designing Long-Running AI Agents
A deep dive into the 7 core components for building long-running AI Agents: Goal, Evaluator, Verifier, Loop, Orchestration, Observability, and Memory.
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·4 min
Claude Code Getting Started: Environment Setup, Core Commands, and Git Workflow Automation
A practical guide to Claude Code covering installation, Chinese LLM switching, project analysis, key commands, and conversational Git workflow automation for developers.
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·3 min
Claude Code Practical Guide: Environment Setup, Git Workflows, and Engineering in Production
A complete guide to Claude Code: environment setup, switching to domestic LLMs, CLI commands, Git workflows, MCP, Subagents, and enterprise project walkthroughs.
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·2 min
Vibe Coding in Practice: From Idea to Real Product — No Coding Required
Vibe Coding is an AI-driven development paradigm that requires no coding skills. Use natural language to guide AI through design, development, and testing. Learn the 4-step workflow using Gemini, Figma Make, and Claude Code.
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·4 min
AI-Powered Automated Testing: Using Pytest + MCP to Tackle Non-Determinism
Deep dive into AI-era automated testing: using Pytest + Playwright + MCP for stable automation, constraining code conventions with Skills, avoiding non-determinism and high token costs. Includes real debugging war stories.
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·2 min
The Complete Guide to Agent Skills: Andrew Ng × Anthropic Joint Course Breakdown
Andrew Ng's DeepLearning.ai teams up with Anthropic to teach Agent Skills: file structure, progressive disclosure, MCP integration, and the full path from Claude.ai to the Agent SDK.
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·2 min
OpenSpiel 2.0 Released: Reinforcement Learning Framework Fully Embraces Large Language Models
OpenSpiel 2.0 by Google DeepMind adds LLM fine-tuning examples, MCP tool server, JSON trajectories, AlphaZero on JAX, 19 new games, and Windows support.
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·3 min
Andrew Ng × Anthropic: A Practical Guide to Claude Code and Its Core Methodology
Andrew Ng and Anthropic's Claude Code course covers RAG development, data analysis, and Figma-to-frontend projects, with deep dives into context management, MCP tools, and CLAUDE.md architecture.
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·7 min
Claude Code Practical Tutorial: A Complete Beginner's Guide to Automating Your Entire Workflow
No coding required! This guide breaks down the complete Claude workflow: custom Projects, batch SEO content, one-sentence tool building with Artifacts, and Claude Code terminal ops—with real traffic-growth cases.
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·3 min
Automating Chrome Extension Development with AI Coding Agents: Antigravity & DevTools MCP Complete Workflow
A complete workflow from Google I/O: use Antigravity, Modern Web Guidance, and Chrome DevTools MCP to build Chrome extensions automatically — from prompt to publish.
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·4 min
WebMCP in Practice: A Complete Guide to Making Your Website AI Agent-Ready
A deep dive into the WebMCP proposal: how AI agents read websites, how to expose structured tools via imperative and declarative APIs, and how to audit agentic readiness with Lighthouse.
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·3 min
Perplexity Max Deep Dive: Is the $200/Month Plan Worth It? The Truth About Credit Consumption
A Reddit user's real experience with Perplexity Max ($200/month): 15,000 credits burned on one task, failed Grok integration, and complex MCP setup. Is it worth it?
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·2 min
Chrome DevTools for Agents: The Closed-Loop Debugging Powerhouse for AI Coding Agents
Chrome DevTools for Agents, unveiled at Google I/O, gives AI coding agents a closed feedback loop with Lighthouse audits, user input simulation, and device emulation.
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·4 min
Using MCP Protocol to Give AI Agents Full Visibility into SQL Server
Learn how to connect AI agents to SQL Server via MCP protocol — wrapping DMV queries, managing multi-instance fleets, and enabling natural language ops with layered safety guardrails. Includes Docker deployment.
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·2 min
Claude Code for QA Engineers: A Practical Handbook from Basics to Engineering-Level Implementation
A practical Claude Code handbook built for QA engineers — covering environment setup, spec-driven development, Skill encapsulation, MCP integration, and AI test sub-agents across 10 core modules.
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·3 min
OpenClaw Multi-Agent AI Programming Workflow in Practice: From Coder to Manager
Deep dive into OpenClaw multi-agent AI programming workflows: context layering, CMUX parallel terminal management, work trees, and manager-perspective debiasing for scalable AI dev automation.
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·3 min
GPT-5.6 Real-World Test: The True Capability Limits of AI-Assisted Game Development
A creator tests GPT-5.6 Codex on two real projects: an Android MAUI app and a tower defense game prototype. Zero code written for the app; the game demo surfaces multiple bugs. Honest look at AI coding's real limits.
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·3 min
Pi Agent Review: A Minimalist Coding Agent That's Lightweight, Efficient, and Highly Customizable
Pi Agent hands-on review: ~1,200 token overhead, no built-in system prompts, supports Codex/Grok and more. Compared to Claude Code and OpenCode, Pi Agent wins with minimalist design and full customizability.
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·3 min
Cosmonapse: A Decentralized Multi-Agent Framework Built on an Event Bus
Cosmonapse is an open-source multi-agent framework that replaces central orchestrators with peer nodes, using typed signals and an event bus to fully decouple tool calls, memory, and HITL. Supports Python and TypeScript under Apache 2.0.
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·3 min
Coze Deep Dive: Multi-Agent Collaboration Platform Features & Usage Guide
A comprehensive guide to Coze by ByteDance: multi-agent collaboration, local tool integration, cross-platform sync, and credit system. Compare with Dify to get started fast.
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