Learn Agentic AI
18 guides for developers building with AI agents
Free, in-depth guides on AI agents, frameworks, MCP servers, and production patterns. No fluff — written by developers, for developers.
Featured Guides
How to Build an AI Agent: Step-by-Step Guide (2026)
A practical, step-by-step guide to building your first AI agent. Covers choosing a framework, adding tools via MCP, memory, orchestration, and deployment.
Claude Code vs Cursor vs GitHub Copilot: Which AI Coding Tool in 2026?
An in-depth comparison of the three leading AI coding tools: Claude Code (CLI-first agent), Cursor (AI IDE), and GitHub Copilot (inline completion). Features, pricing, and when to use each.
AI Coding Agents Compared: Claude Code vs Cursor vs Devin vs Copilot
A head-to-head comparison of the leading AI coding agents in 2026. Covers features, pricing, strengths, and which is best for different development workflows.
The Agentic AI Landscape: State of the Ecosystem in 2026
A comprehensive overview of the agentic AI ecosystem in 2026 — market trends, key players, adoption patterns, and where the industry is headed.
AI Agent Design Patterns for Production Systems
Battle-tested patterns for building reliable AI agents: ReAct loops, plan-and-execute, human-in-the-loop, error recovery, and guardrails for production deployment.
Multi-Agent Systems: Patterns and Best Practices for 2026
Learn how to design systems where multiple AI agents collaborate on complex tasks. Covers coordinator patterns, parallel research, debate, and pipeline architectures.
Understanding the Model Context Protocol (MCP): The USB-C of AI
A comprehensive guide to MCP — the open protocol that standardizes how AI agents connect to tools, data sources, and APIs. Learn how it works and how to use it.
What Are AI Agents? A Developer's Guide to Autonomous AI Systems
Understand what AI agents are, how they differ from simple chatbots, and why they're reshaping software development. Covers agent architectures, tool use, and real-world applications.
Fundamentals
How to Build an AI Agent: Step-by-Step Guide (2026)
A practical, step-by-step guide to building your first AI agent. Covers choosing a framework, adding tools via MCP, memory, orchestration, and deployment.
Awesome LLM Apps: The Ultimate Collection of AI Agent Examples
A deep dive into the 100K-star awesome-llm-apps repository — the largest open-source collection of LLM application examples covering AI agents, RAG pipelines, multi-agent teams, voice AI, and MCP integrations.
Prompt Engineering for AI Agents: Beyond Simple Instructions
Master the art of writing system prompts, tool descriptions, and CLAUDE.md files that make AI agents effective. Covers role definition, guardrails, and common mistakes.
What Are AI Agents? A Developer's Guide to Autonomous AI Systems
Understand what AI agents are, how they differ from simple chatbots, and why they're reshaping software development. Covers agent architectures, tool use, and real-world applications.
How to Evaluate AI Agents: Benchmarks, Metrics, and Real-World Testing
A practical guide to evaluating AI agents before adopting them. Covers SWE-bench, HumanEval, cost-per-task metrics, and how to run your own agent evaluations.
Frameworks
MCP Protocol
How to Set Up MCP Servers: Quick Start Guide for Claude Code & Cursor
A practical, copy-paste guide to installing and configuring MCP servers in Claude Code, Cursor, and other AI coding tools. Covers the top 5 servers every developer should install.
Building Your First MCP Server: A Step-by-Step Tutorial
Learn how to build a Model Context Protocol server from scratch. Covers TypeScript and Python SDKs, tool definitions, transport setup, and testing with Claude Code.
Understanding the Model Context Protocol (MCP): The USB-C of AI
A comprehensive guide to MCP — the open protocol that standardizes how AI agents connect to tools, data sources, and APIs. Learn how it works and how to use it.
Design Patterns
19 RAG Patterns You Should Know: From Basic Chains to Autonomous RAG
A comprehensive guide to Retrieval-Augmented Generation patterns from the awesome-llm-apps collection — covering basic RAG, corrective RAG, autonomous RAG, knowledge graph RAG, and production deployment patterns.
13 Multi-Agent Team Patterns: From Finance to Full-Stack Coding
How to build multi-agent teams that collaborate on complex tasks — patterns and implementations from the awesome-llm-apps collection covering finance, legal, recruitment, competitive intelligence, and software development.
AI Agent Design Patterns for Production Systems
Battle-tested patterns for building reliable AI agents: ReAct loops, plan-and-execute, human-in-the-loop, error recovery, and guardrails for production deployment.
Multi-Agent Systems: Patterns and Best Practices for 2026
Learn how to design systems where multiple AI agents collaborate on complex tasks. Covers coordinator patterns, parallel research, debate, and pipeline architectures.
Tools & Workflows
Claude Code vs Cursor vs GitHub Copilot: Which AI Coding Tool in 2026?
An in-depth comparison of the three leading AI coding tools: Claude Code (CLI-first agent), Cursor (AI IDE), and GitHub Copilot (inline completion). Features, pricing, and when to use each.
AI Coding Agents Compared: Claude Code vs Cursor vs Devin vs Copilot
A head-to-head comparison of the leading AI coding agents in 2026. Covers features, pricing, strengths, and which is best for different development workflows.
Observability and Debugging for AI Agents
Learn how to monitor, debug, and optimize AI agents in production. Covers tracing, cost tracking, evaluation, and the best observability tools in the ecosystem.
Industry & Trends
The Agentic AI Landscape: State of the Ecosystem in 2026
A comprehensive overview of the agentic AI ecosystem in 2026 — market trends, key players, adoption patterns, and where the industry is headed.
Open Source vs Commercial AI Agents: Making the Right Choice
A practical guide to choosing between open-source and commercial AI agents. Covers cost, control, customization, support, and when each option makes sense.
About This Learning Hub
DeepYard's learning hub covers the full spectrum of agentic AI — from fundamentals like understanding what AI agents are and how they work, to advanced topics like multi-agent orchestration, MCP server development, and production deployment patterns. All content is free, regularly updated, and focused on practical knowledge that developers can immediately apply.