A field manual for building agents that survive contact with production

What it does This is a structured learning repository — six chapters covering GenAI foundations, framework tutorials, agentic system design, use cases, resources, and even interview prep. It’s less a library you import and more a well-organized notebook from someone who has actually built agent systems, with code examples alongside the theory.

The interesting bit The system design chapter is the real payload: design patterns, architecture layers, RAG variants (basic through agentic and multi-modal), evaluation frameworks, and a 2026 update covering harness engineering, AgentOps, context engineering, and MCP/A2A. That last document is the kind of forward-looking synthesis you rarely find bundled with beginner tutorials.

Key highlights

• Agentic AI design patterns doc — reusable patterns rather than framework-specific recipes
• RAG deep dive spanning basic, agentic, multi-modal, and advanced architectures
• Evaluation guide covering layers, dimensions, methods, and frameworks
• Code examples for OpenAI Agents SDK, LangGraph, LangChain, CrewAI, and LlamaIndex
• Interview prep chapter with system design frameworks and practice templates

Caveats

• It’s a personal knowledge base, not a maintained framework — expect uneven depth across chapters and “more coming soon” sections
• “Production-ready, tested examples” is claimed but the README doesn’t show evidence of testing or deployment guides in practice

Verdict Worth a bookmark if you’re moving from “I’ve called an LLM API” to designing multi-agent systems and want one place with patterns, architectures, and evals. Skip it if you want a pip-installable tool — this is reading material, not software.