ai-agents

Guides to building AI agents in Python that can reason, use tools, and complete tasks.

RAG System Design: The LLM Interview Whiteboard Round

RAG system design, short for Retrieval-Augmented Generation, is the GenAI whiteboard round where an interviewer says “design ChatPDF for an enterprise” and then watches how you think for forty-five minutes, not what you can draw. This post walks that round end to end: an eight-layer framework, two fully worked designs, the follow-up gauntlet on ACLs, freshness, and scale, and a mock transcript with the rubric, every number computed with real code. “They grade the numbers you defend, not the boxes ... Read More

AI Design Patterns: Routers, Fallbacks, Caches, and Agents

At 2am, with a provider down and an agent looping, nobody invents a good fix from scratch. AI design patterns are the reusable answers to the failures every production AI system eventually hits: dead providers, ballooning bills, models that quietly rot while dashboards stay green. This catalog puts them side by side, each with the problem it solves, the structure, a runnable sketch, and when not to use it. “If the implementation is hard to explain, it’s a bad idea.” ... Read More

AI Agent Project: From Raw Loop to LangGraph, With Evals

Agent frameworks rotate every few months; the design underneath them does not. This AI agent project is the capstone: one research-and-summarize assistant with four tools, built three ways. A raw loop written by hand, the same tools served from your own MCP server, and a LangGraph 1.x port with checkpoints and a human approval step. All three get graded on the same 20 tasks, with the results table in the README. “Make it work, make it right, make it fast. ... Read More