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Basic Information

This repository is a six‚Äëweek hands‚Äëon course called Master AI Agentic Engineering designed to teach practitioners how to code and deploy autonomous AI agents. The materials focus on practical agent development using tools and frameworks named in the README, including the OpenAI Agents SDK, CrewAI, LangGraph, AutoGen and MCP. The repo aggregates step‚Äëby‚Äëstep setup instructions for Windows, Mac and Linux, guided notebooks and example projects, week‚Äëby‚Äëweek notes (notably special guidance for CrewAI in week three), troubleshooting resources and course videos. It is aimed at learners who want a structured, project‚Äëbased path from installation through building and running multi‚Äëcomponent agent projects while paying attention to API usage and platform‚Äëspecific issues. The content is educational and action‚Äëoriented rather than a single runnable product.

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1691
App URL
https://github.com/ed-donner/agents
Github Repository
https://github.com/ed-donner/agents/blob/main/README.md

App Details

Features
The repository includes a structured six‚Äëweek curriculum with guided notebooks, example projects, and troubleshooting material. Platform‚Äëspecific setup instructions are provided for Windows, Mac and Linux, plus practical notes for CrewAI week three such as required build tools and commands to install or upgrade Crew tooling. The course lists supported agent toolchains and libraries used in lessons, and it documents operational details like environment variable requirements and common gotchas. There are references to course resources, video material, and a troubleshooting notebook to diagnose issues. The README also calls out cost considerations for API usage and points to lower‚Äëcost or local model alternatives. The repo is organized for stepwise learning with reproducible exercises and instructor guidance.
Use Cases
The project helps learners and developers gain practical experience building autonomous agents by combining theory, example code and runnable exercises. It reduces onboarding friction with clear, OS‚Äëspecific setup guides and explicit notes about common errors and fixes, including commands and environment variable tips for specific tools. The curriculum promotes hands‚Äëon practice through week‚Äëby‚Äëweek projects and sample CrewAI runs, and it highlights operational concerns such as API costs and alternatives to expensive model calls. Supplementary resources like guides, notebooks and troubleshooting material make troubleshooting and replication easier. The course also encourages community connection and instructor support to accelerate learning and resolve issues while developing agentic systems.

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