Material Detail

Complete teaching materials on Agentic AI, suitable for advanced undergraduate or introductory graduate coursesin Computer Science or Computer Engineering. Available in English and Spanish.

The package includes a teaching guide, 15 weekly lecture notes, 8 hands-on lab assignments with Python startercode, 2 coursework assignments, a final project specification, a sample midterm exam with answer key, gradingrubrics, a reading list, a technical setup guide, a glossary of 50+ key terms, 27 architectural diagrams, and 8Python skeleton projects.

Topics cover: agent architectures (ReAct, Reflexion, LATS, Plan-and-Execute), tool use and the Model ContextProtocol (MCP), memory systems, retrieval-augmented generation (RAG), multi-agent systems with moderncommunication protocols (Google A2A, IBM ACP), agent evaluation and benchmarking, safety/alignment/guardrails,human-agent interaction and computer use, agentic software engineering with modern coding agents and frameworks(Claude Code, Cursor, OpenAI Agents SDK, LangGraph, CrewAI), and AI governance under the EU AI Act.

All code examples use Python with OpenAI and Anthropic APIs. Prerequisites: Machine Learning and SoftwareEngineering.

This will delete the comment from the database. This operation is not reversible. Are you sure you want to do it?

Thank you for reporting a broken "Go to Material" link in MERLOT to help us maintain a collection of valuable learning materials.

If you feel this material is inappropriate for the MERLOT Collection, please click SEND REPORT, and the MERLOT Team will investigate. Thank you!

You are being taken to the material on another site. This will open a new window.

Do not show me this again