Offered by: University of California, Davis via Coursera
Learner/Practitioner: Noor Uddin (noor.cs2@yahoo.com)
License: © 2025 Coursera & University of California, Davis
Purpose: Personal learning notes and summaries compiled by Noor Uddin
This repository contains structured notes, key takeaways, and practical insights from the Coursera course "AI Agents: From Prompts to Multi-Agent Systems". The course explores the evolution of AI agents from basic prompt-based interactions to complex, multi-agent systems. It is designed for learners interested in understanding how large language models (LLMs) and agent frameworks are shaping the next generation of AI applications.
Disclaimer: All content is based on the course provided via Coursera. This repository is created for educational purposes only and does not claim ownership over the original material.
(Contains Tasks 1–8: Customizing, Frameworks, Role-Based, Chain, Meta, Receiver, Style, Reasoning Prompting)
See module1_prompt_engineering.md →
This module focuses on embedding context into AI agent workflows using APIs, context windows, Retrieval-Augmented Generation (RAGs), autonomy, and structured workflows.
- AI Agent Workflows
- Systematic Review of AI Agents
- Agentic Function Calls (API, Context Windows, RAGs, Search)
- Interactive Lab: Function Call Power
- Building Effective Agents
- Algorithms and Workflows
- Agentic Workflows (Parts 1–3)
- Agents Autonomy
This module explores multi-agent systems, inter-agent communication, adaptive intelligence, and emergent behavior.
- Introduction to Multi-Agent Systems
- Multi-Agent Systems
- Connected AI Agents (Parallelization, Exponential Challenges)
- Interdependent AI Agents
- Adaptive AI Agents (Parts 1–3)
- Optional: Diversity, Ability, Path-Dependence, Requisite Variety
- Emergent AI Agents Systems