agentic-artificial-intelligence
PhD Course: Agentic Artificial Intelligence - LLM Agents, RAG, Multi-Agent Systems
Publications
ReAct: Synergizing Reasoning and Acting in Language Models
While large language models (LLMs) have demonstrated impressive capabilities across tasks in language understanding and interactive decision making, their abilities for reasoning (e.g. chain-of-thought prompting) and acting (e.g. action plan generation) have primarily been studied as separate topics...
| Property | Value |
|---|---|
| arXiv | 2210.03629 |
| Year | 2022 |
Authors: Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, Yuan Cao
Information
| Property | Value |
|---|---|
| Language | Python |
| Stars | 0 |
| Forks | 0 |
| Watchers | 0 |
| Open Issues | 1 |
| License | No License |
| Created | 2025-12-24 |
| Last Updated | 2026-02-19 |
| Last Push | 2026-01-04 |
| Contributors | 1 |
| Default Branch | main |
| Visibility | private |
Notebooks
This repository contains 14 notebook(s):
| Notebook | Language | Type |
|---|---|---|
| L01_first_agent | PYTHON | jupyter |
| L02_prompting_strategies | PYTHON | jupyter |
| L03_function_calling_comparison | PYTHON | jupyter |
| L03_mcp_tool_implementation | PYTHON | jupyter |
| L04_reflexion_implementation | PYTHON | jupyter |
| L05_coordination_demo | PYTHON | jupyter |
| L05_message_passing | PYTHON | jupyter |
| L06_LangGraph_Agent | PYTHON | jupyter |
| L07_Self_RAG | PYTHON | jupyter |
| L08_GraphRAG | PYTHON | jupyter |
| L09_Verification | PYTHON | jupyter |
| L10_Benchmarking | PYTHON | jupyter |
| L11_Code_Agent | PYTHON | jupyter |
| L12_Generative_Agents | PYTHON | jupyter |
Datasets
This repository includes 13 dataset(s):
| Dataset | Format | Size |
|---|---|---|
| .lighthouserc.json | .json | 0.63 KB |
| datasets | | 0.0 KB |
| .gitkeep | | 0.0 KB |
| glossary_inventory.json | .json | 30.6 KB |
| term_index.json | .json | 19.18 KB |
| charts.json | .json | 4.45 KB |
| course.json | .json | 2.36 KB |
| exercises.json | .json | 4.25 KB |
| glossary.json | .json | 0.31 KB |
| quizzes.json | .json | 0.44 KB |
| weeks.json | .json | 0.6 KB |
| week_fixes.json | .json | 4.6 KB |
| quality_report.json | .json | 2.66 KB |
Reproducibility
This repository includes reproducibility tools:
- Python requirements.txt
Status
- Issues: Enabled
- Wiki: Enabled
- Pages: Enabled
README
Agentic Artificial Intelligence
PhD-level course on LLM-based autonomous agents, multi-agent systems, and advanced RAG architectures.
Course Overview
This 12-week course covers the theory and practice of building intelligent agents powered by large language models. Students will learn to design, implement, and evaluate autonomous systems that can reason, plan, and act in complex environments.
Prerequisites
- Machine Learning fundamentals
- Python proficiency (3.10+)
- Experience with LLM APIs (OpenAI, Anthropic)
- Basic knowledge of transformers and attention mechanisms
Course Structure
| Module | Weeks | Topics |
|---|---|---|
| Foundations | 1-2 | Agent definition, ReAct, LLM prompting, CoT/ToT |
| Single-Agent Systems | 3-4 | Tool use (MCP), planning, memory, Reflexion |
| Multi-Agent Systems | 5-6 | Architectures, LangGraph, AutoGen, CrewAI |
| RAG & Knowledge | 7-8 | Advanced RAG, GraphRAG, knowledge graphs |
| Safety & Evaluation | 9-10 | Hallucination prevention, benchmarks, red-teaming |
| Applications | 11-12 | Domain agents, research frontiers, projects |
Getting Started
- Clone the repository
- Install dependencies:
pip install -r requirements.txt - Set up API keys (OpenAI, Anthropic, etc.)
- Navigate to lesson folders for materials
Repository Structure
agentic-artificial-intelligence/
|-- L01_Introduction_Agentic_AI/
| |-- L01_Introduction_Agentic_AI.tex
| |-- 01_agent_definition/
| | |-- chart.py
| | |-- chart.pdf
| |-- notebooks/
| |-- exercises/
| |-- readings/
|-- L02_LLM_Foundations_Agents/
|-- ...
|-- L12_Research_Frontiers/
|-- tools/
| |-- hallucination_checks/
| |-- quality/
|-- datasets/
|-- SYLLABUS.md
|-- PROGRESS_TRACKER.md
|-- requirements.txt
Tools & Frameworks
- LangGraph: State machine-based agent framework
- AutoGen: Conversation-based multi-agent
- CrewAI: Role-based team agents
- MCP: Model Context Protocol for tool use
Evaluation
| Component | Weight |
|---|---|
| Weekly Exercises | 30% |
| Midterm Project (Week 6) | 20% |
| Paper Presentation | 15% |
| Final Project | 35% |
Resources
Contributing
See SYLLABUS.md for course schedule and PROGRESS_TRACKER.md for development status.
License
Educational use only. Contact instructor for permissions.