Agentic AI — Course

lecture + exercises + lab + tutorials

Agentic AI systems combine language models with tools, memory, and autonomous loops. This course builds them from first principles — foregrounding the mathematics and algorithms / data structures — before any framework.

Prerequisites: Mathematics (probability and linear algebra), plus prior courses in data science and machine learning.

Contents (preliminary syllabus)

Status: Alpha - Work in progress

Part I — Foundations

Lecture Topic Slides Exercises Lab
1 L-Intro Introduction to Agents & the Training Ladder lecture exercises lab
2 L-ReAct The ReAct Loop & Tool Use lecture exercises lab

Part II — Reasoning & Planning

Lecture Topic Slides Exercises Lab
3 L-Decomp Task Decomposition & Planning lecture
4 L-ToT Tree-of-Thoughts & Search lecture

Part III — Model & Data

Lecture Topic Slides Exercises Lab
5 L-Att Inside the Model: Tokenization, Attention & Context Window lecture
6 L-Embed Embeddings & Vector Arithmetic lecture
7 L-RAG Vector Databases & (Agentic) RAG lecture
8 L-Memory Sessions, Memory & Compaction lecture

Part IV — Evaluation & Systems

Lecture Topic Slides Exercises Lab
9 L-Orch State Machines & Workflow Orchestration lecture
10 L-Eval Observability & Evaluation
11 L-MAS Multi-Agent Systems: Organization & Coordination

Part V — Safety & Wrap-up

Lecture Topic Slides Exercises Lab
12 L-Gateway Gateway, Security & Guardrails
13 L-Practice Agents in Practice (Production)
14 L-Outlook Outlook & Ethics

Extended Course (optional)

Lecture Topic Slides Exercises Lab
15 L-MDP Agents as Markov Decision Processes lecture
16 L-Game Game Theory for Multi-Agent Systems
17 L-Collective Collective Decisions & Agent Populations

Fundamentals

A fundamentals notebook recaps the mathematics the lectures reuse from earlier courses. It carries no exercises; the exercises on the material are part of the lectures that use it.

Topic Content Used in Notes
Probability sample space, random variables, joint, marginal and conditional distributions, conditional independence L-Intro, L-Att notes
Statistics standard error of a success rate, 95 % interval, Wald test for two success rates L-Eval notes
Linear Algebra vectors, dot product, norms, angle between two vectors L-Embed, L-RAG notes

Tutorials

A tutorial is a self-contained jupyter notebook.

Tutorial Content Requires Notebook
Building a SWE Agent from Scratch a software-engineering agent in plain Python — model call, action parsing, a sandboxed shell and the loop — extended step by step into mini-swe-agent Ollama, podman notebook

Literature & further reading

Agentic patterns & engineering

  • Gulli, A. (2025). Agentic Design Patterns. Springer.
  • Lakshmanan, V.; Hapke, H. (2025). Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications. O'Reilly.
  • Huyen, C. (2025). AI Engineering: Building Applications with Foundation Models. O'Reilly.
  • Albada, M. (2025). Building Applications with AI Agents: Designing and Implementing Multiagent Systems. O'Reilly — German ed.: KI-Agenten entwickeln: Entwurf, Implementierung, Monitoring (transl. F. Langenau, dpunkt.verlag, 2026).
  • Ryan R. (2026), The Agentic AI book: From Language Models to Intelligent Agents

Multi-agent, protocols & MCP

  • Dibia, V. (2025). Designing Multi-Agent Systems: Principles, Patterns, and Implementation for AI Agents. Self-published — multiagentbook.com.
  • Lanham, M. (2026). AI Agents in Action (2nd ed.). Manning — MCP & A2A protocols.
  • Shoham, Y.; Leyton-Brown, K. (2009). Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. Cambridge University Press — the standard MAS textbook; full text free at masfoundations.org (L-MAS, L-Game).
  • Arsanjani, A.; Bustos, J. P. (2026). Agentic Architectural Patterns for Building Multi-Agent Systems. Packt — agentic patterns, RAG, LLMOps, enterprise-scale systems (L-RAG, L-MAS, L-Practice).

Foundations & "inside the LLM"

  • Russell, S.; Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson — classical-AI foundations: intelligent agents, search ($A^*$, L-Decomp, L-ToT), sequential decisions / MDPs (L-MDP), and Multiagent Decision Making (Ch. 18; Ch. 17 in the Global Edition — game theory; L-Game).
  • Sutton, R. S.; Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press — finite MDPs, returns & value functions, Bellman equations (L-MDP).
  • Raschka, S. (2024). Build a Large Language Model (From Scratch). Manning — attention, tokenization, KV-cache (L-Att).
  • Alammar, J.; Grootendorst, M. (2024). Hands-On Large Language Models: Language Understanding and Generation. O'Reilly — vector spaces & embeddings (L-Embed).

Frameworks & agentic software engineering

  • Oshin, M.; Campos, N. (2025). Learning LangChain: Building AI and LLM Applications with LangChain and LangGraph. O'Reilly — graph-based state machines (L-Orch).
  • Hassan, A. E., et al. (2025). Agentic Software Engineering: Foundational Pillars and a Research Roadmap. arXiv:2509.06216 — the SASE framework (capstone, L-ASE).
  • Hassan, A. E., et al. (2026). Agentic Software Engineering (book) — companion to the SASE paper (capstone, L-ASE).