teaching_llm_agents
LLM Agents
Course Overview
This course introduces students to LLMs, agents and multi-agent systems.
- practical development of intelligent agents and applications powered by Large Language Models (LLMs).
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Students will explore how LLMs can be orchestrated and integrated with external tools, APIs, and structured memory systems
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enable decision-making, problem-solving, and autonomous interaction across complex software engineering tasks.
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A variety of application areas will be considered, for example including the use of LLM-based technologies to develop assistive systems. Application areas will include medical diagnosis, or to streamline resource allocation, hazard analysis and decision-making.
- Core agent architectures, and retrieval-augmented agents will be examined through applied coding labs and project-based learning
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students will gain hands-on experience with agentic frameworks and prompt-augmentation techniques to build agents for a variety of purposes: searching the web, querying databases, controlling devices, composing workflows etc.
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Emphasis will be placed on the integration of LLMs in software engineering processes and practices, and on how to document this usage.
- Taking a systems-centric view, students will learn the importance of understanding and bounding the behaviour of LLM-based agents in the wider system context
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how to evaluate and monitor agents’ behaviours, and how to use guardrails to frame their autonomy where necessary.
- Ethical and safety considerations, including agent alignment, hallucination control, and failure recovery, will also be addressed to ensure responsible deployment in real-world contexts.
Course Materials
Course content and materials can be found in the following files:
- Resources
- Installation guide
- Skills
- Tools
- Agents
- MCP
- How to use coding agents by Andrew Ng
- Agents in LangGraph
- Computer use
- Harness
- Guardrails
- Safety
- Evals
- Open source models and Ollama
- Rapid prototyping
- Agentic hackathon
- Projects, exams and quizzes
Resources
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Course Website: https://neelsoumya.github.io/teaching_llm_agents/
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https://pragprog.com/titles/jwpaieng/a-common-sense-guide-to-ai-engineering/
Learning Objectives
By the end of this course, students will be able to:
- Create multi-agent systems
Course Structure
Required Materials
- Setup instructions
- Installation guide
- Google Colab
Acknowledgements
Generative AI was used to generate content for this course. All content was vetted and verified by the author.
Support
- Office Hours: [TBD]
- Discussion Forum: [TBD]
- Teaching Assistants: [TBD]