Course overview
Build agents. Understand the architecture. Engineer for production.
Building an AI agent is easy.
Building one that is reliable, observable, secure, testable, and capable of running in production is a much harder engineering problem.
This course teaches Agent Engineering from first principles. You will start by understanding how Large Language Models work as components of software systems, then progressively learn how to give models access to tools, manage context and state, build agentic workflows, introduce memory and retrieval, evaluate behavior, and operate agents reliably in production.
The goal is not to teach you how to call a framework API and label the result an agent.
You will learn what happens underneath the abstractions, why different agent architectures work, where they fail, and how to make sound engineering decisions when building real-world AI systems.
Java is used throughout the course for implementation. We will use frameworks and technologies such as LangChain4j where they provide useful abstractions, while keeping the underlying concepts framework-independent.
What You Will Learn
By the end of this course, you will understand how to:
- Understand LLMs as components of software systems
- Work with tokens, context windows, messages, prompts, and structured outputs
- Connect LLMs to external systems through tool calling
- Understand and implement the core agent execution loop
- Design effective context for agentic systems
- Build reliable boundaries around probabilistic model behavior
- Build agents and stateful agentic workflows
- Manage conversation state, memory, and persistence
- Use embeddings, retrieval, and RAG effectively
- Design single-agent and multi-agent architectures
- Test and evaluate nondeterministic AI systems
- Trace, debug, monitor, and observe agent behavior
- Design for latency, cost, reliability, and scalability
- Protect agent systems against prompt injection and unsafe tool execution
- Deploy and operate agent-powered applications in production
- Make informed architectural trade-offs instead of blindly following framework patterns
Who Is This Course For?
This course is designed for software engineers who want to move beyond basic LLM integrations and understand how production-grade agentic systems are actually engineered.
It is particularly useful for:
- Backend engineers moving into AI and agent engineering
- Software engineers building LLM-powered products
- Engineers who want to understand agents beyond framework abstractions
- Developers preparing to work with LangChain, LangGraph, LangChain4j, or similar agent frameworks
- Engineers interested in the architecture and operation of production AI systems
Prerequisites
This is not a programming course.
You should already be comfortable writing software in Java and understand general software engineering concepts such as APIs, data structures, databases, HTTP, and application development.
No previous experience with AI, machine learning, Large Language Models, LangChain, or agent engineering is required.
We will build the necessary AI and LLM foundations from the ground up.
How This Course Approaches Agent Engineering
The course follows one principle:
Understand the mechanism before depending on the abstraction.
When we study tool calling, you will first understand what actually happens between the model and your application.
When we study agents, you will understand the execution loop before relying on an agent framework.
When we study memory and retrieval, you will understand the underlying primitives before introducing higher-level abstractions.
When we study production systems, we will treat the LLM as one component inside a larger distributed software system rather than treating AI as magic.
Frameworks will change.
The engineering principles behind reliable agent systems will last much longer.
What You Will Build
Throughout the course, you will progressively build increasingly capable LLM-powered systems rather than completing disconnected toy examples.
You will move from direct model interaction to tool-using systems, stateful agents, retrieval-augmented applications, observable and evaluated workflows, and eventually a production-oriented agent system that brings the concepts from the course together.
By the end, you should not only be able to build an agent.
You should be able to explain why it works, where it can fail, how to measure it, and how you would operate it in production.