AI Agent Development Fundamentals

Overview

The AI Agent Development Fundamentals course is designed for developers, engineers, and technical product builders who are new to Generative AI but already have intermediate machine learning knowledge, basic Python proficiency, and familiarity with development environments such as VS Code, and who want to engineer, customize, and deploy open generative AI solutions while avoiding vendor lock-in. The course introduces learners to the core design patterns and practical skills required to build autonomous AI agents. Learners begin by studying the architectural foundations of agent systems, including perception, reasoning, and action loops, as well as the differences between reactive, deliberative, and hybrid agent types. The course then focuses on building simple reactive agents, where learners apply structured prompting, decision-making frameworks, and natural language understanding to implement predictable and testable behaviors. In the final module, learners extend their agents with tool-use and memory management capabilities, using function-calling patterns, conversation history maintenance, and context window optimization. Practical exercises emphasize building agents with resilience through error handling and recovery strategies. By the end of the course, learners will have created functional agents capable of integrating tools, maintaining memory, and performing autonomous tasks.

Syllabus

Agent Architecture Fundamentals

Learn the key components that make agents work, perception, reasoning, action selection, and execution loops. You’ll compare reactive, deliberative, and hybrid designs, and see how prompt templates and state management enable multi-turn interactions. By the end, you’ll know how different agent types function, when to use each, and how they provide value in real-world scenarios.

Building Simple Reactive Agents

You'll build and test simple reactive agents that respond predictably using structured prompts and rule-based decision logic. You'll implement input parsing, apply deterministic behavior patterns through severity classification and action-mapping frameworks, and design clear output formatting strategies. Through validation frameworks, reasoning traces, and structured debugging, you'll evaluate how consistent your agent's behavior is across different scenarios. By the end, you'll know how to create reliable, production-ready reactive agents and understand why structured behavior is the foundation for more advanced systems with tools and memory.

Tool Use and Memory Patterns

You’ll extend agents with tools and memory so they can recall context and perform real tasks. You’ll implement tool-calling patterns, design short-term and long-term memory strategies, and test how agents handle conversation history. These capabilities transform basic models into production-ready agents that adapt to users, integrate with systems, and deliver consistent value over time.