Understand Agentic AI

What you'll learn
Master the Foundations of Agentic AI
Implement the ReAct Framework
Design Advanced Memory and Safety Systems
Orchestrate Multi-Agent Systems (MAS)
Requirements
To succeed in this course, you must have a solid foundation in Python programming-specifically with classes, decorators, and dictionaries-as these are essential for building the tool registries and agent objects discussed. You should also possess basic AI literacy, including an understanding of how Large Language Models operate and experience interacting with APIs to handle JSON data. Additionally, you will need a development environment like VS Code or Jupyter Notebooks, along with access to an LLM provider (such as OpenAI, Anthropic, or Google Gemini) to execute the reasoning loops and multi-agent workflows. While the course moves into advanced architectures like the ReAct framework and hierarchical orchestration, it is designed for any developer who can break down complex problems into logical steps and is ready to transition from basic prompting to building autonomous AI agencies.
Description
This course provides a technical deep dive into the architecture of autonomous, multi-agent AI systems using the G.A.M.E. (Goals, Actions, Memory, Environment) and P.A.R.A. frameworks. You will move beyond basic prompt engineering to understand the mechanics of professional "AI Agencies" by mastering the ReAct (Reasoning + Acting) loop-a methodology that forces agents to verbalize internal logic before execution. This ensures every step of the decision-making process is transparent, systematic, and auditable, bridging the gap between static LLM responses and dynamic agentic behavior.
The curriculum focuses on the practical logic of a Python-based Tool Registry, where you will see how to equip agents to autonomously execute code, interact with external APIs, and perform real-time web searches. You will explore sophisticated memory management strategies, including Semantic Retrieval (RAG) via Vector Databases and agentic memory pruning, to effectively overcome context window limitations and maintain long-term session relevance across complex, multi-turn interactions.
A primary focus of this course is Multi-Agent Orchestration, where you will study how to logically structure collaborative workflows. You will learn to define specialized Personas-such as Researchers, Coders, and Critics-and understand how to organize them into hierarchical or sequential systems. By implementing Human-in-the-Loop (HITL) safety guardrails, you will ensure your AI ecosystems remain controllable. This course is essential for developers and architects aiming to build self-correcting, production-ready AI systems that provide reliable and transparent reasoning paths for real-world enterprise applications.
Who this course is for
This course is designed for software developers, AI engineers, and technical architects who are ready to move beyond basic prompt engineering and explore the frontier of autonomous, multi-agent systems. It is ideal for professionals who have a working knowledge of Python and a high-level understanding of Large Language Models (LLMs) but want to master the architectural patterns-such as ReAct, Orchestrator-Workers, and Evaluator-Optimizers-required to build production-grade AI agencies. Whether you are a researcher looking to automate data synthesis, a coder aiming to integrate agentic workflows into enterprise applications, or a student of computer science eager to understand the shift from single-task AI to collaborative ecosystems, this curriculum provides the technical depth and practical implementation steps needed to succeed. The content is specifically valuable for those who prioritize system reliability, audibility, and safety, offering a clear roadmap for implementing long-term memory via Vector Databases and essential human-in-the-loop guardrails.