Understand Agentic AI

About this course

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.

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