Building Agentic AI Workloads – Crash Course

This course, from Rola Dali, PhD, provides a comprehensive overview of agentic AI, defining agents as software entities that use LLMs to perceive environments, make decisions, and execute actions to achieve specific goals. It explores the critical distinction between static workflows and dynamic agentic systems, emphasizing how LLMs serve as a reasoning "brain" to decompose tasks at runtime. Through practical Python demonstrations, the course covers essential components like system prompts, tools, and memory, while also comparing architectural patterns such as Supervisor and Swarm. Finally, the session addresses the future of technology by discussing emerging interoperability protocols like MCP and the shifting paradigms of software development in an AI-driven world. Slides and Labs: https://github.com/rdali/ML105_Agents Profile: https://www.linkedin.com/in/roladali/ ❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning: https://scrimba.com/freecodecamp ⭐️ Contents ⭐️ - 0:00:00 Introduction and Speaker Background - 0:01:15 A Brief History of Artificial Intelligence (1940s–Present) - 0:05:43 Traditional Machine Learning vs. Generative AI - 0:06:35 The Three Pillars of AI: Algorithms, Data, and Compute - 0:11:08 Specific Tasks vs. General Task Execution - 0:14:41 Defining Agency and the Spectrum of Autonomy - 0:18:00 Agentic Milestone Timeline (2017–2026) - 0:20:31 What is a Generative AI Agent? - 0:23:04 Agents vs. Workflows: Dynamic Flow vs. Static Paths - 0:26:18 Pros and Cons of Agentic Systems - 0:29:59 Patterns and Anti-patterns: When to Use Agents - 0:32:36 The Core Components of an Agent - 0:34:55 Choosing the Right LLM for Your Agent - 0:37:38 Crafting Identity with System Prompts - 0:39:00 Understanding Memory: Intrinsic, Short-term, and Long-term - 0:41:26 Enhancing Capabilities with Tools and Actions - 0:43:09 Hands-on Implementation: From Single LLM Call to Python Agent - 0:52:18 Add