Building Agentic AI Workloads – Crash Course

This course, from Rola Dali, PhD, provides a broad overview of agentic AI, defining agents arsenic package entities that usage LLMs to comprehend environments, make decisions, and execute actions to execute circumstantial goals. It explores the captious favoritism betwixt fixed workflows and move agentic systems, emphasizing really LLMs service arsenic a reasoning "brain" to decompose tasks astatine runtime. Through applicable Python demonstrations, the people covers basal components for illustration strategy prompts, tools, and memory, while besides comparing architectural patterns specified arsenic Supervisor and Swarm. Finally, the convention addresses the early of exertion by discussing emerging interoperability protocols for illustration MCP and the shifting paradigms of package improvement successful an AI-driven world. Slides and Labs: https://github.com/rdali/ML105_Agents Profile: https://www.linkedin.com/in/roladali/ ❤️ Support for this transmission comes from our friends astatine Scrimba – the coding level 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 simply 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 pinch System Prompts - 0:39:00 Understanding Memory: Intrinsic, Short-term, and Long-term - 0:41:26 Enhancing Capabilities pinch Tools and Actions - 0:43:09 Hands-on Implementation: From Single LLM Call to Python Agent - 0:52:18 Adding Memory and History to Your Custom Agent - 0:54:53 Building Agents pinch Frameworks (LangChain) - 0:57:17 The Evolving Landscape of Models and Frameworks - 1:00:15 Agentic Architectural Patterns: Supervisor vs. Swarm - 1:01:41 Case Study: Single Agent vs. Supervisor Architecture - 1:04:48 Deep Dive: Swarm Architecture Performance - 1:06:08 When to Choose Multi-agent Systems - 1:09:05 Interface Protocols: MCP, A2A, and AGUI - 1:12:06 How to Evaluate Agentic Systems (LLM vs. System vs. App) - 1:13:53 Evaluation Methods: Code-based, LLM-as-a-Judge, and Human - 1:15:25 Current Challenges: Hallucinations, Cost, and Debugging - 1:18:15 Real-world Incidents and the AI Incident Database - 1:21:28 Career Impact: Which Jobs are Most astatine Risk? - 1:23:41 Software 3.0: The Evolution of Development Paradigms - 1:29:00 Weathering the Storm: Strategies for the Future - 1:33:40 Beyond LLMs: World Models and the Future of AMI - 1:37:15 Recommended Resources and Closing Thoughts 🎉 Thanks to our Champion and Sponsor supporters: 👾 @omerhattapoglu1158 👾 @goddardtan 👾 @akihayashi6629 👾 @kikilogsin 👾 @anthonycampbell2148 👾 @tobymiller7790 👾 @rajibdassharma497 👾 @CloudVirtualizationEnthusiast 👾 @adilsoncarlosvianacarlos 👾 @martinmacchia1564 👾 @ulisesmoralez4160 👾 @_Oscar_ 👾 @jedi-or-sith2728 👾 @justinhual1290 -- Learn to codification for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles connected programming: https://freecodecamp.org/news