4 AI Agentic Design Patterns – Workflows with Ollama

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4 AI Agentic Design Patterns – Workflows with Ollama

Coursera · Beginner ·🤖 AI Agents & Automation ·1mo ago
This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this comprehensive course, you will delve into the world of AI agentic workflows, exploring the foundational design patterns that make intelligent systems tick. You'll gain hands-on experience in building and integrating these design patterns, starting with the Reflection pattern, followed by Tool Use, Planning (ReAct), and Multi-agent collaboration. As you progress, you will learn how to implement these patterns in real-world scenarios, enhancing your ability to design AI systems that are both efficient and intelligent. The course is structured into multiple modules, each focusing on one design pattern. The first few lessons set the stage by covering essential prerequisites, such as development environment setup and an in-depth dive into AutoGen, a tool crucial for AI agentic workflows. Then, each design pattern is introduced through theory, followed by hands-on exercises to cement your understanding. Whether you're creating simple workflows or complex multi-agent systems, the course guides you step-by-step through the process. This course is perfect for anyone with a basic understanding of AI looking to deepen their knowledge and practical skills. You’ll learn how to apply cutting-edge techniques to solve real-world challenges in AI agentic systems. The course is beginner-friendly, but a background in basic AI principles and programming would be beneficial. By the end of the course, you will be able to confidently apply the four key AI agentic design patterns to build sophisticated workflows, integrate multi-agent systems, and optimize decision-making in intelligent systems. You will also understand how to leverage Ollama Models locally to reduce costs in AI workflows.
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