Course Overview
This course provides a comprehensive guide to building intelligent, autonomous AI agents capable of thinking, planning, and executing real-world tasks. It covers the core lifecycle, architectures, and design patterns of AI agents, with a strong emphasis on practical applications using Python, n8n, and local AI models like Ollama. Students will master advanced prompt engineering techniques and learn to implement AI safety guardrails and human-in-the-loop systems.
Key Takeaways
- Understand the lifecycle, architectures, and design patterns of AI agents including memory and decision-making.
- Build and deploy custom AI agents using Python integrated with local models like Ollama.
- Master advanced prompt engineering techniques such as ReAct, N-Shot, and Chain-of-Thought prompting.
- Design and automate scalable single and multi-agent workflows using Python and n8n.
- Implement AI guardrails and safety evaluations for reliable human-in-the-loop systems.
- Transition from basic AI chatbots to fully autonomous, practical agentic workflows for real-world tasks.
Prerequisites
- Basic understanding of Python programming is recommended.
- No prior experience with n8n, AI agents, or advanced prompt engineering is required.
- A computer with internet access to install and run Python, n8n, and local AI models like Ollama.
- A strong curiosity and desire to learn about automation and artificial intelligence.
Target Learners
- Python developers seeking to build intelligent, autonomous AI agents.
- Automation enthusiasts and professionals integrating AI into n8n workflows.
- Tech entrepreneurs and innovators leveraging AI for practical tasks.
- Anyone interested in Generative AI beyond simple chatbots to advanced agentic automation.
Introduction1.1 Introduction to AI, Generative AI & Agentic AI 8:32
1.2 AI Chatbots, Assistants, Agents & Workflows 5:33
1.3 Real-World AI Agents Examples 7:40
1.4 Mini Practical: Building an AI Agent 4:27
2.1 Understanding the AI Agent Lifecycle 3:59
2.2 Memory, Planning & Decision Making in AI Agents 6:10
2.3 Types of AI Agents 7:22
2.4 Human-in-the-Loop AI Agents 5:13
2.5 Building an Agent with Python 6:41
2.6 Decision Making & Decision Trees 6:54
3.1 Understanding AI Agent Architectures 12:11
3.2 AI Agent Design Patterns 10:46
3.3 ReAct Explained Visually 6:37
3.4 Single Agents vs. Multi-Agent Systems (With Practical Demos) 7:14
3.5 Calling an Ollama AI Model Through Python 6:04
4.1 Writing Effective Prompts (Hands-on Practical Demos) 4:37
4.2 Learning N-Shot Prompting: Zero, One & Few-Shot (With Practical Examples) 3:21
4.3 Chain-of-Thought Prompting (With Practical Demos) 4:21
4.4 Prompt Optimization and Tool Use (With Practical Learning Examples) 7:57
4.5 AI Guardrails & Safety Evaluation 7:16
5.1 AI and Stages of Evaluation 8:54
5.2 Your Relation With AI (With Hands-on Practical Examples) 5:31
Access Files No pi required.
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