AI Agents and Agentic AI Architecture in Python
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AI Agents and Agentic AI Architecture in Python
This course is part of multiple programs.
Instructor: Dr. Jules White
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What you'll learn
How to implement agents that can dynamically adopt different expert personas and reason with or transform unstructured data
Techniques for building Multi-Agent Collaboration Systems in Python that support sophisticated memory sharing and intelligent coordination
How to implement Trustworthy and Safe Agent Architectures in Python using staged execution, reversible actions, and safety patterns
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2 assignments
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There are 5 modules in this course
Master the Art of Building Intelligent Python Agents That Think, Reason, and Act
Unlock the full potential of Python for creating autonomous AI agents that solve complex problems without constant human direction. In this comprehensive course on AI Agents and Agentic AI with Python & Generative AI, you'll learn how to architect sophisticated agent systems that leverage Python's robust ecosystem and industry-standard capabilities. This course takes you beyond the foundations covered in the AI Agents and Agentic AI with Python & Generative AI course to explore advanced patterns for building truly intelligent agents in Python. You'll delve into specialized techniques like self-prompting, expert personas, document-as-implementation, and multi-agent orchestration - all implemented with Python's powerful frameworks and libraries. What You'll Learn: - Self-Prompting Patterns in Python: Build agents that dynamically adopt different thinking modes to handle specialized tasks, transforming unstructured data into structured formats with clean Python implementations - Python-Based Expert Persona Systems: Implement consultation frameworks where agents can invoke domain experts for specialized knowledge while maintaining clean architecture - Document-as-Implementation: Use Python's powerful file handling to create systems where human-readable documents become executable business logic - Multi-Agent Collaboration with Python: Design sophisticated memory sharing and coordination mechanisms between specialized Python agents - Progress Tracking & Planning: Implement robust planning and reflection capabilities using Python's comprehensive tooling - Python Agent Safety & Trust Systems: Build transaction management and safety mechanisms that leverage Python's exception handling and security features By the end of this course, you'll be equipped to build complex, production-ready agent systems in Python that can reason across multiple domains, handle complex workflows, and safely interact with real-world systems. Whether you're building productivity tools, automating complex business processes, or creating intelligent assistants, you'll have the Python-specific knowledge to implement agentic AI solutions that provide genuine business value. This course will teach you these concepts using OpenAI's APIs, which require paid access, but the principles and techniques can be adapted to other LLMs.
What's included
5 videos1 reading1 assignment7 plugins
5 videosβ’Total 39 minutes
- Prompts as Computationβ’9 minutes
- Bridging Computer Tools & Unstructured Data with Prompting - the AI Shimβ’7 minutes
- The Persona Pattern and Reasoning - Personas are an efficient programming abstractionβ’5 minutes
- The Persona Patternβ’14 minutes
- Simple Multi-Agent Systems with Personasβ’5 minutes
1 readingβ’Total 10 minutes
- Format of the Persona Patternβ’10 minutes
1 assignmentβ’Total 30 minutes
- Persona & Self-Prompting Reviewβ’30 minutes
7 pluginsβ’Total 105 minutes
- Self-Prompting & Clean Separation of AI Agent Reasoningβ’15 minutes
- AI Agent Structured Data Extractionβ’15 minutes
- An Invoice Processing Agentβ’15 minutes
- Consulting Experts or Simulating with the Persona Patternβ’15 minutes
- The Persona Abstraction & Agentsβ’15 minutes
- Invoice Processing with Expertsβ’15 minutes
- Using Human Policies for Document-as-Implementationβ’15 minutes
What's included
1 video2 plugins
1 videoβ’Total 8 minutes
- The MATE Design Principles for AI Agentsβ’8 minutes
2 pluginsβ’Total 30 minutes
- MATE Design Principles in Codeβ’15 minutes
- AI Agents & Environment Safetyβ’15 minutes
What's included
4 videos1 assignment3 plugins
4 videosβ’Total 30 minutes
- Introduction to Multi-Agent Systemsβ’6 minutes
- Agent Interaction & Memoryβ’10 minutes
- Removing Noise: Focusing Agent Attentionβ’8 minutes
- Providing Agentic AI Information About the Worldβ’6 minutes
1 assignmentβ’Total 30 minutes
- Agent Interaction Architecturesβ’30 minutes
3 pluginsβ’Total 45 minutes
- Building Multi-Agent Systems: Agent-to-Agent Communicationβ’15 minutes
- Agent Interaction Patterns with Memoryβ’15 minutes
- Advanced Agent Interactionβ’15 minutes
What's included
1 video2 plugins
1 videoβ’Total 4 minutes
- Isolating Agents from Accidental Complexityβ’4 minutes
2 pluginsβ’Total 30 minutes
- Clean AI Tools with Dependency Injectionβ’15 minutes
- Clean Tool Dependency Injection with the Environmentβ’15 minutes
What's included
4 videos3 plugins
4 videosβ’Total 32 minutes
- Improving AI Agent Reasoning with In-Context Learningβ’12 minutes
- Improving AI Agent Reasoning with Up-front Planning & Chain of Thoughtβ’6 minutes
- Improving AI Agent Reasoning with In-loop Planningβ’5 minutes
- The Great Agent Trade-off: Ahead of Time vs. Dynamicβ’9 minutes
3 pluginsβ’Total 45 minutes
- The Capability Architectural Patternβ’15 minutes
- Ahead of Time Planning for Improving Agent Reasoningβ’15 minutes
- Intermediate Planning: Tracking Progress in the Agent Loopβ’15 minutes
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Reviewed on May 11, 2025
One of the great course to understand the importance of controllable outputs from LLMs
Reviewed on Apr 14, 2026
Highly informative and well-aligned with current market needs; the concepts are easily applicable across a wide range of workflows.
Reviewed on Aug 23, 2025
Another amazing class! I will go through this one again.
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