Agentic Programming with LangChain

Overview Agentic Programming is transforming AI development by enabling autonomous, adaptable AI agents that can collaborate, plan, and execute tasks independently. This course provides a hands-on introduction to building and deploying intelligent AI agents using cutting-edge frameworks like Microsoft AutoGen, Magentic-One, and n8n. Participants will learn how to integrate AI agents into workflows, automate complex processes, and enhance productivity in real-world applications. _ Designed for developers, automation specialists, and AI professionals, this course equips learners with the skills to create multi-agent systems, validate AI-generated data, and implement best practices for AI-driven automation. By the end, participants will understand how to leverage agentic AI for business efficiency, software development, and intelligent decision-making—while also addressing key ethical and security considerations. Note: As AI technology evolves, the course content will be updated periodically to reflect new trends and advancements. Prerequisites Basic knowledge of Generative AI is required. Understanding of Python programming concepts is recommended, but not required. COURSE OUTLINE Introduction to AI Agents and Agentic AI Understanding agentic programming and its role in AI development How AI agents differ from traditional software applications Real-world examples of agentic AI in automation and business workflows Core Principles of Agent-Oriented Programming (AOP) Key characteristics of AI agents: autonomy, adaptability, and interactivity Multi-agent collaboration: How AI agents communicate and interact Overview of agentic design patterns: reflection, tool use, planning, and multi-agent collaboration Building Intelligent AI Agents with AutoGen Introduction to Microsoft’s AutoGen framework: Capabilities and use cases Understanding the agent communication model and message passing Designing and deploying agents using Python and AutoGen APIs Using AutoGen Studio for rapid prototyping and workflow design Implementing Multi-Agent Systems with Magentic-One What is Magentic-One? How it extends AutoGen for complex workflow Orchestration of AI agents: Coordinating multiple specialized agents Practical use cases: Task delegation, multi-step planning, and execution Integrating AI Agents into Workflows with n8n Introduction to n8n as a workflow automation tool Connecting agents to external services: Email, Google Docs, and APIs Automating repetitive tasks with AI-powered decision-making Validating and Structuring Agentic Data with Pydantic Introduction to data validation in AI workflows Using Pydantic to define schemas for AI-generated content Ensuring structured and reliable outputs from AI agents AI Agents in Business and Automation Automating email processing: Categorizing and summarizing incoming emails AI-assisted content creation: Refining blog posts and generating business documents Improving productivity with AI-driven workflow automation GitHub and Copilot for AI-Assisted Development Understanding AI-powered code generation with GitHub Copilot Best practices for integrating Copilot into Python and AutoGen development Using Git for version control in agentic programming projects GitHub and Copilot for AI-Assisted Development Ethical Considerations and Challenges Understanding AI bias and accountability in autonomous agents Security concerns and responsible AI usage in workflow automation Best practices for monitoring and auditing AI agent decisions