Agentic AI: Deploy LangChain AI Agent Projects to Production

<div>Learn how to build intelligent AI agents using LangChain and Google Gemini and deploy them in real-world applications. This <b><u>Agentic AI: Deploy LangChain AI Agent Projects to Production course</u></b> is designed to take you from the fundamentals of AI agents to advanced, production-ready systems through hands-on projects and clear step-by-step guidance.</div><div><br></div><div>AI agents are the next step beyond chatbots. They can reason, use tools, access APIs, remember users, and automate tasks. This course focuses on teaching you how to build such systems in a practical and structured way.</div><div><br></div><div>You will learn both theory and implementation, and you will build multiple real-world projects that demonstrate how AI agents are used in modern applications.</div><div><br></div><div>What you will learn</div><div><ul><li><span style="font-size: 1rem;">Build AI agents using LangChain and Google Gemini</span></li><li><span style="font-size: 1rem;">Understand how AI agents differ from large language models</span></li><li><span style="font-size: 1rem;">Apply ReAct reasoning patterns for agent decision making</span></li><li><span style="font-size: 1rem;">Configure and use tools and function calling in agents</span></li><li><span style="font-size: 1rem;">Implement short-term and long-term memory for agents</span></li><li><span style="font-size: 1rem;">Use embeddings and databases for memory storage</span></li><li><span style="font-size: 1rem;">Apply prompt engineering techniques for agent control</span></li><li><span style="font-size: 1rem;">Stream real-time responses from AI agents</span></li><li><span style="font-size: 1rem;">Add middleware for safety and control</span></li><li><span style="font-size: 1rem;">Implement guardrails and human-in-the-loop systems</span></li><li><span style="font-size: 1rem;">Secure agents using sandboxed code execution</span></li><li><span style="font-size: 1rem;">Build REST APIs for AI agents using FastAPI</span></li><li><span style="font-size: 1rem;">Create user interfaces with Streamlit</span></li><li><span style="font-size: 1rem;">Deploy AI agents on AWS EC2</span></li><li><span style="font-size: 1rem;">Connect agents to external services using MCP</span></li><li><span style="font-size: 1rem;">Build end-to-end full-stack AI applications</span></li></ul></div>

What you'll learn:

• Build real AI agents using LangChain and Google Gemini that can reason, use tools, and complete tasks autonomously
• Design agent architectures using ReAct patterns, tool calling, and structured decision making
• Implement short-term and long-term memory in AI agents using databases and embeddings for personalized experiences
• Create and manage agent tools for web search, weather, finance, document analysis, and external APIs
• Apply prompt engineering techniques to control agent behavior, improve output quality, and guide tool usage
• Add safety layers such as guardrails, human-in-the-loop approval, and middleware controls to prevent errors and misuse
• Stream real-time responses and generate structured outputs from AI agents in production-style applications
• Secure AI agents with sandboxed code execution to prevent file deletion, credential leaks, and system risks
• Build REST APIs for AI agents using FastAPI with validation, CORS, and production-ready patterns
• Develop full-stack AI applications using Streamlit connected to LangChain agents
• Deploy AI agents on AWS EC2 and configure them for real-world access and scalability