Agentic AI: Deploy LangChain v1 Agent Projects to Production
Last updated 2/2026
Created by KGP Talkie | Laxmi Kant
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 257 Lectures ( 26h 41m ) | Size: 22.1 GB
Agentic AI, Gen AI Agents, LangChain v1, Gemini 3, LLM & MCP Projects, Guardrails, FastAPI, Deploy AI App, Python AI ML
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
Requirements
● Basic knowledge of Python, including functions, variables, and working with libraries
● Familiarity with running Python code in notebooks or a code editor like VS Code
● A computer with internet access to use cloud APIs and developer tools
● Willingness to learn and experiment with AI and new technologies
Description
LangChain AI Agent Projects: Build and Deploy Real AI Agents
Learn how to build intelligent AI agents using LangChain and Google Gemini and deploy them in real-world applications. This course 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.
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.
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.
What you will learn
• Build AI agents using LangChain and Google Gemini
• Understand how AI agents differ from large language models
• Apply ReAct reasoning patterns for agent decision making
• Configure and use tools and function calling in agents
• Implement short-term and long-term memory for agents
• Use embeddings and databases for memory storage
• Apply prompt engineering techniques for agent control
• Stream real-time responses from AI agents
• Add middleware for safety and control
• Implement guardrails and human-in-the-loop systems
• Secure agents using sandboxed code execution
• Build REST APIs for AI agents using FastAPI
• Create user interfaces with Streamlit
• Deploy AI agents on AWS EC2
• Connect agents to external services using MCP
• Build end-to-end full-stack AI applications
Who this course is for
â Python developers who want to build real world AI agents and automation tools.
â Backend and software engineers who want to add AI features to their applications.
â AI and ML engineers who want hands-on experience building real-world AI agent systems.
â Software engineers interested in using large language models in real products.
â Automation engineers who want to build intelligent workflows using AI.
â Data analysts who want to automate reporting, summarization, and analysis using AI agents.
â Product engineers who want to prototype AI-powered features quickly.
â Startup founders who want to build AI-driven tools without hiring a large team.
â Students who want project-based learning in AI agent development.
â Professionals who want to improve productivity using AI agents in daily work.
â Anyone curious about how AI agents work and how to apply them in real business use cases.
â Developers who want to learn how to connect AI models with real APIs like Gmail, YouTube, and weather services.
â Developers exploring LangChain, Google Gemini, and modern agent frameworks
â Students and professionals preparing for careers in AI, GenAI, or intelligent application development
â Builders and founders who want to create AI-powered products and prototypes
â Python developers who want to build real AI applications and move into AI agent development
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