Learn how to build real-world AI agents using frameworks like LangChain, LangGraph, CrewAI, and AutoGen. This playlist covers agent architectures, workflows, tool calling, memory, and real-world use cases — from beginner to advanced. Perfect for AI engineers, developers, and system design learners. #AIAgents #GenerativeAI #LangChain #AIEngineering
Curated by: Naveen Tech Hub (38 videos)
Ever wondered what happens when AI agents finally start talking to each other as effortlessly as we do? In this video, we dive into the next massive leap in artificial intelligence: Multi-Agent Systems (MAS). We explore how single, isolated AI chatbots are evolving into dynamic, collaborative teams capable of independently solving complex, real-world problems. To make this AI teamwork possible, we break down two groundbreaking open-source protocols that are shaping the future of AI architecture: 🤝 Agent-to-Agent (A2A) Protocol Released by Google, A2A acts as a "Universal Language" or "diplomat" for AI. It provides a standardized communication framework that allows agents built by different developers or vendors to seamlessly discover each other, negotiate, and delegate tasks. It allows multiple agents to share goals and coordinate complex workflows securely. 🛠️ Model Context Protocol (MCP) Introduced by Anthropic, MCP acts as the AI's "hands" or a "Universal Adapter". While A2A helps agents talk to each other, MCP securely connects those agents to the outside world—giving them real-time access to live APIs, databases, and enterprise applications like Slack or GitHub. What you will learn in this video: Why complex problems exceed the capabilities of a single AI agent. How A2A uses "Agent Cards" so new AIs can introduce their skills to the rest of the team. How A2A and MCP perfectly complement each other to create highly scalable ecosystems. Real-World Example: We walk through an automated flight booking system where a Booking Agent, a Calendar Agent, and a Payment Agent collaborate using A2A, while relying on MCP to execute live searches and process transactions. If you want to understand how AI is moving from simply answering prompts to autonomously managing multi-step workflows through decentralized teamwork, this video is for you Tags: #AIAgents #A2AProtocol #MCP #ArtificialIntelligence #MultiAgentSystems #FutureOfTech #GoogleAI #Anthropic