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Explore API vs RAG vs MCP vs A2A Explained in 19 Minutes. with 224,710 views, 3,601 likes, and 136 comments. Experience the impact of this video content that has captured audience attention.

Get $5 in free credits for the Sent API: https://www.sent.dm/en?utm_source=codist&utm_medium=x&utm_campaign=partner Try out my new fav place to learn how to code : https://scrimba.com/?via=codist RAG, MCP, and A2A solve three very different problems in AI agent development—but understanding how they work together is the key to building reliable, production-ready agents. In this video, we build an imaginary travel assistant called Atlas and follow it through the complete AI agent stack: - How APIs provide predictable software integrations - How RAG gives agents fresh, relevant, and authorized knowledge - How MCP connects AI applications to external tools and services - How A2A enables independent agents to collaborate on complex tasks - Why permissions, validation, user consent, and security still matter - How all four layers work together in a real-world booking workflow The simplest distinction: RAG provides knowledge. MCP provides tools. A2A coordinates agents. APIs power the underlying services.** By the end, you’ll understand where each technology fits, what it does—and what it does not do. Sent provides one messaging API for SMS, WhatsApp, and RCS. Sign up through the link above to receive $5 in free credit. Want to see the complete Atlas workflow built in code? Comment **“Atlas”** below. Subscribe for more practical explanations of AI agents, LLM architecture, MCP, RAG, and agentic systems. #RAGVsMCPVsA2A #RAGMCPA2AExplained #MCPExplained #RAGExplained #A2AProtocolExplained #AIAgentsExplained #AIAgentArchitecture #AgenticAI #ModelContextProtocol #Agent2AgentProtocol #RetrievalAugmentedGeneration #AIAgentTutorial #HowAIAgentsWork #MCPVsAPI #A2AVsMCP #RAGVsFineTuning #LLMAgents #AIAgentTools #FunctionCalling #MultiAgentSystems #AgentToAgentProtocol #AIArchitecture #GenerativeAI #AnthropicMCP #GoogleA2A #BuildAIAgents

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