Jan 16
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Agentic AI represents one of the most powerful and rapidly evolving shifts in generative AI—and AWS is bringing it to life through native support in Amazon Bedrock. In this video, you’ll learn how large language models move beyond passive text generation and become intelligent, action-oriented systems capable of interacting with real-world data, APIs, and AWS services. We begin by clarifying terminology, explaining why terms like LLM agents and Agentic AI refer to the same core idea: foundation models that can reason, decide, and take action. From there, we contrast traditional foundation models—closed systems limited to training data and prompt context—with agent-based architectures that can retrieve real-time information, invoke APIs, and execute business logic. You’ll then explore the core concept behind Agentic AI: a foundation model equipped with tools. These tools allow models to break out of their sandbox and interact with external systems, transforming LLMs into production-ready, intelligent systems rather than simple chat interfaces. The second half of the video dives deeper into multi-agent system design. You’ll learn when and why single-agent systems fall short, how specialized agents improve scalability and clarity, and how common architectural patterns are used in real-world AI systems. These include the orchestrator pattern, router-based designs for cost optimization, parallel and aggregation workflows, prompt chaining, and evaluator–optimizer loops for iterative refinement. Throughout the session, concepts are explained from a practical AWS perspective, with Amazon Bedrock serving as the foundation for building, deploying, and scaling agentic workflows—without needing to stitch together complex custom frameworks. Whether you’re designing GenAI applications, preparing for AWS certifications, or architecting production-grade AI systems, this video provides a clear mental model for understanding Agentic AI and multi-agent architectures on AWS. #AgenticAI #programming GenAIonAWS #AmazonBedrock #AIAgents #LLMAgents #GenerativeAI #MultiAgentSystems #AIArchitecture