Summary
Full Transcript
In this video, you’ll learn how multi-agent systems extend single-agent (augmented LLM) designs by introducing multiple specialized agents—each focused on a specific responsibility—to solve complex problems more effectively. We start by reviewing single-agent limitations, including tool overload, prompt complexity, and context window constraints. From there, we introduce multi-agent systems, explain when they make sense, and walk through the most common multi-agent design patterns used in real-world AI systems. What You’ll Learn What multi-agent systems are and why they matter When to use single-agent vs multi-agent architectures How specialization improves scalability, clarity, and output quality Common signals that your workload needs multiple agents Multi-Agent Patterns Covered Orchestrator Pattern – breaking complex tasks into sub-tasks and coordinating specialized worker agents Router-Based Pattern – routing requests to the best-fit model based on intent, complexity, or cost Parallel & Aggregation Pattern – running multiple agents in parallel and combining results Prompt Chaining with Gates – sequential agent workflows with validation checkpoints Evaluator–Optimizer Pattern – iterative refinement using generation and automated evaluation Architecture Focus Each pattern is explained using clear architecture diagrams, showing how agents interact, run in parallel or sequence, and produce grounded, high-quality outputs—all aligned with Amazon Bedrock agent design principles. This video is ideal for: AWS developers and architects GenAI engineers building agentic workflows Anyone preparing for AWS Generative AI or Bedrock-related certifications Teams designing scalable, cost-aware AI systems
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