AAIFAWAmazon Web ServicesAW-AAIF1.0<p>In this course, you will learn to:</p> <ul> <li>Summarize the evolution of Agentic AI and define what makes something &quot;agentic&quot;</li><li>Identify core components of agentic systems: goals, memory, tools, and environment</li><li>Distinguish between workflow, autonomous, and hybrid agents</li><li>Compare AWS service options for Agentic AI (Specialized, Managed, and DIY approaches)</li><li>Describe capabilities and use cases of Amazon Q Developer, Amazon Q Business, and Kiro</li><li>Explain Amazon AgentCore and Amazon Bedrock Agents core functionalities</li><li>Identify basic implementation patterns for Agentic AI</li><li>Describe observability and interoperability patterns for production agentic AI systems</li></ul><p>We recommend that attendees of this course have:</p> <ul> <li>Generative AI Essentials or equivalent work experience</li><li>Basic AWS knowledge and software development experience</li></ul><p>This course is intended for:</p> <ul> <li>Software developers new to Agentic AI seeking foundational knowledge and practical implementation skills</li><li>Technical professionals exploring AI capabilities and interested in core components and applications of agentic AI</li><li>Development teams evaluating Agentic AI solutions and needing to differentiate between agent types</li><li>AWS Users expanding into Agentic AI, including current users of Amazon Q Developer, Amazon Q Business, and Amazon Bedrock Agents</li></ul><h5>Module 1: From LLMs to Agents</h5><ul> <li>Understanding Large Language Models (LLMs)</li><li>Innovations powering agents</li><li>Evolution timeline from LLMs to Agents</li></ul><h5>Module 2: Exploring Agentic AI</h5><ul> <li>Understanding Agentic AI</li><li>Types of AI agents</li><li>Agentic AI applications</li></ul><h5>Module 3: Understanding Agentic AI Workflows</h5><ul> <li>Workflow patterns</li><li>Amazon Bedrock flows overview</li><li>Demo: Amazon Bedrock Flows</li></ul><h5>Module 4: Introducing Autonomous Agents</h5><ul> <li>How Autonomous Agents work</li><li>ReAct</li><li>ReWoo</li><li>Multi-agent collaboration</li><li>AWS Agentic AI solutions</li></ul><h5>Module 5: Amazon Q and Agentic Development Tools</h5><ul> <li>Amazon Q Developer</li><li>Amazon Q Business</li><li>Amazon Q in AWS Services</li><li>Kiro: AI-powered IDE with spec-driven development</li><li>Demo: Amazon Q</li></ul><h5>Module 6: Agentic AI with Amazon Bedrock</h5><ul> <li>Amazon Bedrock Agents</li><li>Amazon Bedrock AgentCore</li><li>Demo: Amazon Bedrock Agents</li><li>Hands-on lab: Explore Amazon Bedrock Agents integrated with Amazon Bedrock Knowledge Bases and Amazon Bedrock Guardrails</li></ul><h5>Module 7: Building DIY Solutions</h5><ul> <li>DIY solutions</li><li>Observability and Monitoring</li><li>Agent Interoperability</li></ul><h5>Module 8: Course Wrap-up</h5><ul> <li>Next steps and additional resources</li><li>Course summary</li></ul>In this course, you will learn to: - Summarize the evolution of Agentic AI and define what makes something "agentic" - Identify core components of agentic systems: goals, memory, tools, and environment - Distinguish between workflow, autonomous, and hybrid agents - Compare AWS service options for Agentic AI (Specialized, Managed, and DIY approaches) - Describe capabilities and use cases of Amazon Q Developer, Amazon Q Business, and Kiro - Explain Amazon AgentCore and Amazon Bedrock Agents core functionalities - Identify basic implementation patterns for Agentic AI - Describe observability and interoperability patterns for production agentic AI systemsWe recommend that attendees of this course have: - Generative AI Essentials or equivalent work experience - Basic AWS knowledge and software development experienceThis course is intended for: - Software developers new to Agentic AI seeking foundational knowledge and practical implementation skills - Technical professionals exploring AI capabilities and interested in core components and applications of agentic AI - Development teams evaluating Agentic AI solutions and needing to differentiate between agent types - AWS Users expanding into Agentic AI, including current users of Amazon Q Developer, Amazon Q Business, and Amazon Bedrock AgentsModule 1: From LLMs to Agents - Understanding Large Language Models (LLMs) - Innovations powering agents - Evolution timeline from LLMs to Agents Module 2: Exploring Agentic AI - Understanding Agentic AI - Types of AI agents - Agentic AI applications Module 3: Understanding Agentic AI Workflows - Workflow patterns - Amazon Bedrock flows overview - Demo: Amazon Bedrock Flows Module 4: Introducing Autonomous Agents - How Autonomous Agents work - ReAct - ReWoo - Multi-agent collaboration - AWS Agentic AI solutions Module 5: Amazon Q and Agentic Development Tools - Amazon Q Developer - Amazon Q Business - Amazon Q in AWS Services - Kiro: AI-powered IDE with spec-driven development - Demo: Amazon Q Module 6: Agentic AI with Amazon Bedrock - Amazon Bedrock Agents - Amazon Bedrock AgentCore - Demo: Amazon Bedrock Agents - Hands-on lab: Explore Amazon Bedrock Agents integrated with Amazon Bedrock Knowledge Bases and Amazon Bedrock Guardrails Module 7: Building DIY Solutions - DIY solutions - Observability and Monitoring - Agent Interoperability Module 8: Course Wrap-up - Next steps and additional resources - Course summary1 day750.00900.00750.00750.00750.00695.00960.00890.00490.00750.00700.00695.00