Bedrock AI Agent Monitoring and Iteration

Deploying AI agents is only the first step; keeping them reliable, safe, and accurate in production requires systematic monitoring and continuous refinement. This text-based course guides you through the foundational concepts of observability for autonomous AI, showing you how to analyze agent behavior, track performance metrics, and iterate safely without disrupting your users. What you'll learn: - Understand the core architecture of Bedrock AI agents and how they process user prompts. - Configure trace logging to inspect the step-by-step reasoning and orchestration of your agents. - Monitor agent performance and system health using CloudWatch metrics and custom alarms. - Evaluate response quality using modern evaluation techniques and feedback loops. - Apply safe iteration practices, versioning, and aliases to deploy agent updates with zero downtime. - Analyze agent-tool interactions to debug integration issues and optimize prompt performance. You will start with foundational definitions of agent observability before moving into written guides on log analysis, metric tracking, and systematic evaluation techniques. This course is designed for software developers, cloud enthusiasts, and AI beginners looking to manage production-grade agents with no advanced machine learning background required. Start reading today to master the art of production AI observability and continuous improvement.