Amazon Bedrock AgentCore Observability addresses debugging challenges for production AI agents that can fail silently by returning plausible but incorrect answers, entering infinite reasoning loops, or selecting the wrong tools without triggering error alerts. The service provides visibility into agent execution across three layers: metrics, traces, and structured logs, allowing users to follow each reasoning step, inspect tool invocations, and identify where execution diverges from expectations. This is Part 1 of a two-part series, with Part 2 covering performance optimization and memory management. Prerequisites include an AWS account with Amazon Bedrock AgentCore access, familiarity with Amazon CloudWatch dashboards and basic log querying, a working understanding of AWS Identity and Access Management (IAM) roles and policies, CloudWatch Transaction Search turned on, and an Amazon Bedrock AgentCore agent already deployed or permission to deploy one. Most production issues fall into three categories: quality, reliability, and efficiency. Quality failures occur when an agent completes a task but returns incorrect results, including hallucinations, factual errors, reasoning issues, or inappropriate tool selection.