Production artificial intelligence (AI) agents can fail silently. They may return plausible but incorrect answers, enter infinite reasoning loops, or select the wrong tools without triggering error alerts. These failures make debugging production agent behavior difficult because standard logs and metrics do not capture how decisions are made.

Amazon Bedrock AgentCore Observability addresses these debugging challenges by giving you visibility into agent execution across three layers: metrics, traces, and structured logs. You can follow each reasoning step, inspect tool invocations, and identify exactly where execution diverges from expectations. This visibility moves you from detecting that a failure occurred to understanding why it happened. You can trace how your agent reasoned, which tools it selected, and where the workflow broke down, even when no explicit error is raised. In this post, you learn how to debug production agent failures using built-in observability capabilities. We walk through common failure patterns, show how to analyze agent behavior with traces and metrics, and provide structured workflows for resolving issues such as infinite loops and tool invocation failures. This is Part 1 of a two-part series. Part 2 covers performance optimization and memory management.