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.

Before following the walkthroughs in this post, make sure you have the required access and tools in place.

You need an AWS account with Amazon Bedrock AgentCore access turned on, familiarity with Amazon CloudWatch dashboards and basic log querying, and a working understanding of AWS Identity and Access Management (IAM) roles and policies. You also need CloudWatch Transaction Search turned on for your account (see the Enabling observability section) and an Amazon Bedrock AgentCore agent already deployed or permission to deploy one.

AI agents fail differently than traditional applications. You might see successful executions that still return incorrect answers, incomplete workflows, or unexpected tool usage. These issues often appear in production without triggering standard error alerts, which makes them harder to detect and diagnose. Most production issues fall into three categories: quality, reliability, and efficiency. Understanding these patterns helps you narrow your investigation quickly.

Quality failures occur when your agent completes a task but returns incorrect results. Monitoring systems often show successful executions while users receive inaccurate responses. Hallucinations and factual errors appear frequently. An agent might reference policies that do not exist or generate data to fill gaps. In multi-agent systems, these errors can propagate when one agent’s output becomes another agent’s input. Reasoning issues can also surface. An agent may repeat the same incorrect calculation or choose an inappropriate tool. When this happens, reviewing execution traces helps you identify where the logic breaks down.