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You deployed your AI agent to production with and added . Now users interact with your agent, but can you answer these questions: How do you trace agent decisions? What happens when behavior is inconsistent? How do you detect performance degradation?
Traditional monitoring shows infrastructure status. Agent observability reveals whether your AI makes effective decisions. Amazon Bedrock AgentCore Observability solves this problem. When you deploy your agent on AgentCore Runtime, observability works automatically—complete visibility without instrumentation code or configuration changes.
AgentCore Observability by Services Overview| Service | Purpose | Observability Features |
|---|---|---|
| AgentCore Runtime | Serverless execution | Automatic observability without code changes |
| AgentCore Memory | Cross-session persistence | Built-in span tracking |
| AgentCore Gateway | API management | Tool invocation monitoring |
| AgentCore Identity | Credential management | Access logging |
| AgentCore Observability | *Production monitoring | OpenTelemetry traces, metrics, dashboards |
Understanding Sessions, Traces, and SpansAgentCore Observability uses a three-tier hierarchy providing visibility at different granularity levels:
Sessions represent complete interaction context between you and your agent, from initialization to termination. Sessions provide high-level views of engagement patterns, monitor agent performance, and show how you interact with agents over time. Learn more in documentation.
Traces capture single request-response cycles within sessions—the complete execution path from agent invocation to response. Traces include request details, processing steps, tool invocations, resource utilization, error information, and response generation. This provides deep insights into internal agent workings for troubleshooting and optimization. Learn more in documentation.
Spans represent discrete, measurable units of work within traces—fine-grained operations with defined start and end times. Spans capture operation names, timestamps, parent-child relationships, tags, events, and status information. Spans form hierarchical structures within traces; for example, a "process user query" span contains child spans for "parse input," "retrieve context," and "generate response." Learn more in documentation.
The AgentCore Observability Advantage- Zero Development - Automatic instrumentation for Runtime agents
- Complete Visibility - Sessions, traces, spans, and metrics in one place
- Pre-Built Dashboards - CloudWatch GenAI Observability ready to use
- Framework Agnostic - Works with Strands Agents, LangChain, CrewAI, or custom code
- OpenTelemetry Standard - Industry-standard telemetry format
- Production-Grade - Built for scale and enterprise reliability
Step-by-Step: Enable CloudWatch for AI Agent MonitoringLearn more in documentation.
Console Setup (5-minute tutorial)Important: This is a one-time setup per AWS account. After you enable it, all AgentCore agents automatically send observability data to CloudWatch.
Via Console:
- Open the
- Navigate to Application Signals → Transaction Search
- Choose Enable Transaction Search
- Select the checkbox to ingest spans as structured logs
- Set indexing percentage to 1% (free tier) or adjust based on your requirements
Wait 10 minutes after enabling for spans to become available for search and analysis.
Learn more:
Verify Observability is ActiveWhen you deploy your agent with agentcore launch, observability activates automatically. No additional configuration needed.
Use the AgentCore CLI to check your agent's status:
agentcore status
Your agent now sends session metrics, trace data with spans, performance metrics (latency, duration, tokens), and error tracking.
Navigate the GenAI Observability DashboardAccess your agent's observability data through the CloudWatch GenAI dashboard:
- Open the
- In the left navigation, select GenAI Observability
- Click the Bedrock AgentCore tab
The main dashboard provides overview metrics: Total Agents, Sessions, Traces, Error Rate, and Throttle Rate. Adjust time filters to view specific periods (last hour, day, week, or custom range).
, I don't have many agents on this account.
Analyzing AI Agent Performance: Dashboard Deep DiveThe dashboard offers three specialized views for detailed analysis:
Agents View shows all agents (Runtime and non-Runtime hosted), agent-specific performance metrics, session and trace counts per agent, error rates, and visualization graphs. Use this view to monitor operational health and compare performance across agents.
Sessions View displays all sessions across agents, session duration, request counts, session-level errors, user engagement patterns, and session timelines. Filter by Session ID, Agent, Time range, Duration, or Error status. Key insights reveal longest sessions, error locations, typical conversation length, and average requests per session.
Traces View provides detailed trace information, complete span breakdowns, execution timelines (waterfall visualization), tool invocation sequences, and error stack traces. Use this view to debug issues, optimize bottlenecks, and understand complete execution flow.
Analyze Trace DetailsClick any Trace ID in the Traces View to open detailed visualization. The trace shows a tree structure of operations (spans) with parent-child relationships. A waterfall chart displays the execution timeline, identifying sequential operations, parallel operations, bottlenecks, and error points.
Click any span to see detailed information in three tabs: Attributes (operation metadata, input parameters, output results), Events (significant occurrences with timestamps), and Duration and Status (exact start/end times, total duration, success/error status).
You achieved comprehensive production monitoring without writing instrumentation code. AgentCore Runtime handles this automatically.
Expanding Observability to All AgentCore ServicesAgentCore Runtime automatically creates CloudWatch log groups for service-provided logs. However, for Memory, Gateway, Identity, and Built-in Tools resources, you must configure log destinations manually. Follow the steps.
Since Memory, Gateway, Identity, and Built-in Tools don't appear in the GenAI Observability dashboard, access their metrics directly in CloudWatch:
AgentCore Memory tracks memory lifecycle operations: Latency, Invocations, System/User Errors, Throttles, and Creation Count. Learn more in documentation.
AgentCore Gateway monitors tool invocations: Invocations, Latency, Duration, TargetExecutionTime, Throttles, Errors, and TargetType. Learn more in documentation.
AgentCore Identity tracks authentication operations: WorkloadAccessTokenFetch metrics, ResourceAccessTokenFetch metrics, and ApiKeyFetch metrics. Learn more in documentation.
Built-in Tools monitors Code Interpreter and Browser: Tool Invocations, Browser TakeOver events, and Resource Usage (CPU, Memory). Learn more in documentation
Monitor Key Performance MetricsAgentCore automatically tracks critical metrics for production monitoring. Learn more in
What You've LearnedIn this tutorial, you mastered production observability for AI agents without writing instrumentation code. You activated CloudWatch Transaction Search for automatic observability, understood the observability hierarchy (sessions, traces, spans), navigated the GenAI Observability Dashboard, analyzed trace details with waterfall visualizations, extended observability to Memory, Gateway, Identity, and Built-in Tools, and used OpenTelemetry standards for flexibility.
AgentCore Observability provides zero-code instrumentation for Runtime agents and comprehensive metrics for all services—giving you production-grade visibility into agent behavior, performance, and operational health.
Complete AgentCore SeriesMaster production AI agents with this series:
Monitor AI Agents in Production Without New Development ← You are here
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