Observability runtime metrics Resource usage metrics and logs Provided span data Application log data Error types

AgentCore generated runtime observability data

The runtime metrics provided by AgentCore give you visibility into your agent execution activity levels, processing latency, resource utilization, and error rates. AgentCore also provides aggregated metrics for total invocations and sessions.

Observability runtime metrics

The following list describes the runtime metrics provided by AgentCore. Runtime metrics are batched at one minute intervals. To learn more about viewing runtime metrics, see View observability data for your Amazon Bedrock AgentCore agents.

Invocations: Shows the total number of requests made to the Data Plane API. Each API call counts as one invocation, regardless of the request payload size or response status.
Invocations (aggregated): Shows the total number of invocations across all resources
Throttles: Displays the number of requests throttled by the service due to exceeding allowed TPS (Transactions Per Second) or quota limits. These requests return ThrottlingException with HTTP status code 429. Monitor this metric to determine if you need to review your service quotas or optimize request patterns.
System Errors: Shows the number of server-side errors encountered by AgentCore during request processing. High levels of server-side errors can indicate potential infrastructure or service issues that require investigation. See Error types for a list of possible error codes.
User Errors: Represents the number of client-side errors resulting from invalid requests. These require user action to resolve. High levels of client-side errors can indicate issues with request formatting or permissions that need to be addressed. See Error types for a list of possible error codes.
Latency: The total time elapsed between receiving the request and sending the final response token. Represents complete end-to-end processing time of the request.
Total Errors: The total number of system and user errors. In the Amazon Bedrock AgentCore console, this metric displays the number of errors as a percentage of the total number of invocations.
Session Count: Shows the total number of agent sessions. Useful for monitoring overall platform usage, capacity planning, and understanding user engagement patterns.
Sessions (aggregated): Shows the total number of sessions across all resources.

Resource usage metrics and logs

Amazon Bedrock AgentCore runtime provides comprehensive resource usage telemetry, including CPU and memory consumption metrics for your runtime resources.

Note

Resource usage data may be delayed by up to 60 minutes and precision might differ across metrics.

Vended metrics

Amazon Bedrock AgentCore runtime automatically provides resource usage metrics at account, agent runtime, and agent endpoint levels. These metrics are published at 1-minute resolution. Amazon CloudWatch aggregation and metric data retention will follow standard Amazon CloudWatch data retention polices. For more information, see https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/cloudwatch_concepts.html#Metric.

Here are the dimension sets and metrics available for monitoring your resources:

Name	Dimensions	Description
CPUUsed-vCPUHours	Service; Service, Resource; Service, Resource, Name	The total amount of virtual CPU consumed in vCPU-Hours unit, available at the resource and account levels. Useful for resource tracking and estimated billing visibility.
MemoryUsed-GBHours	Service; Service, Resource; Service, Resource, Name	The total amount of memory consumed in GB-Hours unit, available at the resource and account levels. Useful for resource tracking and estimated billing visibility.

Dimension explanation

Service - AgentCore.Runtime
Resource - Agent Arn
Name - Agent Endpoint name, in the format of AgentName::EndpointName

Account level metrics are available in Amazon CloudWatch Bedrock AgentCore Observability Console under the Runtime tab. The dashboard displays Memory and CPU usage graphs generated from these metrics, representing total resource usage across all agents in your account within the region.

Agent Endpoint level metrics are available in AgentEndpoint page of Amazon CloudWatch Bedrock AgentCore Observability Console. The dashboard displays Memory and CPU usage graphs generated from these metrics, representing total resource usage across all sessions invoked by the specified Agent Endpoint.

Note

Telemetry data is provided for monitoring purposes. Actual billing is calculated based on metered usage data and may differ from telemetry values due to aggregation timing, reconciliation processes, and measurement precision. Refer to your AWS billing statement for authoritative charges.

Vended logs

Bedrock AgentCore Runtime provides vended logs for session-level usage metrics at 1-second granularity. Each log record contains resource consumption data including CPU usage (agent.runtime.vcpu.hours.used) and memory consumption (agent.runtime.memory.gb_hours.used).

Each log record will have following schema:

Log type	Log fields	Description
USAGE_LOGS	event_timestamp, resource_arn, service.name, cloud.provider, cloud.region, account.id, region, resource.id, session.id, agent.name, elapsed_time_seconds, agent.runtime.vcpu.hours.used, agent.runtime.memory.gb_hours.used	Resource Usage Logs for session-level resource tracking.

To enable USAGE_LOG log type for your agents, see Add observability to your Amazon Bedrock AgentCore resources. The logs are then displayed in the configured destination (AWS LogGroup, Amazon S3 or Amazon Kinesis Firehose) as configured.

In the Agent Session page of the Amazon CloudWatch Bedrock AgentCore Observability Console, you can see resource usage metrics generated from these logs. To optimize your metric viewing experience, select your desired time range using the selector in the top right to focus on specific CPU and Memory Usage data.

Note

Provided span data

To enhance observability, AgentCore provides structured spans that provide visibility into agent runtime invocations. To enable this span data, you need to enable observability on your agent resource. See Add observability to your Amazon Bedrock AgentCore resources for steps and details. This span data is available in AWS CloudWatch Logs aws/spans log group. The following table defines the operation for which spans are created and the attributes for each captured span.

Operation name	Span attributes	Description
InvokeAgentRuntime	aws.operation.name, aws.resource.arn, aws.request_id, aws.agent.id, aws.endpoint.name, aws.account.id, session.id, latency_ms, error_type, aws.resource.type, aws.xray.origin, aws.region	Invokes the agent runtime.

aws.operation.name - the operation name (InvokeAgentRuntime)
aws.resource.arn - the Amazon resource name for the agent runtime
aws.request_id - request ID for the invocation
aws.agent.id - the unique identifier for the agent runtime
aws.endpoint.name - the name of the endpoint used to invoke the agent runtime
aws.account.id - customer’s account id
session.id - the session ID for the invocation
latency_ms - the latency of the request in milliseconds
error_type - either throttle, system, or user (only present if error)
aws.resource.type - the CFN resource type
aws.xray.origin - the CFN resource type used by x-ray to identify the service
aws.region - the region the customer resource exists in

Application log data

AgentCore provides structured Application logs that help you gain visibility into your agent runtime invocations and session-level resource consumption. This log data is provided when enabling observability on your agent resource. See Add observability to your Amazon Bedrock AgentCore resources for steps and details. AgentCore can output logs to CloudWatch Logs, Amazon S3, or Firehose stream. If you use a CloudWatch Logs destination, these logs are stored under your agent’s application logs or under your own custom log group.

Log type	Log fields	Description
APPLICATION_LOGS	timestamp, resource_arn, event_timestamp, account_id, request_id, session_id, trace_id, span_id, service_name, operation, request_payload, response_payload	Application logs for InvokeRuntimeOperation with tracing fields, request, and response payloads

request_payload - the request payload of the agent invocation
response_payload - the response from the agent invocation

Error types

The following list defines the possible error types for user, system, and throttling errors.

User error codes

InvocationError.Validation - Client provided invalid input (400)
InvocationError.ResourceNotFound - Requested resource doesn't exist (404)
InvocationError.AccessDenied - Client lacks permissions (403)
InvocationError.Conflict - Resource conflict (409)

System error codes

InvocationError.Internal - Internal server error (500)

Throttling error codes

InvocationError.Throttling - Rate limiting (429)
InvocationError.ServiceQuota - Service-side quota/limit reached (402)

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Document Conventions

AgentCore generated observability data

Memory observability data