Monitor on-premises and multi-cloud AI agents with AgentCore Observability

Deploying AI agents across diverse environments requires comprehensive observability to ensure performance, safety, and compliance. Amazon Bedrock AgentCore, a platform for building and managing AI agents, offers native observability features within AWS. However, for agents running outside AWS, additional setup is necessary.

This process involves using the AWS Distro for OpenTelemetry (ADOT) to auto-instrument the agent application, capturing traces, metrics, and logs, and exporting this telemetry to Amazon CloudWatch via OTLP endpoints secured with IAM credentials. The setup supports environments such as on-premises servers, Google Cloud Platform, and Microsoft Azure.

The solution architecture includes ADOT auto-instrumentation, IAM credentials for secure authentication, and environment variables to configure routing and authentication. Once configured, telemetry data from external environments is routed to the AgentCore Observability dashboard, allowing centralized monitoring of agent behavior, reasoning chains, and model outputs.

The setup process involves installing dependencies, configuring AWS credentials, setting environment variables, creating the agent application, and running the agent with ADOT auto-instrumentation. Verification is performed via the CloudWatch console, where telemetry data from agents in different environments appears in a unified dashboard.

This approach enables organizations to maintain visibility into AI agents deployed across multiple clouds and on-premises, facilitating responsible AI practices, cost monitoring, and troubleshooting. The method is validated across simulated on-premises setups and Google Cloud Shell, demonstrating its effectiveness and flexibility.

Overall, extending Amazon Bedrock AgentCore Observability beyond AWS enhances multi-cloud and hybrid deployment strategies, providing a consistent and comprehensive view of AI agent performance and behavior.