AWS Launches CloudWatch Generative AI Observability Preview
AWS introduces CloudWatch generative AI observability to monitor and troubleshoot complex AI applications, providing end-to-end tracing across LLMs and agents.
Amazon Web Services (AWS) has unveiled a new generative AI observability feature for Amazon CloudWatch, designed to help organizations monitor and troubleshoot complex AI applications. The preview, announced in a blog post, addresses the challenges developers face when deploying large language models (LLMs) and AI agents.
Key Features
- End-to-end tracing: Provides visibility across LLMs, agents, knowledge bases, and tools.
- Flexible deployment: Works with Amazon Bedrock AgentCore, EKS, ECS, and on-premises infrastructure.
- OpenTelemetry support: Compatible with frameworks like Strands Agents, LangGraph, and CrewAI.
- Integration with CloudWatch: Leverages existing features like Application Signals, Alarms, and Dashboards.
Implementation Scenarios
The blog outlines two deployment scenarios:
- Agents hosted on Amazon Bedrock AgentCore runtime: Uses the AWS Distro for OpenTelemetry (ADOT) SDK for automatic instrumentation.
- Agents running outside AgentCore: Requires manual setup of OTEL environment variables.
Observability Dashboard
The CloudWatch console now includes a GenAI Observability (Preview) section with:
- Model Invocations dashboard: Tracks metrics like invocation count, token usage, and error rates.
- Bedrock AgentCore Agents dashboard: Monitors runtime metrics, sessions, and traces.
Benefits
- Centralized monitoring: View all AI agents in one place.
- Performance insights: Analyze latency, errors, and throttling.
- Troubleshooting: Drill down into individual traces and spans.
For more details, visit the official documentation.
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Alex Thompson
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Senior technology editor specializing in AI and machine learning content creation for 8 years. Former technical editor at AI Magazine, now provides technical documentation and content strategy services for multiple AI companies. Excels at transforming complex AI technical concepts into accessible content.