Observability patterns for AI agents on Amazon EKS
AWS Containers Blog describes two observability patterns for AI agents on Amazon EKS: a decentralized pattern where each agent exports traces straight to Langfuse, and a centralized pattern where traces pass through a shared OpenTelemetry Collector first. Both use the Strands Agents SDK for instrumentation, Bifrost as an LLM gateway routing to Amazon Bedrock, and OpenTelemetry GenAI conventions, with trace context propagated via traceparent headers. The post is cut off before its full results were shown in the source text.
What changes for platform teams
Platform teams choosing between the two patterns trade per-agent configuration sprawl against a shared Collector that must be sized and monitored as critical infrastructure. Adding a new telemetry backend is a Collector config change in the centralized pattern, and a change to every agent in the decentralized one.
Read the original at AWS Containers Blog ↗
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