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Observability patterns for AI agents on Amazon EKS
Choosing between decentralized or centralized observability patterns for AI agents on EKS depends on whether you prefer per-agent autonomy or fleet-wide telemetry control.
What: AWS engineers demonstrate two OpenTelemetry-based patterns for monitoring AI agents using EKS, Bifrost as an LLM gateway, and Langfuse as a backend, comparing direct export (decentralized) versus using an OTEL Collector (centralized).
Why it matters: Standardized observability is essential for debugging non-deterministic AI agent behavior, specifically reasoning chains and token cost attribution.
Takeaway: Start with the decentralized pattern for a small number of agents and migrate to a centralized OTEL Collector when you need consistent fleet-wide sampling and multi-backend routing.
Deep dive
- The decentralized pattern exports traces directly from agents to Langfuse, reducing infrastructure complexity.
- The centralized pattern routes traces through an OTEL Collector, allowing for unified sampling, batching, and multi-backend configuration.
- Trace correlation relies on W3C traceparent headers passed between agent and gateway.
- Both patterns utilize OpenTelemetry GenAI semantic conventions for token and cost metadata.
- Bifrost provides correlated spans for Amazon Bedrock model invocations.
- Strands Agents SDK provides automatic instrumentation for reasoning steps and tool calls.
Decoder
- Bifrost: An LLM gateway that routes requests to models (like Amazon Bedrock) while injecting correlation IDs for telemetry.
- Traceparent: A W3C header used to propagate the context of a distributed trace between services.
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