Platform · AWS Bedrock

Observe AI agents that run on AWS Bedrock.

Your agent calls Bedrock — Claude, Nova, Titan, Mistral, Llama — for inference. Trefur captures each invocation as a span on the agent trace with tokens, latency, cost, and any tool-use cycle that came with it.

What you see.

Model ID and provider for each Bedrock invocation — Claude, Nova, Titan, Mistral, Llama.
Prompt tokens, completion tokens, total tokens, with USD attributed per Bedrock pricing.
Region and account ID per invocation, so you can split usage by environment.
Stream vs non-stream, with full completion captured as a single span.
Bedrock Converse tool-use cycles — tool requested, tool response, follow-up completion.
Throttle errors (429), validation errors, model-not-found errors, all on the span.
Retry attempts with back-off windows captured as child spans.

Why it matters.

For engineers

Point your agent's OpenTelemetry exporter at Trefur. Every InvokeModel and Converse call your instrumentation records shows up on the agent trace with tokens, latency, and cost.

For platform teams

Roll up Bedrock usage per account, per region, per model ID, per agent. Give finance a clean breakdown without joining CloudWatch logs to product telemetry by hand.

For business operators

Know which workflows depend on Bedrock and what they cost. Catch silent failover to a more expensive model. Tie model spend to ticket resolution or completion rate.

For SRE

Bedrock throttling and capacity errors land on the span as tool failures. Envelope alerts catch retry-rate spikes and latency walks before they reach customers.

How it works.

Your agent calls Bedrock through the AWS SDK (boto3, AWS SDK for JavaScript, etc.). Send its OpenTelemetry traces to Trefur and each InvokeModel or Converse call becomes a span. Spans use the OpenTelemetry GenAI shape so they land in the same trace as any other LLM call from a different vendor.

See what your Bedrock agents are actually doing.

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