Measuring retry amplification in AI agent workflows
Connect logical tasks to every model and tool attempt, then measure cost and latency per accepted outcome without hiding failed or unknown work.
Guides, news and explanations about AI and reliable systems.
Connect logical tasks to every model and tool attempt, then measure cost and latency per accepted outcome without hiding failed or unknown work.
Tail sampling selects recorded traces by error, latency and other evidence. A checkout timeout shows how routing, waiting time and memory affect the trace a responder can retrieve.
A durable queue can preserve telemetry through an outage, then take longer to catch up. Arrival rate, export capacity and event age explain what recovery looks like.
Kubernetes attributes connect agent calls to pods, workloads and paging routes. A processor migration can break those connections while telemetry still flows.
Use instrumentation and context propagation to connect the evidence in an investigation.