Distributed tracing: choose the first request path to instrument
Start tracing with a customer-critical path, then test propagation, sampling, and whether the trace supports a real investigation.
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Start tracing with a customer-critical path, then test propagation, sampling, and whether the trace supports a real investigation.
Understand spans, parent-child relationships, retries, and missing evidence so traces support a careful diagnosis.
Use instrumentation and context propagation to connect the evidence in an investigation.
The stable Kubernetes attributes processor helps connect AI-agent traces to their workloads. Check the metadata joins before upgrading.
Locate the delay, choose a bounded intervention, and measure recovery quality alongside elapsed time.
Splunk’s September release connects agent evaluation and cost monitoring. The useful test is whether a responder can trace spending back to an outcome.
Account for context limits, retries, latency, and the work behind a useful result.
When inference slows without obvious application errors, test physical constraints alongside queueing, workload changes, and software regressions.
Adapt a complete SLO record, worked budget examples, and an exception log without confusing bad requests with minutes of downtime.
Make a useful AI request without assuming that tool approval, anonymization, or a training opt-out settles every data-handling question.
Follow evidence through technical failure and incident response, then test whether the proposed correction changes the mechanism.
Counters, gauges, and distributions answer different questions. Build metrics that preserve scope, denominators, and the evidence behind an incident.