Understand what your systems are telling you, and what they are not.
Splunk’s September releases combine agent traces, evaluations and token-cost monitoring. A rejected incident summary shows how retries and human corrections affect the apparent saving.
Conflicting restart instructions show where a source-backed notebook helps: finding the relevant passages, dates and exceptions for the runbook owner.
Power and cooling affect the work a GPU pool can sustain. Device signals, request completions and queue growth help distinguish a physical limit from a workload change.
A rollback exception tests whether Opus 4.6 preserves decisive evidence across a long session, and whether the resulting answer arrives in time to help.
A reference design for duplicate deliveries and partial API success, with durable incident identifiers and summaries drawn from the resulting record.
Traffic assignment determines which behavior reaches a canary. Promotion also depends on the baseline, service objectives and state left behind by rollback.
Worked incident records explain the MTTD formula, uncertain onset times and the effect of including failures first reported by customers.
Counters, gauges and distributions preserve different information. A misleading latency trend shows why the counted requests matter as much as the chart.
A complete Python example sends the result to stdout, records an exception once and returns the failure status its scheduler needs.
A complete Python example combines request and error counts, while a failed regional collection explains why correct arithmetic can still produce an incomplete report.
An unfinished worker operation connects event fields, collection loss and search permissions to the responder’s next retry decision.
A deployment and a simultaneous dependency outage illustrate how AI-assisted observability can locate useful evidence and distinguish competing explanations.
