News
Splunk agent observability: follow the work, then the bill
Splunk’s September release connects agent evaluation and cost monitoring. The useful test is whether a responder can trace spending back to an outcome.
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News
OpenTelemetry’s Kubernetes milestone matters for AI agent tracing
The stable Kubernetes attributes processor helps connect AI-agent traces to their workloads. Check the metadata joins before upgrading.
AIOps
NotebookLM for SRE: build a source-backed incident dossier
Use a curated notebook to compare runbooks and incident evidence, while keeping citations, freshness, and operational authority in view.
AIOps
GPU capacity depends on power and cooling
When inference slows without obvious application errors, test physical constraints alongside queueing, workload changes, and software regressions.
AIOps
Evaluate Claude Opus 4.6 for operational work
Test evidence retrieval, long-session consistency, tool boundaries, and cost before letting a model influence production changes.
AIOps
Connect PagerDuty, Jira, and Slack without losing incident state
A design guide for an incident assistant that handles duplicate events, partial failures, and reviewed AI summaries across tools.
How-To
Canary deployments: limit exposure and test the comparison
Choose representative traffic, define promotion criteria, and verify rollback compatibility before expanding a release.
How-To
Mean time to detect: calculate MTTD and expose measurement gaps
Use consistent onset and discovery timestamps, include customer-reported incidents, and interpret the average with its sample and uncertainty.
How-To
Metrics for observability: choose the question before the chart
Counters, gauges, and distributions answer different questions. Build metrics that preserve scope, denominators, and the evidence behind an incident.
AIOps
Python logging for operations: record failure and preserve the exit status
Use Python logging to capture useful context without swallowing errors, duplicating exceptions, or exposing sensitive data.
AIOps
Collect and aggregate operational data with Python
Validate the source data, preserve useful denominators, and distinguish a failed collection from a real zero before aggregating metrics.
How-To
Observability logs: preserve the events an investigation needs
Design logs around useful events, searchable context, and a collection path whose failures are visible.