GuideAI in production
Evaluate AIOps tools with the work your responders actually do
Compare detection, correlation, investigation, and automation using your incidents, review costs, and failure paths.
Articles
In-depth explanations, practical guides and independent analysis. Choose a topic and format to find your next read.
Guides show how. Explainers unpack a concept. Commentary makes an argument. Explore Production notes.
GuideIncident response
Use AIOps to shorten the part of incident response that is slow
Locate the delay, choose a bounded intervention, and measure recovery quality alongside elapsed time.
GuideIncident response
Customer reliability engineering: connect the report to the service
Translate customer symptoms into an owned investigation while preserving impact, uncertainty, and a useful communication loop.
GuideObservability
Alert trust: decide which signals earn an interruption
Decide which alerts earn an interruption using urgency, reachable authority, coverage evidence, and a practical alert acceptance record.
GuidePlatform engineering
AI in release engineering: improve evidence without weakening the gate
Use models for test suggestions and change analysis while keeping artifact identity, promotion rules, and rollback checks enforceable.
GuideAI in production
Using ChatGPT at work: check the account, data, and destination
Make a useful AI request without assuming that tool approval, anonymization, or a training opt-out settles every data-handling question.
GuideIncident response
Incident authority: make the next mitigation decidable
Separate incident coordination from mitigation authority, compare competing actions, and use a worksheet that preserves decisions and verification.
GuidePlatform engineering
Canary deployments: limit exposure and test the comparison
Choose representative traffic, define promotion criteria, and verify rollback compatibility before expanding a release.
GuideIncident response
Run a post-incident review that explains the difficult decisions
Prepare evidence, reconstruct the timeline, examine contributing conditions, and choose follow-up proportionate to the incident.
GuideObservability
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.
GuideAI in production
AIOps Automated Remediation: A Practical SRE Workflow
Connect AI-assisted diagnosis to bounded runbooks, explicit approval gates, and independent recovery checks. Start with a practical SRE rollout checklist.
GuideObservability
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.