AI SRE Overview

AI SRE is an AI-powered incident response assistant that helps support engineers, production engineers, on-call engineers, application engineers, and SREs investigate and resolve production incidents faster.

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Overview

AI SRE investigates incidents by:

• Accessing code — Reviews code changes, commits, and correlates them with incidents
• Querying systems — Gathers evidence from telemetry, infrastructure, and knowledge bases
• Building evidence chains — Correlates findings across systems to identify root causes
• Delivering insights — Provides evidence-based conclusions with confidence levels and suggested actions

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Benefits

• Faster MTTR — AI SRE does the investigation work, reducing investigation time from hours to minutes
• Evidence-based — Uses facts from your systems, not speculation—every finding is backed by data from your code, logs, and metrics
• Works with your tools — No data migration required; connects to your existing stack
• Confidence levels — Clearly states certainty and what’s missing, so you know when to verify

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Architecture

1. You ask — Type questions about incidents in natural language
2. AI SRE works — Queries code repositories, scans logs and metrics, reviews recent changes, and correlates data across your stack
3. AI SRE correlates — Links findings across systems, builds evidence chains, and identifies root causes
4. AI SRE delivers — Provides evidence-based conclusions with confidence levels and actionable next steps
5. You resolve — Use insights to fix incidents faster

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Use cases

• “Explain the latest code change and its impact” — AI SRE reviews recent deployments and code changes, correlating with current system state to explain what changed and how it affects your services
• “Analyze this alert and summarize what’s happening” — AI SRE queries logs, metrics, and traces to understand the alert context, identifies affected services, and provides a clear summary of what’s broken
• “What caused this incident?” — AI SRE investigates by reviewing code changes, correlating with incident timeline, querying telemetry data, and building evidence chains to identify the root cause
• “Which team owns this service?” — AI SRE searches code repositories, reviews ownership patterns, and identifies team information from your knowledge base

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Data Security & Privacy

Your data is isolated per company, encrypted in transit and at rest, and never used for AI model training. Integrations use read-only access. See the Security & Privacy section for details.

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Limitations

• Read-only — AI SRE cannot make changes to your systems
• Data availability — Insights depend on connected integrations and data quality
• Confidence levels — Findings include confidence levels; always verify critical decisions
• Tool expertise — Works best with well-configured integrations and monitoring

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