Use CaseIncident Response

Autonomous incident response in 35 seconds

The TigerOps AI SRE agent detects incidents, correlates signals across your entire stack, identifies the root cause, executes remediation, and communicates to stakeholders — all without waking a single engineer.

60%reduction in MTTR for teams using autonomous incident response
60%
Reduction in MTTR
vs. industry average
80%
Incidents auto-resolved
without human intervention
35s
Average time to resolution
for known incident types
0
False positive pages
with AI signal correlation

Incident response is still mostly manual

~62 min
Average MTTR
for production incidents
30%
Alert noise
of alerts are actionable
3–5 tools
Context switches
per average incident investigation

Most teams still rely on paging an engineer, who then manually pieces together context from multiple tools before even starting to diagnose the problem. The first 30 minutes of every incident is pure toil.

How It Works

End-to-end autonomous incident handling

Every step from detection to post-mortem, handled by AI.

0s
01
AI detects the anomaly

Detection

Multi-signal anomaly detection identifies the issue before users are impacted. AI correlates metrics, traces, and logs to confirm a real incident — not a false positive.

p99 latency spike on api-gateway detected. 3.2σ above baseline. Error rate rising: 0.1% → 4.7%
4s
02
Signals cross-referenced

Correlation

The AI SRE agent correlates the anomaly across every upstream and downstream dependency — traces, infrastructure metrics, deployment events, and recent changes.

Correlating 6 dependent services, 3 database connections, 1 recent deployment (14 minutes ago)
11s
03
Root cause identified

Root Cause

AI pinpoints the exact root cause with a confidence score, matching the pattern against historical incidents and known failure signatures.

Root cause: connection pool exhaustion on postgres-primary. Confidence: 97.4%. Runbook match found.
23s
04
AI executes the fix

Remediation

The AI agent executes the matching runbook: scaling the connection pool, redistributing read traffic, and applying any necessary config changes — all without human intervention.

Scaling connection pool 150 → 400. Routing read queries to replica. Restart deferred.
28s
05
Stakeholders notified

Communication

A full incident summary is automatically posted to Slack and PagerDuty. On-call engineers get context-rich notifications — or none at all if the incident is fully resolved.

Slack notification sent. PagerDuty alert suppressed. Incident summary posted to #incidents.
35s
06
Auto-drafted and assigned

Post-Mortem

TigerOps auto-generates a post-mortem with timeline, contributing factors, remediation steps, and action items. Runbook is updated with the new pattern for future prevention.

Post-mortem drafted. Timeline: 28 seconds. Impact: 0 users. Runbook updated with new pattern.

Before vs. after TigerOps

The same incident. A completely different experience.

Aspect
Without TigerOps
With TigerOps
Detection
User reports issue → on-call paged → ~8 minutes
AI detects anomaly before user impact → 0 seconds
Triage
Engineer logs in, checks dashboards, gathers context → 15–30 min
AI correlates all signals automatically → 4 seconds
Root Cause
Manual investigation across tools → 30–90 min
AI identifies root cause with confidence score → 11 seconds
Remediation
Run playbook manually, coordinate changes → 20–60 min
AI executes runbook autonomously → 12 seconds
Communication
Manual status updates drafted and posted → 10–20 min
AI auto-posts summary to Slack and PagerDuty → immediate
Post-Mortem
Written days after incident, often skipped → hours
AI-drafted within minutes of resolution → automatic
60% reduction in MTTR

Stop fighting fires. Start preventing them.

Let the AI SRE agent handle routine incidents while your team focuses on reliability engineering that matters.