Infrastructure that fixes itself
The TigerOps AI SRE agent executes your runbooks, scales your infrastructure, rolls back bad deployments, and self-heals services — all without waking an engineer. 80% of incidents resolved autonomously.
What the AI agent can fix autonomously
TigerOps handles the full spectrum of remediation actions — from simple restarts to complex multi-step orchestrations.
Runbook Execution
Codify your incident playbooks once. The AI agent matches incidents to runbooks with confidence scoring and executes them automatically — with full audit trails.
Infrastructure Scaling
AI detects capacity issues before they cause user impact and proactively scales infrastructure — Kubernetes pods, autoscaling groups, database replicas.
Deployment Rollbacks
When a new deployment causes a performance regression or error spike, AI automatically rolls back to the last stable version — before on-call is paged.
Self-Healing Services
Services that detect and fix their own degradation — restarting crashed pods, clearing deadlocks, reconnecting dropped database connections, and more.
From detection to resolution — fully automated
Incident Detected & Diagnosed
AI SRE detects the anomaly, correlates signals, and identifies the root cause with a confidence score. Remediation candidate is selected.
Runbook Matched
The incident signature is matched against your runbook library. Best match is selected with a confidence score. Human approval gate is optional.
Remediation Executed
AI agent executes the runbook step-by-step with real-time monitoring. If any step fails or causes a regression, remediation is halted and a human is paged.
Resolution Verified
After remediation, AI monitors key metrics for 5 minutes to confirm the fix held. If metrics regress, a follow-up runbook is triggered or a human is notified.
Audit Trail & Learning
Complete audit trail logged: what was detected, what action was taken, who approved it, and what the outcome was. Runbook updated with outcome data.
AI with guardrails
Autonomous doesn't mean unchecked. Every action the AI takes is governed by policies you define, with full audit trails.
Approval Policies
Define which runbooks require human approval and which can be executed automatically based on confidence threshold, blast radius, and environment.
Blast Radius Limits
Set hard limits on what the AI agent can do — maximum instances to scale, deployments it can touch, and environments where autonomous action is disabled.
Rollback Safeguards
If any remediation step causes a secondary anomaly, the AI halts immediately, rolls back its changes, and escalates to a human with full context.
Full Audit Trail
Every automated action is logged with actor (AI agent), timestamp, decision rationale, confidence score, and outcome — ready for SOC 2 and compliance audits.
Before vs. after autonomous remediation
Our on-call rotation went from a nightmare to almost boring. 80% of our incidents are now handled automatically before anyone is even paged. The safeguards are solid — we defined our policies once and haven't had to think about it since.
Let AI handle the routine. You own the strategy.
Deploy the AI SRE agent and reclaim your on-call hours. Autonomous remediation with full guardrails and audit trails.