Autonomous AI SRE
for Production

The agent continuously analyzes metrics, traces, and logs across your entire stack using ML-based anomaly detection. It builds dynamic baselines for each service and fires only when signals deviate from expected patterns — not on static thresholds — so it catches real incidents while ignoring routine traffic fluctuations.

The agent can scale Kubernetes pods, restart crashed services, roll back deployments, update runtime configuration, flush queues, and execute any runbook you define. All actions are audited, logged, and configurable with approval gates so you retain full control over what it is allowed to do autonomously.

Yes. Every action the agent takes is gated by configurable approval policies — you choose which remediations run automatically versus which require human sign-off. A full audit trail captures every decision and action with timestamps, so you always know exactly what the agent did and why.

After each incident the agent runs an automated post-mortem: it updates its signal correlation model, refines the runbook it executed, and records the root cause pattern. Over time this reduces false positives and improves mean time to remediation as the agent recognises recurring failure modes faster.

Fully. You define permission scopes per service and environment — for example, the agent may auto-scale pods in production but requires a Slack approval to roll back a deployment. You can also put the agent into observe-only mode where it diagnoses and recommends but never acts without explicit approval.