AI DevOps
Your infrastructure now has a night shift
AI DevOps is an autonomous operations layer: it monitors everything, fixes the routine, escalates the novel — with a diagnosis, not just an alert — and documents every action it takes.
94%
of incidents resolved without human action
38s
median time from anomaly to diagnosis
0
maintenance windows you have to plan
31%
average infrastructure cost saved by right-sizing
The autonomy loop
Observe. Diagnose. Act. Explain.
The same loop a senior SRE runs — executed continuously, across your entire estate, with receipts.
Observe
Metrics, logs, traces, and synthetic checks across every app and node — collected automatically, no agent configuration.
Diagnose
Anomalies are correlated across layers into a root cause: not '500s are up' but 'the payment webhook is timing out because the queue is backed up'.
Act
Routine fixes execute autonomously under your pre-set rules: restart, scale, failover, patch, rollback. Novel issues escalate with a diagnosis attached.
Explain
Every action is logged with reasoning, before/after evidence, and rollback paths. Your morning digest reads like a great SRE's handover note.
What it handles
The pager duties nobody will miss
Predictive scaling
Learns your traffic patterns — campaign spikes, seasonal cycles, exam weeks — and provisions capacity before demand arrives, releasing it after.
Zero-downtime updates
Application and OS updates are tested against your configuration, rolled out progressively in safe windows, and reverted automatically on regression.
Self-healing
Crashed services, full disks, stuck queues, expiring certificates, memory leaks — detected and remediated under pre-set rules you control.
Cost optimization
Continuous right-sizing recommendations with projected savings; approve once and the platform applies them with zero downtime.
Drift correction
Configuration drift from manual changes or failed updates is detected and reconciled back to the approved baseline — with a note about what changed.
Runbook automation
Teach it your procedures once — in plain language — and they become automated runbooks triggered by conditions or a chat command.
Integrations
Plugged into the tools you already use
Digests in Slack or Teams. Escalations to PagerDuty. Deploy triggers from GitHub or GitLab. Tickets in Jira or Linear. Metrics exported to Grafana. AI DevOps joins your workflow — it doesn't replace it.
Ask it anything, anywhere: “@anystacks why was checkout slow at 9am?” gets a root-cause answer in your team chat, with graphs attached.
Get started
Retire the 3am page
We'll show you AI DevOps running against a live environment — and what your team's mornings look like after.