Custom autonomous systems · Mock demo
Lookout sits between your monitoring and your on-call rota. It groups alerts that share a cause, runs the runbooks you've approved for routine problems, and sends a person only what needs one, with the evidence attached. Every Friday it shows which alert rules create the most noise and proposes how to fix them.
The metric at the centre: alerts that reach a human. Lookout's job is to make that number small and keep it honest.
Doing this by hand first? Our guide to reducing alert fatigue in Prometheus and Alertmanager covers grouping, inhibition, routing, for: durations and the weekly noise review.
Mock demo · fictional company and data
Press Restart, then Next (or →). The run stops when it needs your approval: you play the human.
Beamreach
DiskPressure=False on all 3 nodes, evicted pods rescheduled, 5xx back to 0.1%. 29 alerts resolved. Opened PAY-2231 for the payments team: LOG_LEVEL=debug is still on and the disks will fill again in about 30 hours.bi_reader query on the replica, but it belongs to a finance report, so that's a person's call. Problem B is attached to OPS-1180 and silenced until Thursday.| disk_usage_80 | 212 fires in 4 weeks, 0 actions. Raise to 90% for 10 min; RB-12 handles the rest. | Tune |
| pod_restart_any | 96 fires, every one a rolling deploy. Alert only on more than 3 restarts in 10 min. | Tune |
| latency_p99_staging | 63 fires, staging only. Send to the ticket queue instead of the pager. | Reroute |
This is a mock scenario. The figures are design targets, not measured results.
Try what you already have first. Most teams can cut a lot of noise with the tools below, and some never need more. Product details were checked on each vendor's site in October 2026.
Free and already running for most Prometheus and Grafana users. Grouping (group_by), inhibit rules, routing non-urgent alerts to tickets and for: durations cut out repeat and symptom pages. Enough when you have one monitoring stack, consistent labels and someone with time to review rules. Our step-by-step guide covers it.
Intelligent Alert Grouping uses machine learning to merge related alerts into one incident, and Auto-Pause holds notifications for transient alerts so they can resolve on their own. Both come with the AIOps add-on, or as Signal Intelligence in PD Reliability Platform plans. Runbook Automation can run diagnostics and fixes from an incident. Enough when PagerDuty is already where every alert ends up.
Groups related alerts into cases, with Intelligent Correlation (machine learning that uses your service topology) or Pattern-based Correlation (rules you define). Enough when most of your monitoring already lives in Datadog.
An "agentic ITOps" platform (its own term) that correlates alerts from many monitoring and observability tools into incidents, with ServiceNow and Jira integrations. Enough when you're a large IT operations organisation with many tools and an ITSM process to feed.
On Opsgenie? Atlassian stopped selling it on 4 June 2025 and ends support on 5 April 2027; its alerting and on-call features move to Jira Service Management. Plan that migration before investing in Opsgenie-specific tuning.
That's what we build. See Custom Autonomous Systems Engineering.
By deciding what reaches a person. An AI triage agent groups alerts that share a cause, handles the routine ones with runbooks you've approved, and pages someone only for what's left, with the evidence already collected. It also reviews which alert rules create the most noise and proposes fixes, so the number of pages keeps falling.
Alertmanager grouping batches alerts that have the same values for the labels you choose, such as cluster and alertname. Correlation goes further and links alerts that look different but share a cause, for example a full disk and the pod evictions and 5xx errors it causes. Alertmanager's inhibit rules handle simple cause-and-symptom pairs (how to set them up); correlation tools and agents use timing, topology and history to handle the rest.
Only within limits. Lookout runs a runbook on its own only if your team has approved it for that, and only within the scope you set, such as one node at a time. After each run it checks the metric that raised the alert; if the metric hasn't recovered, it stops and pages a person with what it tried. Runbooks that can't be checked, undone or kept small stay with humans.
Lookout never silences or changes an alert rule on its own. Its weekly noise review proposes rule changes as pull requests that your team reviews and merges. After a change, it compares what fired with what was delivered, so a rule that went quiet when it shouldn't have shows up in the next review.
Count the alerts that reach a human each week, broken down by alert rule, and note for each page whether anyone acted on it. Rules that page often and rarely lead to action are your noise. Lookout is built around that single number, alerts that reach a human, and reports it weekly. The guide shows where to get the numbers in Prometheus and Alertmanager.
No. It sits between them. Alertmanager, Datadog, CloudWatch or Grafana still detect problems, and PagerDuty or your on-call tool still pages people. Lookout decides what gets passed on, what gets fixed by an approved runbook, and what gets attached to an existing ticket instead.
Lookout acts on its own only through runbooks your team has approved for that, within the limits you set, and each one is verifiable, reversible and contained. If a fix doesn't move the metric, it stops and pages a person. It never silences or changes an alert rule without approval, and after a change it checks that nothing important went quiet.
We build it around your monitoring, your runbooks and your rules for what may run unattended. Tell us how many pages your team got last week.
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