62% faster MTTR on P1 incidents at a global tech firm — AIOps + agentic response
A global tech firm's SRE org was drowning in alerts and losing senior engineers to on-call fatigue. pronix.ai deployed an AIOps + agentic incident-response layer that correlated signals across Datadog, ServiceNow ITOM and PagerDuty — 62% faster P1 MTTR, 47% fewer false-positive pages, and post-incident summaries drafted before the war room ended.
- Client
- Global technology firm, 12,000 engineers
- Industry
- Enterprise
- Platform
- ServiceNow ITOM · PagerDuty · Datadog · Kore.ai Agent Platform · Amazon Bedrock
*Representative outcome; results vary by client, scope and platform configuration.
The challenge
The firm generated 1.4M alerts/month across observability tools with a 22% false-positive page rate on P1s. P1 MTTR averaged 78 minutes, and post-incident reviews took 5–8 business days to publish. On-call attrition among senior SREs was 34% year-over-year — the single biggest talent risk in the org.
Our approach
Signal correlation before human paging
An AIOps layer clustered related Datadog / ServiceNow ITOM / cloud-native signals into single incidents with a confidence score. Low-confidence noise never paged a human.
Agentic first-responder runbooks
Bounded response agents executed known-good runbooks — restart, failover, rollback, autoscale nudge — against a small set of blessed actions, each idempotent, logged, and reversible. Human on-call stayed in the loop and could veto in one click.
War-room copilot
During P1s, an in-channel copilot pulled deploy diffs, related tickets, similar past incidents and current telemetry into a live briefing view — commander stopped chasing tabs.
Drafted post-incident summaries
The agent drafted a blameless post-incident summary — timeline, contributing factors, corrective actions — before the war room disbanded. SRE lead reviewed and published in hours instead of days.
Guardrails on every automated action
Automated actions capped by blast radius, environment tier and change-freeze windows. Anything outside the envelope escalated to a named human owner instead of executing.
Feedback loop back into detection
Every closed incident fed labeled data back into the correlation and suppression models — false-positive rate dropped week over week for the first six months.
“The measurable win was MTTR. The real win was that our best SREs stopped quitting.”
Illustrative case study. Scenarios, metrics, quotes and client details are representative composites based on Pronix engagements and industry benchmarks unless a named client is shown with written consent. Outcomes vary by client, scope, data quality and platform configuration. Nothing on this page is a guarantee, warranty or professional advice. See our Terms of Use for the full disclaimer.
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