AI SRE
Reduce MTTR by connecting alerts, changes, and human insight with AI.
AI SRE understands your full delivery workflow across deployments, feature flags, infrastructure changes, and monitoring signals. Ask questions mid-incident for instant correlation, auto-documentation, and one-click remediation.
AI Scribe Automatic incident record across Slack, Zoom & Teams — zero manual note-taking.
AI Root Cause Analysis Correlates deploys, flags, infra & monitors in seconds to surface root cause instantly.
Automation Runbooks ServiceNow → Jira → GitHub → rollback → investigate in one chain. One-click remediation.
On-Call & Escalations Schedules, rotations & policies you own end-to-end — right person, every time.
AI SRE shifts the metrics that matter: fewer interruptions, less manual work, and more confident responders.
Responders start with a full picture: ownership, recent changes, similar past incidents, blast radius, and a suggested starting hypothesis. No more blank-screen triage at 2 a.m.
Responders get logs, metrics, recent changes, ownership, and past incidents in one place at alert time. No more dashboard hunting in the first 20 minutes of an incident.
The on-call engineer has enough context to act without immediately paging the service owner. Better information means fewer people pulled in unnecessarily.
The timeline, decisions, and action items are captured during the incident. Less writing after the fact, and the output is actually accurate.
Recurring patterns become easier to spot, so teams can fix the root cause instead of fighting the same fire again.
Deploys, flags, config changes, and infrastructure events are automatically correlated and attached at alert time, with no manual digging through change records.
Known failure patterns move from manual response to guided fixes, and eventually to full automation once trusted. Every incident handled consistently, not differently by whoever is on call.
Related alerts get grouped into real incidents. Fewer pages for the same problem, so on-call engineers spend time on real work, not duplicate notifications.
Complete incident response from detection through documentation and resolution, powered by AI and integrated with your existing tools.
Automatically correlates deploys, flags, and config changes to surface likely causes.
Builds a real-time, shared timeline from alerts, logs, chats, and meetings.
Routes incidents using live schedules, ownership, and severity policies.
Captures decisions, actions, and context automatically, eliminating retroactive RCA writing.
Safely rollback, mitigate, or fix forward with trusted automation.
Pulls in alerts, tickets, deploys, flags, and events from all your tools.
AI Scribe automatically documents incidents from Slack and Zoom, eliminating manual note-taking
Root cause analysis correlates changes across deployments, flags, and infrastructure in seconds
Automation runbooks standardize first response and reduce MTTR consistently
How teams use AI SRE to cut through noise, find cause, and respond safely.
Use change context to collapse duplicates and focus on the event that matters.
Use recent change context to narrow scope, identify what changed, and determine likely cause.
Let Scribe handle notes, decisions, and the action audit trail.
One-click runbooks to roll back, scale out, or toggle a feature flag.
“AI Scribe eliminated hours of manual postmortem writing. It captures everything from our Slack channels and generates comprehensive incident reports automatically.”
— Principal Engineer, SaaS Platform
“Change correlation is a game changer. Instead of manually checking deploy logs and feature flags during incidents, AI SRE surfaces exactly what changed and when.”
— Senior SRE, E-commerce
“Automation runbooks reduced our MTTR by 40%. Standard remediation steps now execute automatically, and our on-call team sleeps better.”
— Engineering Manager, Fintech
Connect your entire incident response stack: alerting, observability, communication, and ticketing, all working together with AI.
AI Site Reliability Engineering applies artificial intelligence and machine learning to automate and improve system reliability, monitoring, incident response, and operational tasks.
AI analyzes patterns in logs and metrics to detect anomalies faster, predicts potential failures before they occur, and suggests remediation steps based on historical incident data.
Traditional SRE relies on manual processes and rule-based automation, while AI SRE uses machine learning to adapt, predict issues, and automate complex decision-making at scale.
Common use cases include anomaly detection, predictive alerting, automated root cause analysis, capacity planning, intelligent incident triage, and self-healing systems.
No. Harness AI SRE connects to the alerting, observability, and ticketing tools you already run, so a small team can start with one service and a single use case such as change correlation or automated incident documentation, then expand coverage as trust builds.
Yes! Harness On-Call offers like-for-like functionality with PagerDuty, OpsGenie, and xMatters, including on-call scheduling and escalation support. We also make it easy to switch — you can quickly import your existing setup directly from PagerDuty, OpsGenie, or xMatters so you're up and running with minimal disruption.
Book a personalized demo and watch AI SRE triage a live incident, surface root cause from real change data, and run remediation across the tools your team already uses.