Accelerate incident response with AI agents

The Problem

• Alert fatigue is rampant — engineering teams receive hundreds of monitoring alerts daily from tools like PagerDuty, Datadog, and CloudWatch, most of which are noise or duplicates. When everything is urgent, nothing is, and real incidents get lost in the flood.
• On-call engineers manually correlate alerts across multiple systems — checking dashboards, querying logs, and cross-referencing deployment timelines — just to understand if alerts are related. This detective work eats up the critical first minutes of an incident when speed matters most.
• Runbooks exist in wikis and Notion docs but are outdated, incomplete, or inconsistently followed under the pressure of a live incident. Engineers skip steps, improvise fixes, and the same incident types get handled differently each time depending on who's on call.
• Mean time to resolution is too high because diagnosis and escalation are manual and sequential. By the time the right engineer with the right context is looped in, customers have been impacted for 30 minutes or more, violating SLAs and eroding trust.

How It Works

1. 1Connect the agent to your monitoring and alerting stack — PagerDuty, Datadog, Grafana, AWS CloudWatch, Sentry, or any tool with webhook or API support. The agent ingests alerts in real-time and begins correlation immediately.
2. 2When alerts fire, the agent automatically correlates them by service, time window, and dependency graph to identify the scope and likely blast radius of the incident. It distinguishes between a single service degradation and a cascading multi-service failure within seconds.
3. 3The agent runs diagnostic checks defined in your runbooks — querying service health endpoints, checking database connection pools, reviewing recent deployment diffs, and analyzing error rate patterns. If a known remediation exists, it executes automatically with appropriate safeguards.
4. 4If the automated fix fails or the incident doesn't match a known pattern, the agent escalates to the on-call engineer with a full context package: correlated alerts, diagnostic results, hypothesized root cause, and a timeline of what's already been tried.

Results

• Mean time to resolution drops by 70% or more because the agent handles triage, correlation, and initial diagnosis in seconds rather than the 15-30 minutes it takes a human to context-switch, log in, and start investigating.
• Automated alert correlation eliminates noise by grouping related alerts into a single incident with a clear scope assessment. On-call engineers see one actionable incident instead of 47 individual alerts, dramatically reducing cognitive load.
• Runbooks are executed consistently and completely every single time, regardless of who's on call or what time of day it is. No more skipped steps, improvised fixes, or forgotten rollback procedures during high-stress incidents.
• When escalation is needed, on-call engineers receive full diagnosis context — what triggered the incident, what the agent already checked, what it tried, and what it thinks the root cause is. Engineers start at step 5 instead of step 1.