ZaraIncident Responder
An AI agent that triages alerts by severity, coordinates escalation paths, documents incident timelines in real time, and produces post-mortems that actually prevent recurrence. Memory retains past incident patterns so diagnosis gets faster with every event.
The Problem
When everything is alerting, nothing is alerting.
Your monitoring stack sends 200 alerts a day. Most are noise. Some are warnings that could wait until morning. A few are critical — the kind that cost real money for every minute they go unaddressed. The problem is telling which is which at 3 AM when they all look the same in the notification feed.
Alert fatigue is well-documented and nearly universal. Teams start ignoring alerts because the signal-to-noise ratio is too low. They mute channels, snooze notifications, and create filters that occasionally filter out the one alert that actually mattered. The monitoring infrastructure works perfectly — it's the human response layer that breaks down.
When a real incident does get noticed, the response is often chaotic. Who's on call? Has anyone started investigating? What's the customer impact? Is this related to the deploy that went out two hours ago? These questions get answered eventually, but in the meantime, people are duplicating effort, working on the wrong things, or waiting for someone else to take the lead.
And after the incident, the post-mortem either doesn't happen or happens three weeks later when everyone's forgotten the details. The timeline is reconstructed from Slack messages and memory. Action items are written but rarely tracked. The same class of incident happens again six months later.
Zara handles the entire incident lifecycle — from alert triage through post-mortem — with persistent memory that turns every incident into a lesson the system actually retains.
How It Works
From alert to post-mortem. Automatically.
Zara runs on a Sonnet-class model for nuanced reasoning about severity, impact, and correlation. Here's the incident lifecycle:
Zulu ingests alerts from your monitoring stack — Datadog, PagerDuty, CloudWatch, Prometheus, custom webhooks — and classifies them by severity. Not just by the alert's own priority label, but by context: is this the third time this alert has fired today? Does it correlate with other active alerts? Does it match a known pattern from a previous incident? Noise gets suppressed. Signal gets escalated.
When an alert qualifies as an incident, Zulu initiates the escalation path — notifying the right on-call person, creating an incident channel, and posting an initial impact assessment. If the primary responder doesn't acknowledge within the configured window, Zulu escalates to the backup. No alert sits unacknowledged.
As the incident unfolds, Zulu documents the timeline automatically — alert fired, escalation sent, responder acknowledged, investigation started, root cause identified, mitigation applied, service restored. Every action is timestamped. When the post-mortem happens, the timeline is already written.
Zulu compares the current incident against its memory of past incidents. "This looks similar to the database connection exhaustion we saw three months ago — same symptoms, same time of day, same service." Historical context surfaces immediately, giving responders a head start on diagnosis instead of starting from scratch.
After resolution, Zulu produces a structured post-mortem — incident summary, timeline, root cause analysis, contributing factors, what went well, what didn't, and action items with owners. The post-mortem is generated within hours, not weeks, while the details are fresh and the data is available.
The OS Underneath
More than a model. An operating system.
Zara runs on Montebelle's agent operating system — infrastructure that gives it the persistent context and systematic verification that incident response demands.
Incident Memory
Zulu maintains a persistent record of every incident — symptoms, root causes, resolution steps, time-to-resolve, and contributing factors. This memory enables pattern matching that gets faster and more accurate over time. An agent that's seen 50 incidents in your environment diagnoses differently than one seeing its first. That institutional memory usually walks out the door when engineers leave. With Zulu, it stays.
Severity Verification
Before escalating, the OS verifies severity assessment against multiple signals — not just the alert label, but correlation with other alerts, historical false-positive rates for this monitor, current deployment state, and time-of-day traffic patterns. The goal is zero missed critical incidents and minimal false escalations. Both matter equally.
Fleet Learning
When the Montebelle fleet encounters new incident patterns, discovers better triage heuristics, or identifies common root causes across different environments — those learnings distribute to every Zulu agent. Your incident response benefits from patterns discovered across diverse infrastructure while your specific incident data stays completely confidential.
Ready to turn incidents into lessons instead of recurring nightmares?
Zara is one configuration of the Montebelle operations agent. Your version gets built around your monitoring stack, escalation policies, and incident response workflow.
Let's TalkFixed price. Two to four weeks. You own the agent.