IT playbook · AI Employee: Ivy

Outage Detection & Bulk Tagging

Outages triaged and tagged within 3 minutes of detection

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The problem

Something breaks in production. Ten minutes later, tickets start rolling in — different users, different words, same root cause. The support queue explodes. Every ticket takes a triage touch before anyone realises they're all the same incident. Meanwhile the incident-response channel doesn't know that customer-facing volume is spiking. Coordination between the outage and the tickets it generates is table stakes; almost nobody has it.

At a glance
Trigger
Monitor event
Approvals
Incident commander approval on major outage
What it does
Writes to your systems
Systems
Datadog · PagerDuty · Ticketing
How it feels in production

An hour-by-hour walkthrough.

3:14pm. Datadog fires an alert: latency spike on the payments API. Incident opened in Incident.io. 3:16pm. First customer reports "payments failing". Ivy sees the ticket in Zendesk, matches the description against active incidents, tags it as `INC-4211` — the payments incident. 3:18pm. Nine more tickets arrive. Ivy tags each automatically. Posts to the incident channel: "12 customer tickets so far. Sample subjects: 'checkout failing', 'invoice error 500', 'card processing hung'. Suggested response template: [link]." 3:20pm. Support lead uses the suggested response template + Ivy's bulk-reply action: 42 affected customers get a proactive same-message update ("we're aware, working on it, will update every 15 min"). Sets follow-up cadence. 3:47pm. Incident resolved. Ivy pushes the resolution message to all tagged tickets. Tickets marked resolved. Post-incident: incident report auto-populates the customer-impact section ("12 direct tickets, 42 impacted customers reached out or matched"). No manual reconciliation. If a ticket doesn't match any active incident, it flows to normal triage — the incident-detection is additive, not lossy.
How it works

Step by step.

  1. 01

    Ingest outage detection

    Datadog / PagerDuty / Grafana / SIEM webhook fires. Ivy reads the incident: affected service, severity, symptom pattern, on-call assignment.

    Datadog · PagerDuty · Grafana · Incident.io
  2. 02

    Tag related tickets

    Match incoming and existing tickets against the incident's symptom pattern using text similarity + timing window. Auto-tag matches with the incident ID.

    Zendesk · Freshdesk · Front · Intercom
  3. 03

    Post status update to affected channels

    Post to the incident Slack channel with ticket volume + top symptom subjects. Post to customer-status page if configured. Post to internal announce channel for exec awareness.

    Slack · Status page · Teams
  4. 04

    Open incident bridge

    Zoom / Google Meet bridge with the on-call team. Bridge link posted in every notification so responders can join instantly.

    Zoom · Google Meet · Slack
Systems and wiring

What you connect to make this run.

Datadog · PagerDuty · Grafana · Prometheus

trigger

Alert webhook fires the playbook. Signed payload verified; incident ID extracted or new one created.

Zendesk · Freshdesk · Front · Intercom

read+write

Read incoming tickets in real time; write tag + bulk-reply on match. Idempotent by ticket ID.

Incident.io · PagerDuty · custom incident-management

read+write

Read active incident list for matching; write ticket-impact stats to incident record on resolution.

What changes

Before and after, honestly.

Time from ticket arrival to matched to incident
Before
20-60 minutes (triage backlog)
After
Under 2 minutes (auto-match on arrival)
% of impacted customers reached proactively
Before
10-30% (support triages one at a time)
After
> 90% (bulk-reply to all tagged tickets)
Support-team overtime during incident
Before
4-8 hours of parallel triage work
After
30-60 minutes (Ivy handles the routing, humans handle exceptions)
Post-incident-report accuracy on customer impact
Before
Manual reconciliation; often wildly off
After
Exact — Ivy tagged every affected ticket
Related

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Get Kubernetes Cluster Info from Datadog

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Proactive Device Health

Adjacent — device outages get the same detection pattern.

Frequently asked

Answers about this playbook.

How does Ivy know a ticket relates to an incident?

Text similarity between the ticket subject/body and the incident symptoms + timing (incident opened within past N minutes). Threshold configurable. Low-confidence matches queue for human confirmation.

What if the incident is scheduled maintenance, not a real outage?

Scheduled-maintenance flag on the incident tells Ivy to route customer tickets to a maintenance-specific reply template. Same tagging + bulk-reply, different message.

Can we override an auto-tag?

Yes — support agent can un-tag a ticket if it turns out to be unrelated. Reversal logged; feeds back into matching accuracy over time.

Does this work with our on-call rotation?

Yes — Ivy respects PagerDuty rotation for who to page. Incident-channel invites go to the current on-call + team lead.

What about incidents affecting only some customers?

Configurable per-service scoping. If the incident is scoped to a specific region or customer tier, Ivy tags only tickets from affected customers. Others flow normally.

See it run on your data.

Free plan, no credit card. Connect the systems this playbook needs and run it against a past event first.

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