PagerDuty logo
§ Agent · PagerDuty

The PagerDuty data agent that acts the way you would.

It keeps an eye on your PagerDuty incidents and alerts alongside the rest of your stack, on a schedule you set or whenever fresh data lands. When a pattern needs attention, it tells you, or handles it the way you'd want.

D
DefiniteAPP9:14 AM · #incident-watch
⚠️ checkout-api drove 41% of high-urgency incidents this week, all post-deploy

9 high-urgency incidents on checkout-api in the last 7 days, every one inside 30 minutes of a deploy, well above its ~2/wk baseline. MTTA is also up 3x on this service.

Review & approve Dismiss
PagerDuty Incident + Alert + Service · joined to deploy log + service metrics · audit log

How an agent works

An agent watches one thing and acts on it. Not a workflow, just a standing watch that usually does nothing and acts the moment it should.

◄ repeats on the schedule you set ►

You stay in control

An agent does what you'd do, and only what you've authorized.

The same trusted numbers

It acts on the same governed metrics as your dashboards, and every action is logged and traceable.

You approve anything that writes

It alerts and recommends on its own; anything that changes data is yours to approve.

Try it on a test channel first

Point a new agent at a throwaway channel and watch its judgment before it touches anything real.

No false alarms

It remembers what it already flagged and waits before acting again, so it won't alert you about the same thing twice.

What you can put an agent on

CorrelateACROSS YOUR SOURCES

Tie noisy incidents back to the deploy and the service that caused them

It joins your PagerDuty incidents and alerts to your deploy log and the affected service's telemetry, so a spike isn't just a pager going off, it's a service, a release, and a blast radius you can see. You find out which change is generating the noise before the on-call does it by hand.

IncidentAlertService
Recurrence

Catch the same incident reopening before it becomes a pattern

When a service starts re-triggering the same alert or an incident keeps reopening, it tells you which service, how often, and how the trend broke, instead of letting it hide inside a busy week of pages. You see the flapping early, while it's still one annoyance and not a postmortem.

IncidentIncident ActivityAlert
Response

Watch acknowledgement and escalation for the misses

It keeps an eye on how long alerts sit before someone acks them and where escalations are firing, and flags the services where response time is sliding. You catch a coverage gap or a runaway escalation policy before it shows up in next quarter's reliability review.

AlertNotificationService
Custom

Run any Python it needs to get the job done

Beyond alerts and write-backs, an agent can run arbitrary Python, so it can do whatever the task actually requires: call an API, kick off a job, reshape the data, or wire into your own tooling. The action space is yours to define.

Why not just build it yourself?

You could rig one of these with a cron job and a Slack webhook in an afternoon. The watching is the easy part. Here's what you'd own forever, and don't, here:

  • The cross-source join: not one tool's data, but it reconciled against the rest of your stack
  • A trusted, consistent metric: the same number your dashboards use
  • The investigation into why, when something fires
  • A full audit trail of everything it did
  • The upkeep, when the schema drifts or the script breaks at 2am

The data it works from

Every PagerDuty object, modeled and query-ready the moment you connect.

Incident
infrastructure_devopsoperations
Incident Activity
infrastructure_devopsoperations
Alert
infrastructure_devops
Service
infrastructure_devopsoperations
Notification
infrastructure_devopsoperations

It runs on your real PagerDuty account (test alerts, auto-resolved noise, services nobody renamed and all), not a tidy demo.

Where it acts

Slack

A message in the channel you choose, with the context and a button to act on it.

Email

A summary in the inbox of the people who need to see it.

Webhook

A payload to your own systems, to wire the agent into whatever you already run.

Warehouse write-back

A flag written back to your warehouse for everything downstream to pick up.

Hand off to Fi

Kick the question to Fi to investigate the why and propose the fix.

MCP

Expose it to your own agents and tools over MCP, and drive it from your stack.

Run it in your own VPC or fully self-hosted. Everything it does is pure SQL and Python you can inspect.

Build your agents with Fi

Fi is your AI analyst. It helps you build and customize everything in Definite, including the agents that watch and act.

Fi

Your AI analyst. Ask questions in plain English, and let it help you build and customize everything in Definite, including your agents.

Meet Fi →

Agents

The watchers and actors. Once you've built one, it runs on its own, keeping an eye on what matters and acting the way you would.

Autonomous agents →

Get started

  1. 1Connect PagerDuty, and the sources it needs to reconcile against. Synced and modeled in an afternoon.
  2. 2See the numbers tie out to what you already trust.
  3. 3Put an agent on one thing you can't afford to miss. Fi helps you build it.
§ FAQ

Common questions

You set the schedule, and it also re-checks whenever fresh PagerDuty data lands. Each agent watches the one thing you point it at, nothing else.
It alerts and recommends on its own. Anything that writes, whether to a tool, your warehouse, or a customer, is yours to approve. You can also point a new agent at a test channel first and watch its judgment before it touches anything real.
When something fires, it can hand off to Fi to investigate, drilling into the data it has across your connected sources to find what's behind the move, and showing its work.

Your answer engine
is one afternoon away.

Book a 30-minute call and watch us build your first dashboard live, with your own data.