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.
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.
An agent does what you'd do, and only what you've authorized.
It acts on the same governed metrics as your dashboards, and every action is logged and traceable.
It alerts and recommends on its own; anything that changes data is yours to approve.
Point a new agent at a throwaway channel and watch its judgment before it touches anything real.
It remembers what it already flagged and waits before acting again, so it won't alert you about the same thing twice.
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.
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.
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.
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.
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:
Every PagerDuty object, modeled and query-ready the moment you connect.
It runs on your real PagerDuty account (test alerts, auto-resolved noise, services nobody renamed and all), not a tidy demo.
A message in the channel you choose, with the context and a button to act on it.
A summary in the inbox of the people who need to see it.
A payload to your own systems, to wire the agent into whatever you already run.
A flag written back to your warehouse for everything downstream to pick up.
Kick the question to Fi to investigate the why and propose the fix.
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.
Fi is your AI analyst. It helps you build and customize everything in Definite, including the agents that watch and act.
Your AI analyst. Ask questions in plain English, and let it help you build and customize everything in Definite, including your agents.
Meet Fi →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 →