Your iOS Channels with active opt-in status dropped from 52,300 to 42,900 over the last 30 days, well below your ~2% monthly churn baseline. The decline is concentrated in Named Users acquired through the Q1 onboarding campaign. Android opt-in held steady at 71%.
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 Airship Named Users and Channels to your product usage and revenue data, so you see which push segments actually produce activated users and downstream spend, not just delivery counts. When a segment that used to drive engagement goes quiet, you find out before the cohort churns.
When opt-in rates on a device type or tag group start dropping faster than their baseline, it tells you which Channels are affected, how many Named Users you are losing reach to, and whether the decline maps to a specific app version or locale. You find out the week it shifts, not when the next campaign underperforms.
It watches your Segments and Lists over time. When a List shrinks, a Segment's composition drifts from its original targeting intent, or Channel Tags fall out of sync with your naming conventions, it flags the gap and lines up the cleanup for you to approve before the next send targets the wrong audience.
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 Airship object, modeled and query-ready the moment you connect.
It runs on your real Airship project (untagged channels, dormant named users, orphaned segments 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 →