Usage events for these accounts fell 40%+ over the past 14 days while their renewal dates are within 45 days. Three are enterprise tier. Historically, accounts with this pattern churn at 3x your baseline rate.
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 Totango health scores and usage events against your billing and product data, ranks accounts by risk and revenue exposure, and pushes a prioritized call list to your team each week, so CSMs know which accounts to reach out to before the renewal window opens.
When a customer's event activity drops below its own baseline, the agent surfaces who stopped logging in, which features they abandoned, and how far they are from renewal, so your team can intervene while there is still time to re-engage.
It watches for accounts whose person count or event volume is outgrowing their current tier, correlates that against contract terms from your billing system, and flags the ones worth an expansion conversation this quarter.
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 Totango object, modeled and query-ready the moment you connect.
It runs on your real Totango instance (stale health scores, orphaned accounts, duplicate contacts 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 →