12 accounts averaging 40+ weekly active users fell below 10 WAU in the last 7 days. 8 of them have renewals in the next 60 days. That is 3x your normal weekly drop-off 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 Auth0 login activity to your billing data and flags accounts whose usage has fallen off a cliff relative to their baseline. You get the at-risk list ranked by ARR with days-to-renewal, not a dashboard you have to remember to check.
When new users sign up but never complete their second login, it tells you which accounts are stuck, how far they got, and which application they signed up through, so your team can reach out while the intent is still warm.
When failed login attempts spike or a new geography appears in the logs, it flags the affected accounts, the applications involved, and whether those accounts carry meaningful revenue, so you can prioritize response by business impact.
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 Auth0 object, modeled and query-ready the moment you connect.
It runs on your real Auth0 tenant (test users, blocked attempts, legacy connections 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 →