The orders table hasn't gained a row since 02:00 UTC, against a normal ~1,400/hr. Downstream revenue models and 4 dashboards are now serving stale data.
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 watches your source MySQL/MariaDB tables against the modeled tables they feed downstream, and flags the moment row counts, totals, or freshness drift apart. You find out a sync stalled or a backfill double-counted before a stakeholder finds it in a dashboard.
When a table that should be growing stops, or an expected load window passes with no new rows, it tells you which table, how long it's been quiet, and what depends on it. You catch a stalled replica or a broken job at the source, not three layers down.
When a column is added, dropped, or retyped, or a new table or view appears, it surfaces the change and the models that reference it, so a quiet migration upstream doesn't break a transform you own. You see the drift while you can still plan for it.
Beyond alerts and write-backs, an agent can run arbitrary Python, so it can do whatever the task actually requires: call an API, trigger a re-sync, 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 MySQL/MariaDB object, modeled and query-ready the moment you connect.
It runs on your real MySQL/MariaDB instance (the legacy tables, the undocumented columns, the views nobody owns), not a tidy demo schema.
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 →