Today's events partition wrote 612K rows against a ~1.04M daily baseline, and 3 new columns appeared that your schema doesn't map. Looks like a truncated upstream export.
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 checks the rows and partitions landing in your buckets against the warehouse tables they load and the source systems that produced them, then flags the gaps before your models run. You catch a truncated export or a missed partition while it's still a file in S3, not after it's poisoned every dashboard downstream.
When a new column appears, a type shifts, or a field your pipeline depends on goes missing, it tells you which objects changed and what reads from them downstream. You hear about the drift while you can still adjust the mapping, instead of debugging a failed dbt run at 7am.
It learns the cadence of each prefix and watches for the file that should have arrived and didn't, or arrived far smaller than usual. You find out a feed is stale before the stale numbers reach anyone who'd act on them.
Beyond alerts and write-backs, an agent can run arbitrary Python, so it can do whatever the task actually requires: re-trigger a load, parse a malformed file, reshape a partition, or wire into your own orchestration. 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 Amazon S3 object, modeled and query-ready the moment you connect.
It runs on your real buckets (mixed file formats, half-documented prefixes, columns nobody mapped yet), 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 →