These Records in the Deals base haven't moved stages or received a note since early June, well above your ~5-day touch cadence. 11 of them have close dates inside 3 weeks.
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 Airtable records to the systems they track (your CRM, your billing, your project tools) and flags where a record says done but the source never moved, or where real progress hasn't made it back to the base. You catch the gap between the tracker and reality before someone makes a decision on stale data.
When records in a critical table stop moving, miss a status update, or sit in the same stage past their target date, it tells you which ones, who owns them, and how long they've been frozen. You hear about the stall while there's still time to fix it, not when leadership asks why the tracker looks off.
When someone adds a field, renames a column, or changes a linked record structure, it flags the change and tells you which downstream automations or integrations are affected. You find out before the Monday report breaks, not after.
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 Airtable object, modeled and query-ready the moment you connect.
It runs on your real Airtable workspace (the abandoned bases, the test tables nobody cleaned up, the linked records that point nowhere), 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 →