Your sales pipeline doc has 34 deals that no longer match HubSpot stage or amount. 12 moved to Closed Won in the CRM but still show as Negotiation in Coda, totaling $218K in misreported pipeline.
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 Coda table records against the CRM, billing system, or database they were pulled from, and flags the rows that no longer match. You find out about the drift before someone makes a decision on stale data, not after.
When a doc gets shared to someone outside the expected group, or when public link access appears on a sensitive document, it tells you which docs are affected and who has access, so you can tighten permissions before anything leaks.
It watches document and page activity across your workspace and flags the ones going stale, the tracker nobody updated, the runbook that lost its owner, the formula that stopped returning useful values. You get a weekly rollup instead of discovering it mid-quarter review.
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 Coda object, modeled and query-ready the moment you connect.
It runs on your real Coda workspace (half-finished docs, orphaned tables, test pages 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 →