Net sales booked in Shopify came in $9,400 above what actually settled to your account, mostly refunds and a payout still in transit, well outside your usual sub-$1,000 swing.
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 reconciles the orders and payouts in Shopify against your accounting and bank data, and flags the gaps before close, so the revenue you report matches the cash that actually landed. You stop chasing a discrepancy you found a week too late.
When a product that's still selling drops below the cover you set, it tells you which SKU, which location, and how much revenue is exposed at your current sell-through. You reorder on the real number instead of finding out when the listing goes dark.
When recovered-checkout revenue slips or abandonment breaks its trend, it surfaces the dollars walking out the door and which step they're stalling at. You see the leak while you can still act on it, not in next month's recap.
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 Shopify object, modeled and query-ready the moment you connect.
It runs on your real Shopify store (refunds, partial fulfillments, test orders, and custom metafields 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 →