Carrier rate increases hit 3 of your top 5 SKUs by volume last week. 34 shipments billed at expedited rates that were booked as ground. Your books still reflect last month's per-unit shipping cost, putting actual COGS $8,400 above what the P&L shows.
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 your ShipHero shipping charges, pick-and-pack fees, and order totals against your accounting and bank data, and flags the gaps before close. The COGS on your P&L matches what fulfillment actually cost, so you never explain a margin discrepancy you found too late.
It watches your warehouse inventory positions against order velocity by SKU. When a product is burning through stock faster than your purchase order lead time, it flags the SKU, the projected days of supply, and the revenue at risk, so you reorder before you lose the sale.
When your return rate moves on a SKU or product line, it surfaces the reason codes, the refund impact, and the pattern behind it. You find out the week it changes, not when the quarterly write-down hits the books.
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 ShipHero object, modeled and query-ready the moment you connect.
It runs on your real ShipHero account (partial shipments, return reason codes, multi-warehouse splits, 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 →