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§ Agent · Square

The Square data agent that acts the way you would.

It keeps an eye on your Square data alongside your accounting and bank, on a schedule you set or whenever fresh data lands. When something needs attention, it tells you, or handles it the way you'd want.

D
DefiniteAPP9:14 AM · #finance-alerts
⚠️ Payout shortfall: $7,300 gap between Square orders and Friday deposit

Friday's bank deposit was $7,300 less than the settled Square orders for the week. $4,100 traces to held funds on disputed transactions at your Midtown location; the remaining $3,200 is timing on two batches that haven't cleared yet. Your books still show the full amount as received.

Review & approve Dismiss
Square Order + Payout + Location · reconciled to QuickBooks · audit log

How an agent works

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.

◄ repeats on the schedule you set ►

You stay in control

An agent does what you'd do, and only what you've authorized.

The same trusted numbers

It acts on the same governed metrics as your dashboards, and every action is logged and traceable.

You approve anything that writes

It alerts and recommends on its own; anything that changes data is yours to approve.

Try it on a test channel first

Point a new agent at a throwaway channel and watch its judgment before it touches anything real.

No false alarms

It remembers what it already flagged and waits before acting again, so it won't alert you about the same thing twice.

What you can put an agent on

ReconcileACROSS YOUR SOURCES

Tie your Square revenue to the bank and your books

It reconciles your Square orders and payouts against your accounting and bank data, and flags the gaps before close. The revenue on your P&L matches the cash that actually landed, so you never explain a discrepancy you found too late.

OrderPayout
Refunds

Catch a refund spike with the location and product behind it

When your refund rate breaks its trend, it surfaces which locations, products, and categories are driving it, with the dollar impact and the baseline it moved from. You find out the day it moves, not at month-end close.

RefundOrderLocationProduct
Inventory

Know when a stockout is coming before it hits

It watches inventory counts by location against your sales velocity. When a product is trending toward stockout faster than your reorder lead time, it flags the SKU, the location, and the projected days of supply left, so you reorder before the shelf is empty.

InventoryProductLocation
Custom

Run any Python it needs to get the job done

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.

Why not just build it yourself?

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:

  • The cross-source join: not one tool's data, but it reconciled against the rest of your stack
  • A trusted, consistent metric: the same number your dashboards use
  • The investigation into why, when something fires
  • A full audit trail of everything it did
  • The upkeep, when the schema drifts or the script breaks at 2am

The data it works from

Every Square object, modeled and query-ready the moment you connect.

Location
operations
Product
productrevenue_finance
Inventory
operationsrevenue_finance
Order
revenue_financecustomer
Refund
revenue_finance
Vendor
revenue_financeoperations
Payout
revenue_finance

It runs on your real Square account (voided transactions, multi-location quirks, held funds, and all), not a tidy demo.

Where it acts

Slack

A message in the channel you choose, with the context and a button to act on it.

Email

A summary in the inbox of the people who need to see it.

Webhook

A payload to your own systems, to wire the agent into whatever you already run.

Warehouse write-back

A flag written back to your warehouse for everything downstream to pick up.

Hand off to Fi

Kick the question to Fi to investigate the why and propose the fix.

MCP

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.

Build your agents with Fi

Fi is your AI analyst. It helps you build and customize everything in Definite, including the agents that watch and act.

Fi

Your AI analyst. Ask questions in plain English, and let it help you build and customize everything in Definite, including your agents.

Meet Fi →

Agents

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 →

Get started

  1. 1Connect Square, and the sources it needs to reconcile against. Synced and modeled in an afternoon.
  2. 2See the numbers tie out to what you already trust.
  3. 3Put an agent on one thing you can't afford to miss. Fi helps you build it.
§ FAQ

Common questions

You set the schedule, and it also re-checks whenever fresh Square data lands. Each agent watches the one thing you point it at, nothing else.
It alerts and recommends on its own. Anything that writes, whether to a tool, your warehouse, or a customer, is yours to approve. You can also point a new agent at a test channel first and watch its judgment before it touches anything real.
When something fires, it can hand off to Fi to investigate, drilling into the data it has across your connected sources to find what's behind the move, and showing its work.
Those show you what happened inside Square, when you go look. This watches continuously, reasons across Square plus your books and bank, and hands off to Fi to investigate why, so the gap between what Square settled and what the bank received never surprises you at close.

Your answer engine
is one afternoon away.

Book a 30-minute call and watch us build your first dashboard live, with your own data.