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§ Agent · AfterShip

The AfterShip data agent that acts the way you would.

It keeps an eye on your AfterShip tracking data alongside your orders and carrier SLAs, 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 · #ops-alerts
⚠️ On-time delivery rate dropped to 74% this week, 112 shipments stuck in transit past SLA

Deliveries via your primary carrier fell 14 points below your 88% baseline, concentrated on East Coast lanes. 112 shipments have exceeded their estimated delivery date, representing roughly $47,000 in order value at risk of WISMO escalation.

Review & approve Dismiss
AfterShip Trackings + Couriers · joined to Shopify Orders · 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

FulfillmentACROSS YOUR SOURCES

Tie shipment performance back to orders and customer impact

It joins your AfterShip tracking data to your order and revenue data, so you see on-time delivery, transit time, and exception rates alongside the dollar value and customer segment they affect. You walk into the ops review with carrier performance grounded in revenue impact, not just delivery percentages in isolation.

TrackingsCouriers
Exceptions

Catch a carrier exception spike before customers start calling

When exception rates for a carrier or lane break their trend, it tells you which shipments are stuck, how long they have been stalled, and how much order value is exposed. You hear about it while you can still reroute or proactively notify customers, not after the WISMO tickets pile up.

TrackingsCouriers
Carrier SLA

Spot when a carrier's transit times quietly drift

It watches actual transit times against promised delivery windows by carrier, lane, and origin, and flags when a courier's performance degrades against its historical baseline. A two-day carrier averaging three days on a key lane is a cost and experience problem you want to catch before the next contract review, not during it.

TrackingsCouriers
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 AfterShip object, modeled and query-ready the moment you connect.

Trackings
operationssupportcustomer
Couriers
operationsgeneral_data_storage

It runs on your real AfterShip data (partial tracking updates, courier slug mismatches, stale checkpoints 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 AfterShip, 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 AfterShip 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.
That reports on shipment metrics within AfterShip, when you log in. This watches continuously, reasons across your tracking data plus your orders and carrier contracts, and hands off to Fi to investigate why a lane is degrading, so you find out before the WISMO spike, not after.

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