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.
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 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.
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.
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.
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 AfterShip object, modeled and query-ready the moment you connect.
It runs on your real AfterShip data (partial tracking updates, courier slug mismatches, stale checkpoints 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 →