Review volume on Hydrating Serum, Daily SPF, and Retinol PM jumped 74% week-over-week, with average rating falling from 4.5 to 3.9. Most negative reviews cite a packaging change shipped two weeks ago. These three SKUs account for 28% of monthly revenue.
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 Yotpo review data to your revenue source, so you can see which products have a sentiment problem that actually threatens the business, not just a low star average. You stop reacting to every negative review and start prioritizing the ones attached to real revenue.
When average rating or review volume breaks its trend on a product or collection, it tells you which SKUs moved, what reviewers are saying, and how much revenue those SKUs represent. You find out in hours, not when the PDP conversion rate has already fallen.
When open rates fall or unsubscribe volume spikes on your review request emails, it surfaces the cohort, the timing, and the campaign so you can tell whether the list is fatiguing or the send cadence changed. You protect the channel that feeds your UGC pipeline.
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 Yotpo object, modeled and query-ready the moment you connect.
It runs on your real Yotpo account (spam reviews, test orders, unsubscribe churn 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 →