Your top-converting flow placed 31% fewer messages this week and open rate fell from 52% to 38%, while attributed revenue dropped to $9,400 from a ~$12,900/wk baseline. Looks like a deliverability dip, not a demand dip.
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 Klaviyo's attributed revenue against the orders that actually closed in your store and CRM, so the number you put in the board deck is the revenue email earned, not the number the platform claims. You stop defending a figure two systems disagree on.
When opens, clicks, or placed-message rates break their trend on a key flow, it tells you which flow is affected and how much revenue is at risk, looks at what changed, and lines up the fix for you to approve. You hear about it this week, not after the month's numbers come in soft.
When a list's engaged share drops or unsubscribes spike after a send, it surfaces which segments are fading and which campaigns triggered it, so you can rework the cadence before you train inboxes to ignore you. The sender reputation you've spent months building stays intact.
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 Klaviyo object, modeled and query-ready the moment you connect.
It runs on your real Klaviyo account (test sends, suppressed profiles, half-built flows 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 →