The 5-email welcome flow is converting at 1.2% vs. your 2.1% trailing average. Drop started June 3 after the subject line change on email 2; open rate on that step fell from 38% to 19%. Subscribers entering the series are up 12%, so the list is healthy.
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 Sailthru purchase events and campaign engagement back to your source of truth for revenue, so you can show the CFO a blended CAC that accounts for lifecycle email, not just the number Sailthru self-reports.
When open rates, click rates, or unsubscribe rates break their trend on a campaign or audience segment, it surfaces which list, which blast, and how many subscribers are affected, so you can fix the creative or the cadence before the list degrades.
It tracks list growth, opt-out velocity, and segment overlap across your audience lists, and tells you when a segment is shrinking faster than it should or when two lists have drifted into near-total overlap, wasting send volume.
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 Sailthru object, modeled and query-ready the moment you connect.
It runs on your real Sailthru account (test blasts, suppression lists, partial imports 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 →