Your main trial signup form went from 412 completions/wk baseline to 284 over the last 7 days. Drop-off is concentrated on the company-size field. Projected activation loss based on your 48% trial-to-active rate: ~140 fewer activated accounts this cycle.
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 Jotform submission data with product usage and revenue, so you can see which signup forms, lead-capture flows, or onboarding surveys actually produce activated accounts and paying customers, not just completion counts.
When completion rates on a form break their trend, it tells you which form, which field, and how many respondents you lost compared to baseline, so you can fix the friction before it compounds across a full launch cycle.
It watches for shifts in who is filling out your forms, segments respondents by behavior across submissions, and flags when a segment that historically converts starts showing up more or less often, so product and growth know where to double down.
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 Jotform object, modeled and query-ready the moment you connect.
It runs on your real Jotform account (partial submissions, test entries, duplicate respondents 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 →