Acme Corp, Ridgeline, and Northstar all scored 0-6 on their latest NPS survey after being promoters last quarter. Acme and Ridgeline renew in 74 and 82 days. Comment themes are onboarding friction and response time. None are on this week's CSM call list.
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 AskNicely survey responses and contacts to the account, ARR, and renewal date in your CRM, so a promoter-to-detractor flip on an account renewing next quarter surfaces as a ranked call list, not a score nobody connected to revenue. You catch the sentiment shift while there's still time to save the renewal.
When your rolling NPS drifts below baseline, or a segment's detractor rate spikes, it tells you which contacts and comment themes are behind the shift, with enough context to decide whether it is a product issue, a support issue, or an onboarding gap. You find out the week it moves, not the quarter it shows up in the board deck.
It watches sent, delivered, opened, and responded counts. When your response rate falls below where you'd want it, or unsubscribes tick up past their trend, it flags which segments and delivery windows are slipping, so you fix the cadence before the feedback pipeline goes quiet on the accounts you need to hear from most.
Beyond alerts and write-backs, an agent can run arbitrary Python, so it can do whatever the task actually requires: call an API, push a call list to your CS tool, 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 AskNicely object, modeled and query-ready the moment you connect.
It runs on your real AskNicely data (incomplete responses, unsubscribed contacts, survey fatigue 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 →