14 subscriptions with metered components billed amounts that don't match the revenue recognized in your books, mostly mid-cycle upgrades and prorated component charges that landed after the last sync.
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 your Chargify invoices and transactions against your accounting and bank data, flags the gaps before close, and traces each difference to the subscription change that caused it. The MRR on your board deck matches the cash that actually landed.
When failed-payment events break their trend, it tells you which subscriptions are affected and how much MRR is at risk, traces the failures back to the customer and product, and lines up the dunning fix for you to approve.
It watches metered and quantity-based components across your subscriptions and tells you when a customer's usage is about to cross a pricing tier or spike well past their historical baseline, so you can reach out before the invoice does the talking.
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 Chargify object, modeled and query-ready the moment you connect.
It runs on your real Chargify account (prorated upgrades, mid-cycle component changes, migrated subscriptions 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 →