Invoice totals for the month are $1.23M, but payments applied only reach $1.18M. The gap traces to 14 invoices with partial payments and 6 with failed collection attempts, up from your ~$12K/period baseline.
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 Zuora invoices and payments against your accounting system and bank data, and flags the gaps before close, so the revenue on your board deck matches the cash that actually landed. You never find out about a collection shortfall from your auditor.
When cancellations or downgrades break their trend, it tells you which subscriptions are affected and how much ARR is at risk, surfaces the rate plans losing volume, and lines up the retention action for you to approve before the next renewal cycle closes.
For usage-based charges, it compares metered consumption against what the billing engine calculated, surfaces accounts where the gap is material, and routes the discrepancy to the right person before the invoice is finalized.
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 Zuora object, modeled and query-ready the moment you connect.
It runs on your real Zuora tenant (amendments, voided invoices, mid-cycle proration adjustments 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 →