This pay period's total payroll came in $47,200 above plan. Twelve workers across two cost centers are running 15-22% above their comp band midpoint after recent merit changes, against a trailing six-period average variance of $8,400.
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 Workday payroll results and compensation against your headcount plan and general ledger, and flags the variances before close, so the labor cost on the ops deck matches what actually hit the books.
When voluntary exits break their trend in a department or tenure band, it tells you which org units are affected and how deep the gap is, looks at the position and worker history for patterns, and queues the escalation for you to approve.
When open requisitions, overlapping absences, or overtime spikes would leave a team below operating threshold, it surfaces the dates, the roles affected, and the coverage risk so you can adjust before the week arrives.
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 Workday object, modeled and query-ready the moment you connect.
It runs on your real Workday tenant (retroactive corrections, mid-cycle reorgs, custom report schemas 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 →