Your Anaplan headcount model projects 142 FTEs for July, but your HRIS and payroll show 131 active. The $218K variance traces to 9 backfills budgeted in the model that haven't started yet and 2 unplanned terminations last week. Your board deck still shows the original plan.
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 the budgets and forecasts in your Anaplan model exports against your actuals from accounting, payroll, and your CRM. When a line item drifts past the threshold you set, it flags the variance, traces the driver, and surfaces it before the board deck goes out with a stale plan.
When the assumptions baked into a model quietly age out, the forecast keeps running on numbers that no longer hold. It watches the gap between each model's projections and the actuals flowing in from your systems, and tells you which assumptions broke and by how much, so you re-plan before the quarter closes on a fiction.
It watches your configured Anaplan exports across workspaces and models. When an export stops producing fresh files, delivers a truncated dataset, or a workspace goes inactive, it flags the gap before downstream reporting silently runs on old data.
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 Anaplan object, modeled and query-ready the moment you connect.
It runs on your real Anaplan tenant (stale exports, draft models, overlapping workspaces, 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 →