Two line items in Spring Launch spent $6,800 in the last 24 hours but drove only 9 signups, well off your ~$74 blended CPA, while their CTR held flat.
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 Twitter Ads spend and conversions to the closed revenue in your warehouse, so you see blended CAC and payback by campaign, not the in-platform conversions the Ads dashboard reports to itself. You find out a channel stopped paying back before the next budget review, not after.
When a line item's CPA breaks its trend or it's pacing to blow its budget, it tells you which campaign and creative are responsible and how much you've already spent off-target. It lines up the pause or the cap for you to approve, before the day's budget is gone.
When a promoted tweet's engagement and CTR slide while frequency climbs, it surfaces which creative is fatiguing and what it's costing you in wasted spend. You get the rotation flagged while there's still budget left to move.
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 Twitter Ads object, modeled and query-ready the moment you connect.
It runs on your real Twitter Ads account (paused line items, half-tagged conversions, naming drift 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 →