Your 'Enterprise ABM - Q2' campaign group is converting clicks to opportunities at half its 30-day rate, while spend held flat. The drop tracks to one creative that went live Sunday.
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 LinkedIn Ads spend to the opportunities and revenue those campaigns sourced in your CRM, so you see real cost per opportunity and pipeline by campaign, not the in-platform conversions LinkedIn reports to itself. You walk into the board meeting with a blended number you can defend, not three dashboards that disagree.
When cost per click or cost per lead breaks its trend, it tells you which campaign and creative moved, how much you've spent since it turned, and what it's costing you against baseline. You hear about the spike on Tuesday, not when you pull the weekly report on Friday.
It tracks spend against budget across your ad accounts and flags the campaigns set to overshoot or quietly underspend before the month closes. No more end-of-quarter surprises where half the budget never went out the door.
Beyond alerts and write-backs, an agent can run arbitrary Python, so it can do whatever the task actually requires: pause a campaign through the API, push spend back into your reporting tables, 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 LinkedIn Ads object, modeled and query-ready the moment you connect.
It runs on your real LinkedIn Ads account (paused campaigns, renamed creatives, UTM 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 →