Three prospecting ad squads on the Q3 awareness campaign saw swipe-through rates drop below 0.4% after the audience segment refresh on Monday. Cost per lead jumped from $31 to $54, and pixel-tracked conversions fell to roughly a quarter of your trailing 4-week average.
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 Snapchat Ads spend and pixel conversions against your CRM closed-won data, so you can see blended CAC by campaign and stop relying on platform-reported numbers that never match revenue.
When swipe-through rates or video view completion on an ad squad break their trend, it surfaces which creatives are fatiguing, how much spend is at risk, and which audience segments are dropping off, so you can rotate before performance collapses.
When two ad squads target overlapping audience segments and one is bidding up the other, it catches the collision, shows you the spend impact, and lines up the consolidation for you to approve.
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 Snapchat Ads object, modeled and query-ready the moment you connect.
It runs on your real Snapchat Ads account (test campaigns, paused ad squads, partial pixel data 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 →