ACOS on your top 12 Sponsored Products campaigns climbed from 26% to 41% over the last 7 days. Ad-attributed sales in Amazon Ads are $22,400, but only $14,100 matched to orders in your system. Three campaigns are pacing above budget with declining conversion rates.
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 ad-attributed sales reported by Amazon against your order and revenue data, and flags the gap before your weekly review, so the ROAS in your channel report matches the revenue that actually came through.
When ACOS breaks its trend on any campaign or ad group, it tells you which keywords and product ads are responsible, how much spend is at risk, and lines up the bid adjustment for you to approve before the budget runs out.
When a portfolio's conversion rate drops or spend pacing drifts from plan, it surfaces which campaigns inside that portfolio moved, the products involved, and the budget impact, and routes the finding to the right person before the review cycle.
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 Amazon Ads object, modeled and query-ready the moment you connect.
It runs on your real Amazon Ads account (overlapping campaigns, stale keywords, test portfolios 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 →