Only 9 entries published in the last 30 days versus your 15/mo baseline. 12 entries tagged 'evergreen' haven't been updated in 90+ days. Based on historical organic traffic per post, the gap puts roughly 6,200 sessions at risk this quarter.
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 Contentful entries to your analytics and CRM data so you can see which content types, tags, and publish cadences actually produce sessions, signups, and pipeline, not just word counts.
When entries tagged evergreen or high-traffic go past their update SLA, it flags them with the traffic at stake and the last-reviewed date, so your team knows what to refresh before organic performance decays.
When a content type changes, fields are added or removed, or validations shift, it surfaces the change, the entries affected, and whether any downstream reporting depends on the old structure, before the breakage shows up in a dashboard.
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 Contentful object, modeled and query-ready the moment you connect.
It runs on your real Contentful space (draft entries, orphaned content types, inconsistent tagging 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 →