Eight issues totaling 24 points are still in To Do with three days left, and two of them block the release epic, well off your usual mid-sprint burn.
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 Jira issues and sprints to your deploy history and incident data, so a closed epic that quietly caused three production incidents stops reading as a clean win. You find out which work is actually landing, not just which tickets got dragged to Done.
When committed points stall against your normal burn, it tells you which issues are stuck, which ones block a release, and how far behind the sprint is tracking. You hear it at the midpoint, with time to re-scope, not at the retro.
It watches for issues sitting too long in a status, blockers with no movement, and tickets bouncing between assignees in the change log. It surfaces the dollar or deadline cost and routes it to the right owner before it becomes the thing that slipped.
Beyond alerts and write-backs, an agent can run arbitrary Python, so it can do whatever the task actually requires: call the Jira API to re-assign a blocker, 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 Jira object, modeled and query-ready the moment you connect.
It runs on your real Jira instance (stale tickets, half-filled custom fields, abandoned boards 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 →