Pipeline failures on main climbed from your ~4% baseline to 18% over the last 5 days. 12 of the 17 failures trace to the integration-test job, with 3 test suites accounting for most of the flakes. Median workflow duration also crept from 11 min to 19 min, and deploy frequency is down 3x this week as a result.
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 CircleCI pipeline and workflow data to your deploy log and incident tracker, so when failure rates spike you see the downstream impact on ship rate and reliability in one view. You stop being the person who has to manually correlate a bad CI week with a drop in deploys.
When pipeline or job failure rates break their trend, it tells you which jobs are failing, which test suites are flaking, and how many workflows are affected. You find out the day the pattern emerges, not when someone asks why nothing shipped this week.
It watches workflow and job durations over time. When a job that used to take 8 minutes starts taking 20, it flags which job, when the drift started, and how it is affecting overall pipeline throughput. You catch the regression before the team routes around it.
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 CircleCI object, modeled and query-ready the moment you connect.
It runs on your real CircleCI data (flaky tests, cancelled workflows, resource class mismatches, 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 →