Median time-to-merge climbed from 18 hrs to 29 hrs over the last 14 days, concentrated in billing-service, auth-api, and payments-core. Review queue depth is 2.4x its 30-day baseline, and 11 MRs have been open longer than 5 days.
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 merge request throughput, commit volume, and pipeline success rates to your project milestones and incident data, so you see where delivery is slowing relative to the plan. You stop discovering the bottleneck in standup and start tracing it to the repo.
When merge requests start aging or review throughput drops below its trend, it tells you which projects and reviewers are affected, how many MRs are waiting, and how long they have been open. You find out in hours, not at retro.
When pipeline failure rates climb on a project, it surfaces which jobs are failing, when the failures started, and whether retries are masking a flaky stage. It routes the signal to the right person before failed builds start blocking deploys.
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 GitLab object, modeled and query-ready the moment you connect.
It runs on your real GitLab instance (flaky pipelines, stale branches, bot-authored MRs 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 →