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§ Agent · GitLab

The GitLab data agent that acts the way you would.

It watches your GitLab data alongside your infrastructure and product metrics, on a schedule you set or whenever fresh data lands. When something needs attention, it tells you, or handles it the way you would.

D
DefiniteAPP9:14 AM · #engineering-alerts
⚠️ Merge request cycle time up 62% this sprint; 3 repos account for 84% of the slowdown

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.

Review & approve Dismiss
GitLab Merge Requests + Commits + Pipelines · joined to project plan · audit log

How an agent works

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.

◄ repeats on the schedule you set ►

You stay in control

An agent does what you'd do, and only what you've authorized.

The same trusted numbers

It acts on the same governed metrics as your dashboards, and every action is logged and traceable.

You approve anything that writes

It alerts and recommends on its own; anything that changes data is yours to approve.

Try it on a test channel first

Point a new agent at a throwaway channel and watch its judgment before it touches anything real.

No false alarms

It remembers what it already flagged and waits before acting again, so it won't alert you about the same thing twice.

What you can put an agent on

Delivery HealthACROSS YOUR SOURCES

Tie your GitLab velocity to the roadmap and incidents

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.

Merge RequestCommitPipelineMilestone
Review Bottleneck

Catch review queue pileups before they stall the sprint

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.

Merge RequestUserProject
Pipeline Reliability

Flag CI failure spikes with the job and timeline

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.

PipelineJobProject
Custom

Run any Python it needs to get the job done

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.

Why not just build it yourself?

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:

  • The cross-source join: not one tool's data, but it reconciled against the rest of your stack
  • A trusted, consistent metric: the same number your dashboards use
  • The investigation into why, when something fires
  • A full audit trail of everything it did
  • The upkeep, when the schema drifts or the script breaks at 2am

The data it works from

Every GitLab object, modeled and query-ready the moment you connect.

Group
customeroperations
Project
salessupport
User
customersales
Issue
customermarketing
Epic
supportengagement
Milestone
revenue_financeengagement
Merge Request
salessupport
Commit
salessupport
Pipeline
salesproduct
Job
salesdevelopment
Release
salessupport
Label
revenue_financemarketing
Vulnerability
marketingsupport

It runs on your real GitLab instance (flaky pipelines, stale branches, bot-authored MRs and all), not a tidy demo.

Where it acts

Slack

A message in the channel you choose, with the context and a button to act on it.

Email

A summary in the inbox of the people who need to see it.

Webhook

A payload to your own systems, to wire the agent into whatever you already run.

Warehouse write-back

A flag written back to your warehouse for everything downstream to pick up.

Hand off to Fi

Kick the question to Fi to investigate the why and propose the fix.

MCP

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.

Build your agents with Fi

Fi is your AI analyst. It helps you build and customize everything in Definite, including the agents that watch and act.

Fi

Your AI analyst. Ask questions in plain English, and let it help you build and customize everything in Definite, including your agents.

Meet Fi →

Agents

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 →

Get started

  1. 1Connect GitLab, and the sources it needs to reconcile against. Synced and modeled in an afternoon.
  2. 2See the numbers tie out to what you already trust.
  3. 3Put an agent on one thing you can't afford to miss. Fi helps you build it.
§ FAQ

Common questions

You set the schedule, and it also re-checks whenever fresh GitLab data lands. Each agent watches the one thing you point it at, nothing else.
It alerts and recommends on its own. Anything that writes, whether to a tool, your warehouse, or a customer, is yours to approve. You can also point a new agent at a test channel first and watch its judgment before it touches anything real.
When something fires, it can hand off to Fi to investigate, drilling into the data it has across your connected sources to find what's behind the move, and showing its work.
Those show you delivery metrics inside GitLab when you go look, scoped to GitLab alone. This watches continuously, joins GitLab velocity and CI data to your roadmap and incidents, and acts on the shift the moment it happens, so you catch the review bottleneck before it stalls the sprint.

Your answer engine
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Book a 30-minute call and watch us build your first dashboard live, with your own data.