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§ Agent · GitHub

The GitHub data agent that acts the way you would.

It watches your GitHub data alongside your project tracker and incident tools, 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 · #eng-alerts
⚠️ PR review bottleneck: 31 PRs waiting >48h, 4 devs carrying 78% of reviews

Review throughput dropped 40% this week. Four reviewers approved 78% of merged PRs while 31 open PRs have been waiting more than 48 hours, mostly on the payments and onboarding teams. Median time-to-first-review is 53h, up from your 18h baseline.

Review & approve Dismiss
GitHub Pull Requests + Reviews + Commits · cross-referenced with Linear · 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

VelocityACROSS YOUR SOURCES

Track eng velocity against your project tracker and incidents

It computes cycle time, deploy frequency, and review throughput from your GitHub data, then reconciles those numbers against your project tracker and incident history so you can see whether the team is shipping or just busy.

Pull RequestCommitDeployment
Review load

Spot review bottlenecks before they stall the team

When review wait times break their trend, it tells you which repos and reviewers are affected, how many PRs are blocked, and what the downstream impact looks like on merge velocity, so you can rebalance before the queue backs up.

Pull RequestContributor
CI health

Flag CI failures and flaky builds with the cost in eng hours

When your workflow failure rate spikes or a specific job starts flaking, it surfaces the failing workflows, the repos involved, and the time your team is losing to reruns, so you can fix the pipeline instead of ignoring it.

Workflow Run (GitHub Actions)Repository
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 GitHub object, modeled and query-ready the moment you connect.

Organization
customerengagement
Team
customersales
Repository
salesoperations
Dependency
productdevelopment
Contributor
customersupport
Stargazer
revenue_financecustomer
Repository Traffic
marketingengagement
Issue
marketingsupport
Pull Request
customersales
Commit
operationsdevelopment
Release
supportproduct
Milestone
supportengagement
Workflow Run (GitHub Actions)
productoperations
Deployment
engagementproduct
Discussion
supportengagement
Project (Projects V2)
revenue_financecustomer

It runs on your real GitHub org (bot commits, stale branches, flaky CI 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 GitHub, 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 GitHub 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.
Insights shows you static graphs for one repo at a time, when you go look. This watches continuously across every repo, reasons across GitHub plus your project tracker and incident data, and hands off to Fi to investigate why, so you find out before standup, not after.

Your answer engine
is one afternoon away.

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