How do you keep a live metrics dashboard current without rebuilding it?
A dashboard AI Employee connected to your warehouse and your workspace App Store. It builds the live metrics dashboard once, then keeps the app itself current as the questions and the underlying tables change, with every update published to your App Store for a named human to approve.
×A metrics dashboard is easy to build and hard to keep. The first version ships in an afternoon and everyone loves it. Then a warehouse table gets renamed, a metric's definition changes, a new dimension becomes the one everyone asks about, and the dashboard quietly drifts out of date. It still loads, which is the dangerous part: people keep trusting a number that no longer means what they think it does. Keeping it current is real engineering work that competes with everything else. So dashboards decay, a new one gets built next to the old one, and the team ends up with several half trusted views of the same numbers. What's missing isn't the ability to build a dashboard. It's someone whose standing job is to keep the app matching the warehouse it reads from.
The live dashboard AI Employee builds a metrics dashboard app once, listed in your workspace App Store, then runs on a schedule to keep it current. Each run reads the warehouse to check that the tables, columns, and metric definitions behind the dashboard still hold, updates the app where they've changed, and submits the new version to your App Store. A named human reviews and approves before the updated dashboard replaces the live one. It maintains the app, but it never publishes on its own.
See exactly how the work gets done.
Builds the dashboard app once
From the metrics you want, it scaffolds a small dashboard app in your workspace, listed in your App Store, reading directly from your warehouse.
Runs on a schedule
On the schedule you set, an isolated run starts inside your own environment with the warehouse permissions you've scoped for it, with no manual kickoff and no standing access between runs.
Connects to the warehouse, with permissions you set
It reads the schema and the queries behind each tile to confirm the tables, columns, and metric definitions the dashboard depends on still exist and still mean the same thing. Credentials stay in memory, never exposed to the model.
Updates the app where the source moved
When a table is renamed, a column is dropped, or a metric is redefined, it edits the dashboard app to match, updating queries and tiles rather than rebuilding from scratch.
Publishes the new version for review
The updated dashboard goes to your App Store as a pending version, with a note on what changed in the warehouse and what it changed in response.
A named human approves before it goes live
A named human reviews the diff and approves it before the new version replaces the one people are using.
Runs in your environment
Every run is isolated inside your own infrastructure, reaching only the warehouse and the App Store listing it's scoped to. Your data never leaves.
Read on the warehouse, scoped writes
It reads the schema and queries behind the dashboard; its only write is the updated version of the dashboard app it submits.
Publish gated by a named human
It maintains the app but never replaces the live dashboard itself. A named human approves each new version before it goes live.
Credentials stay in memory
Warehouse credentials live only in the run's memory, never written to disk and never exposed to the model.
You own every rule
The metric definitions, the schedule, and the approval step are yours, versioned and changed on your terms, not in a vendor dashboard.
The dashboard that used to drift silently out of date now has a standing owner that keeps the app matching the warehouse and proposes each update for approval. People stop building a fresh dashboard next to the stale one and start trusting the one that's kept current.
Built once
The dashboard app is maintained, not rebuilt each quarter
On a schedule
Checked against the warehouse and updated when the source moves
Publish gated
A named human approves before a new version goes live
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How do AI Employees keep your docs in sync with the code?
Sweeps the code merged each day, rewrites the docs those changes touched, and opens one reviewable pull request. Publishing waits for a human merge.
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How do AI Employees test every pull request before a human reviews it?
Checks out every pull request, runs the suite, exercises the change through the edge on a test deploy, and posts the result. It stays off production, leaving the merge to a person.
×How do you get a postmortem drafted the moment an incident resolves?
Reconstructs the incident timeline, correlates it against deploys and log spikes, and opens a structured postmortem as a doc PR. It drafts, and the team finalizes.
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