Last night's load wrote 112,000 rows against a trailing-7-day baseline of ~287,000/day. The retention cohort, the product usage rollup, and the exec KPI dashboard all read from this table and are now computing on a partial dataset. Looks like the scheduled query timed out after a slot reservation change at 02:47 UTC.
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.
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
Tie your BigQuery tables to the rest of your data stack
It reconciles the rows in BigQuery against the same entities in your other sources and your dbt models, and flags the gaps before they reach a dashboard. You find out the source-of-record and the warehouse disagree while it is still a sync problem, not a number someone already presented.
Catch a stale or partial load before it ships downstream
When row counts, load timing, or null rates on a table break their trend, it tells you which table is affected and what reads from it, then looks at why the load came up short. You hear about a failed scheduled query or a stuck pipeline from the agent, not from someone staring at a dashboard that is quietly wrong.
Flag a schema change before it breaks a model
When a column gets added, dropped, or retyped, or a view definition shifts under you, it surfaces the change and the models and dashboards that depend on it before the next run fails. The drift that usually shows up as a red pipeline at 6am becomes a heads-up you can act on.
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 Google BigQuery object, modeled and query-ready the moment you connect.
It runs on your real BigQuery project (whatever your pipelines load, however the schemas evolved, including the wide partitioned tables nobody wants to refactor), not a clean demo. You define the metric once as governed SQL and it watches that.
Where it acts
Slack
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.
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.
Google BigQuery guides & insights
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
- 1Connect Google BigQuery, and the sources it needs to reconcile against. Synced and modeled in an afternoon.
- 2See the numbers tie out to what you already trust.
- 3Put an agent on one thing you can't afford to miss. Fi helps you build it.