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§ Agent · Azure MySQL

The Azure Database for MySQL data agent that acts the way you would.

It watches your Azure MySQL tables alongside the rest of your warehouse, on a schedule you set or whenever fresh data lands. When a sync stalls, a schema shifts, or row counts drift from source of truth, it tells you, or handles it the way you would.

D
DefiniteAPP9:14 AM · #pipeline-alerts
⚠️ orders table 4.2 hrs stale; downstream revenue dashboard already diverging from Stripe by $18,300

Incremental sync for app_prod.orders has not landed since 02:14 UTC. The revenue rollup in the warehouse is $18,300 behind what Stripe shows for the same window. Two dbt models depend on this table.

Review & approve Dismiss
Azure MySQL Table sync · reconciled to Stripe Payments · dbt model lineage

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

ReconcileACROSS YOUR SOURCES

Reconcile your MySQL tables against the rest of the warehouse

It compares row counts, totals, and key fields in your Azure MySQL tables against the same data in Stripe, your ERP, or any other source already connected, and flags the gaps before they cascade into a wrong dashboard number.

TableView
Freshness

Catch a stale sync before it breaks something downstream

When an incremental sync falls behind its normal cadence, the agent surfaces which tables are affected, how far behind they are, and which downstream models depend on them, so you can fix the pipeline instead of debugging a dashboard.

Table
Schema

Detect schema changes before they break your models

When a column is added, renamed, or dropped in your MySQL database, the agent catches it from the schema catalog, tells you which tables and views changed, and flags the downstream dbt models or queries that reference those fields.

Schema CatalogTableView
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 Azure MySQL object, modeled and query-ready the moment you connect.

Table
general_data_storage
View
general_data_storage
Schema Catalog
general_data_storage

It runs on your real Azure MySQL instance (nullable columns, legacy tables, half-migrated schemas 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 Azure MySQL, 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 Azure MySQL 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.

Your answer engine
is one afternoon away.

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