A Playbook for Your Data / Engineering Teams

What it is: Monte Carlo AI brings our Operations Agent directly into Slack. @ mention @Monte Carlo AI in any channel, reply in an alert thread, or DM it — it understands plain language and automatically routes your request to the right specialized agent (Troubleshooting, Triage, Tuning, Cost, PII) behind the scenes. You don't pick an agent; you just ask.

How it actually works under the hood

None of this works from a blank slate: Monte Carlo is already connected end-to-end across your stack — warehouses, BI tools, orchestration (Airflow/dbt), and code — with full lineage mapped down to the column level. That's what lets an agent actually answer "why," not just "what," the moment you ask.

Monte Carlo's agent stack isn't one model — it's a pipeline, and the Operations Agent is the front door to all of it:

• Triage Agent — scores every new alert on incident likelihood + blast radius (HIGH/MED/LOW), so you know what to look at first
• Troubleshooting Agent — works through hundreds of root-cause hypotheses (data changes, Airflow/dbt failures, code changes), traverses lineage automatically
• Tuning Agent — when troubleshooting concludes the monitor itself is the problem (not the data), proposes a config fix based on alert history
• Cost & Performance Agent — finds and cuts warehouse spend
• PII/Compliance Agent — warehouse-native PII discovery and monitoring
• PR Agent — reviews code changes before they hit production

Ask a question in Slack, and the Operations Agent decides which of these actually handles it — you never have to know the difference.

Use cases to lead with

1. Troubleshoot something nobody flagged yet

A stakeholder reports a bad number, a dashboard looks off, a pipeline ran weird — none of it tripped a monitor. Bring it to Slack anyway.

• "A stakeholder said this dashboard looks wrong — help me figure out why"
• "Why did this table's row count drop overnight?"
• "What upstream tables feed into this one, and did any of them change recently?"

2. Sanity-check a number before you trust it

Before shipping a report or handing off a table, a quick health check catches problems before they become someone else's problem.

• "How's the health status of table X?"
• "When was table Y last updated?"
• "Is this metric's underlying data affected by any open incidents?"

3. Clear an alert queue in minutes

Instead of scrolling a long alert feed guessing what matters, ask it to prioritize for you.

• "Triage my open alerts" — returns a priority score (HIGH/MED/LOW) with rationale for each
• "Are there any alerts assigned to me today?"

4. Tune monitors

If a monitor keeps firing on things you conider non-issues, ask the agent to fix it rather than muting it and losing coverage.

• "This monitor keeps false-alarming, can we tune it?"
• "Which of my monitors are firing the most false positives?"

5. Build a monitor by just describing it

No need to write monitor config or SQL by hand — describe the concern in plain language.

• "Watch for schema changes on this table"
• "Alert me if this table's volume drops more than 20% day over day"

6. Know what's actually driving your warehouse bill

Turns "our Snowflake bill went up" from a mystery into a specific, actionable answer.

• "What's driving the increase in Snowflake spend this month?"
• "Which queries or tables are the biggest cost contributors?"

7. Monitor PII

A quick way to check exposure without a full audit.

• "Set up a PII monitor to capture sensitive data in schema X"
• "Are there any unmasked columns that contain PII in this schema?"
• "Show me tables where PII detection status changed this week"

Getting started as a team

1. Create a shared channel (e.g. #data-helpdesk) and invite the app: /invite @Monte Carlo AI
2. Seed it with a few real first questions — try "How's the health of [your most-watched table]?" or "Triage today's alerts" to get started
3. Make it your default first stop for "does this look right" questions before opening a warehouse console manually
4. Consider bringing in both engineers and analysts, not just the team that owns pipelines — health checks and freshness questions are just as useful for anyone sanity-checking a number

Tell us what doesn't land — if an answer misses, that feedback helps us make the next one better

NOTES

• It answers with the asking user's own Monte Carlo permissions — it can't see anything you personally couldn't already see in Monte Carlo
• Raw sample data can be restricted per-workspace — check your workspace's Sample Data setting if this matters for your data sensitivity