Hi everyone,

Every few months a launch comes along that reads like someone was taking notes in my hallway conversations. This is one of them.

On October 23, Monte Carlo launched Bayes, which it calls an AI teammate. Bayes observes, fixes and keeps improving AI agents and the data that powers them, together, in one loop.

I want to go deeper than the launch post. This issue covers what Bayes does technically, why I think the timing matters, and what I would ask before rolling it out.

Why this one caught my attention

This fall I covered Dreamforce, UiPath FUSION, Equinix Horizon, IBM Think Mumbai and Gartner IT Symposium for The Ravit Show. The keynotes were about agents. The conversations after the cameras stopped were about something else.

CIOs and CDOs kept describing the same pattern. The agent looks great in the pilot. It goes to production. Months later, somebody finds out it has been confidently giving wrong answers, and the model was never the cause. A table upstream didn’t refresh. A schema changed. A vendor feed showed up late.

The agent had no idea, and nobody else did either.

On the other side of the house, data teams are buried in alerts. When everything pages, people stop reading pages. I have heard more than one leader admit their team simply muted the channel.

Bayes is aimed directly at the space between those 2 problems.

The real problem: agents fail where data fails

Here is how I would explain this to an executive team.

An enterprise agent is only as good as 3 things: the model, the agent’s own logic (its tools, prompts and retrieval steps), and the context and data it pulls from. Most “AI observability” today watches the first 2. Most “data observability” watches the third. Each sits in a different tool with a different owner.

So when an agent gives a bad answer, the investigation crosses teams. The AI engineer looks at traces. The data engineer looks at pipeline runs. Everyone has partial evidence, and the incident stretches from hours into days.

Monte Carlo’s pitch is that you trace them together. You follow a bad answer through the agent’s decisions, into the context it retrieved, and down to the table that went stale.

Anatomy of a 2 AM incident

Monte Carlo’s launch example deserves a full walkthrough, because every data leader reading this has lived some version of it.

Between 2:50 AM and 4:29 AM, a support AI domain fires 12 alerts:

• stripe_charges arrives 50 minutes late

• The orders rebuild times out

• customer_balance doesn’t refresh, then shows values out of range

• refunds_daily is missing yesterday’s rows, then still hasn’t refreshed

• sf_opportunities has fewer rows than expected

• support-agent and pricing-assistant answer slower than usual

• orders has missing customer_id values

• support-agent starts answering from stale data

Seen as a list, that is 12 separate problems. In reality there is 1 causal chain:

1. The Stripe load arrives 50 minutes late

2. The 2:00 AM dbt rebuild of orders times out waiting on it

3. refunds_daily and customer_balance, which depend on orders, don’t refresh

4. The support agent retrieves from those tables and starts quoting yesterday’s refund amounts to customers

At 7:42 AM Bayes posts 1 message in Slack. It says what is broken: 3 of the 12 tables behind the support agent didn’t refresh overnight, and the agent has answered 41 refund questions from stale data since 6:10 AM. It says why that matters: customers are getting the wrong refund amounts. It gives the likely cause, which is the timed out orders rebuild. And it suggests the fix: re-run orders and its 2 downstream models, then raise the job timeout to 45 minutes.

4 alerts grouped into 1 issue. 1 agent, 3 tables. One click to review the fix.

The detail that matters most to me is the business impact line. “41 customers got wrong refund numbers” is a sentence a CIO can act on. “customer_balance freshness SLA breach” is not.

How Bayes works under the hood

Bayes runs a loop that Monte Carlo describes as built on reinforcement learning principles: Learn, Detect, Fix, Measure.

Learn. Once connected, Bayes reads your AI and data stack, proposes a monitoring strategy, and deploys it after you approve. Monte Carlo calls this autonomous onboarding. The promise is full coverage in days rather than months.

Detect. This is triage: is the alert real, and how urgent is it? Bayes is designed to be skeptical by default. It treats an alert as unconfirmed until the evidence backs it up. It also reviews each domain on a regular cadence, agents and data together.

Fix. It finds the likely root cause across agents and data, then suggests a remediation backed by evidence it actually examined. Related alerts are grouped by root cause, and it never alerts you twice on the same issue.

Measure. This step is the one most tools skip. With access to your operational context, Bayes can tell real issues apart from tickets that are already resolved, and it checks that a fix was implemented in each cycle. In Monte Carlo’s example, Bayes opens ticket ORD-482, assigns it to a coding agent, and marks it done only after PR #1187 merges.

That loop is why Monte Carlo talks about “self-improving agents.” Every issue that is fixed and verified should make the next run better.

On top of the loop there are 2 more capabilities:

• Monitor tuning, which keeps monitors useful over time so coverage doesn’t slowly decay into noise

• Compliance, which discovers, monitors and governs PII in your warehouse

The autonomy ladder is the real enterprise feature

If I were a CIO, this is the section I would read twice.

Bayes runs at 4 levels of autonomy:

1. Manual. Your team does every step and Bayes stays on standby

2. Assisted. Bayes supports your team as they work

3. Supervised. Bayes proposes fixes and your team approves them

4. Autonomous. Bayes runs in the background and your team handles exceptions

Access is scoped by domain. In Slack, each domain gets its own channel. Bayes starts with whatever access you are comfortable giving and earns more over time: more domains, more agents, more tables.

This is the right design. No enterprise I know will give an AI agent write access to production pipelines on day 1, and none should. A model where the agent earns trust domain by domain, with every action approved and recorded, is something a governance team can actually sign off on.

Where it lives

• Slack: one channel per domain, where Bayes posts issues and answers questions in thread

• Monte Carlo platform: for reviewing fixes, coverage and the audit trail

• Your coding environment via MCP: Bayes inside Claude Code, Cursor and other agents while engineers build and debug

The MCP piece is underrated. The engineer fixing the pipeline can ask Bayes about the incident directly from their editor instead of switching between 4 tabs.

The numbers Monte Carlo is sharing

These are early customer results from Monte Carlo:

• Global bank: 200%+ more real incidents detected and 87% fewer false positives compared to their in-house approach

• Major global retailer: 75% of all monitoring deployed by the agent, from zero to comprehensive coverage in 30 days

• Global media brand: 20 days to steady-state coverage

• Leading beverage distributor: 24 hours from plan to production for a critical business domain

Monte Carlo says it is trusted by 400+ enterprises, including T. Rowe Price, PepsiCo, Cisco, Comcast, Nasdaq, Disney+, Target and Salesforce. The New York Jets are a published case study.

To be direct: these are vendor-reported numbers. They are strong, and the bank figure in particular describes exactly the pain I hear about. Your own pilot is still the only benchmark that counts.

My take

I have watched Monte Carlo help define data observability as a category. Bayes looks like the logical next step. Data quality and agent quality turn out to be the same problem looked at from 2 sides.

3 things stand out to me.

1. Unifying agents and data is the right call. Most agent failures I hear about in production trace back to the context. A tool that only watches the model is missing most of the picture.

2. Being skeptical by default is a smart design choice. Alert fatigue is the reason monitoring programs fail. A system that groups by root cause, refuses to repeat itself, and pairs every alert with a fix stands a real chance of keeping engineers’ attention.

3. Verification closes the loop. Plenty of tools can suggest a fix. Far fewer confirm the fix landed and the issue is closed. That is the difference between a smart alert and a teammate.

What I would ask before rolling it out

If you are an enterprise leader evaluating Bayes, these are my questions:

1. Data access and residency. What can Bayes read, and where does that data go? Monte Carlo covers this in its FAQ, so have your security team read it closely.

2. Approval controls. Can it change anything without approval? Map the autonomy levels to your change management process before you start.

3. Audit trail. Can you reconstruct exactly why Bayes recommended a fix 6 months later, for an auditor?

4. Your noise baseline. Measure your current false positive rate and MTTR before the pilot so the results mean something.

5. Stack coverage. Monte Carlo supports 30+ data sources plus models, frameworks and telemetry. Confirm your specific agent framework and warehouse are covered.

A 30-day playbook

If I were running this pilot:

• Week 1: Pick 1 domain where an agent faces customers or revenue. Connect it, review the monitoring plan Bayes proposes, and approve it.

• Week 2: Run in Supervised mode. Track how many of Bayes’s issues were real, how many alerts it grouped, and how long fixes took.

• Week 3: Compare against your baseline for false positives, time to detect and time to resolve.

• Week 4: Decide whether to expand to a second domain or move routine fixes in the first domain toward Autonomous.

Final thought

The first wave of enterprise AI was about getting agents built. The next one is about trusting them in production, at scale, without hiring an on-call team for every agent you ship.

Bayes is Monte Carlo’s answer to that, and it is one of the more practical launches I have seen this year.

I want to hear from you. If you are running agents in production today, what breaks most often: the model, the agent logic, or the data underneath? Reply or drop a comment.

Learn more and see how Bayes works: montecarlo.ai/bayes

See you in the next one,
Ravit Jain