Monte Carlo Data

At Monte Carlo, we run a lot of our workflows and processes on AI. Claude is woven into our product, our internal tooling, our agents, and our day-to-day tasks. So when I say teams are routinely wasting 30-40% of their token budget, I’m not guessing. This is something I’ve watched happen at scale, including inside … Continued

CoCo just got a trust layer Snowflake-native data teams have a new way to work. MC Agent Toolkit v1.13.0 brings Monte Carlo’s full coding agent experience to Snowflake Cortex Code — at complete feature parity with our Claude Code plugin. All 17 skills, slash commands, mc-mcp with OAuth, and the Prevent hook lifecycle are available … Continued

Every product team has the same recurring nightmare. A PM opens Mixpanel to answer a simple question — how many users completed onboarding last week — and finds three events that could plausibly mean “completed onboarding”: Onboarding Complete, onboarding_finished, and Completed Setup. None of them is documented. Two of them stopped firing in March. The … Continued

One Genie answer is worth a thousand queries. But only if you can trust it. Business users are increasingly turning to Databricks AI/BI Genie to interrogate their data without having to write SQL. With Genie, a business analyst can ask a question in plain English and Genie will translate it to SQL, run it against … Continued

One of the things I believe most strongly about building AI agents: the best way to understand what your agents actually need is to run them on your own products. That’s the spirit behind our internal testing and iteration program where we use our own products to make our platform better; think of it as … Continued

Material Event filed 2026-06-23

Warehouse costs are rising. The data needed to explain why — query history, lineage, schema metadata, usage patterns — is scattered across systems that don’t talk to each other. Finding the specific tables and pipelines responsible, at scale, without a dedicated analyst to do the forensics, takes time most teams don’t have. This results in … Continued

Most enterprise teams are now running AI agents in production. But a critical problem is quietly growing behind those deployments: when something goes wrong, no one knows where to look. Agents misbehave or hallucinate, outputs degrade, and end users eventually complain. In most of these cases, however, it’s unclear whether the root cause of a … Continued

I’ve spent years talking to data and AI leaders about governance, and one thing I’ve noticed is that the conversation almost always collapses into the same pattern: everyone agrees it matters, but no one can get their organization to actually do it. Somewhere in the middle of all that back-and-forth, the initiative dies. Yesterday at … Continued

You’ve spent months building your shiny new AI product. You’ve reorganized teams, splurged on the best models, and dedicated real people to crafting That One Perfect Prompt. And then it chokes anyway, because the data feeding it just wasn’t ready. Teams are moving fast to ship AI, and in the rush, they’re cutting corners on … Continued

Yesterday at Databricks Data + AI Summit, I had the chance to sit down with Lenny Rosenfeld, VP of Data Science and Software Engineering at NASDAQ, for a fireside chat about what it actually takes to deploy AI agents in one of the world’s most regulated, most consequential data environments. NASDAQ isn’t experimenting with AI … Continued