Monte Carlo Data

TL;DR What is AI observability? AI observability is the practice of monitoring AI applications end-to-end — from source data to model output — to detect and resolve the silent, probabilistic failures that traditional monitoring tools miss. Why it matters AI can fail silently — wrong outputs with no error signal Data issues upstream often look … Continued

Every few months, the world picks two technologies that do completely different things and decides they’re rivals. Right now, it’s RAG vs MCP. Here’s the thing, though. RAG (Retrieval-Augmented Generation) is a pattern for feeding relevant documents to an LLM at query time, while MCP (Model Context Protocol) is a standardized way for AI models … Continued

For thirty years, “user” meant a human being. Every design principle, every interface, every experience framework assumed a person sitting at a screen. That’s over. In the last year, AI agents went from a curiosity to an operating reality. We first heard the term Agent Experience (AX) from Netlify’s Matt Biilmann. Salesforce declared a new … Continued

In March we restructured Monte Carlo’s engineering organization. I want to write down exactly what we changed and why — because I think a lot of engineering leaders are circling these same decisions right now. The starting point: we were doing the right things — just not the new right things Before the restructure, Monte … Continued

Whether you're monitoring a handful of models or managing AI at enterprise scale, you need AI observability tools. Let's dive into it.

Quarterly Report filed 2026-05-07

Material Event filed 2026-05-07

Every data leader is being asked the same question right now: “Can’t we just point Claude at our warehouse and let everyone ask their own questions?” The naive version works for about a week. Then two execs ask the same question and get two different numbers, Claude joins the wrong tables and answers confidently anyway, … Continued

Every data leader is being asked the same question right now: “Can’t we just point Claude at our warehouse and let everyone ask their own questions?” The naive version works for about a week. Then two execs ask the same question and get two different numbers, Claude joins the wrong tables and answers confidently anyway, … Continued

AI coding agents are writing SQL, building pipelines, and modifying schemas at a pace that would have been unimaginable two years ago. They’re fast, tireless, and increasingly capable. But they lack something every experienced data engineer has: the operational instincts to do data work responsibly. They don’t feel accountable like humans do, and they certainly … Continued

AI coding agents are writing SQL, building pipelines, and modifying schemas at a pace that would have been unimaginable two years ago. They’re fast, tireless, and increasingly capable. But they lack something every experienced data engineer has: the operational instincts to do data work responsibly. They don’t feel accountable like humans do, and they certainly … Continued