By Lior Gavish
In March we restructured Monte Carlo’s engineering organization. As I’ve thought about sharing our decision-making process, I’ve wanted to be far enough past the restructure to say something honest, objective, and which other engineering leaders can hopefully find useful. The short version is that our teams and operating principles weren’t broken, which made it particularly … Continued
By Lior Gavish
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
By Lior Gavish
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
By Lior Gavish
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
By Lior Gavish
Data teams have always wanted quality and reliability to be a built-in property of how data gets produced and consumed — not a separate chore bolted on after the fact. The whole point of data observability has been to shrink data downtime — the periods when data is wrong, missing, or otherwise inaccurate — by … Continued
By Lior Gavish
At Snowflake Summit this week, Sridhar Ramaswamy laid out a four-component model for the agentic enterprise: data and content, AI models, applications, and an agentic control plane that ties them together. It’s a useful frame. And I want to build on it — because I think the control plane concept is right, but most teams … Continued
By Lior Gavish
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
By Lior Gavish
I talk to engineering teams from different organizations every day, and many of them are running the same mental model when it comes to large technology projects: build first, then buy when it breaks. For most infrastructure decisions, this is fine; in fact, it is the most rational. You understand your requirements better after building … Continued
By Lior Gavish
Your revenue dashboard looks clean. Your pipeline ran green. Everything’s fine. Until your CFO asks why the numbers don’t match the invoice system and now you’re spending your Tuesday reverse-engineering a JOIN that broke three weeks ago. This kind of thing happens because humans are terrible at catching problems in datasets that update thousands of … Continued
By Lior Gavish
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
