AI Culture

There’s a clear narrative sweeping the software world these days: AI is getting dramatically cheaper. Or, to be more specific, inference is getting cheaper.  Various researchers have been quantifying this:  Stanford’s AI Index, for example, tracked the cost of querying a GPT-3.5-equivalent model falling from $20.00 per million tokens in late 2022 to $0.07 by …

Given the plethora of LinkedIn posts on the subject, if you deploy a general purpose agent or other AI system to the EU market, you are likely aware that the majority of EU AI Act Article 50 transparency rules went into effect on 2 August 2026. At a high level, Article 50 requires providers and …

With the proliferation of AI across businesses for internal use cases, teams are quickly spinning up agents to take on many of the tasks that humans used to do. With that inevitably comes some operational chaos.  Walk into almost any go-to-market org today, for example, and you will find the same thing we found when …

Quick Answer An AI observability engineer monitors, troubleshoots, and improves the reliability of AI systems in production including the data pipelines, models, and agents involved in the stack. Where a data engineer builds pipelines and an AI/ML engineer builds models, the AI observability engineer makes sure both keep working as intended once they ship. As …

For many organizations, getting agents to successfully run in production, particularly to the extent that they can be trusted to be completely autonomous, seems like an insurmountable challenge.  Agents look great in pilots, but once they are deployed live, issues inevitably emerge. Production is unpredictable; agents have to operate in conditions that cannot be anticipated …

Our enterprise customers are shipping agents in production at scale, and that raises the bar on everything underneath them. An agent is only as reliable as the data it reads and the guardrails on what it’s allowed to do with it. That’s why we continue to strengthen and expand the capabilities of our platform.  Read …

In November 2025, I turned our security architecture reviews into a repeatable workflow. That’s the whole origin story. No mandate came down to build an agent fleet. I got tired of running the same design review by hand and wrote a workflow for it instead. By February, the same instinct hit vulnerability triage. That workflow …

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 …

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 …