Monte Carlo Data

Most data and AI teams have one channel where all notifications on the health of their system get sent to. Whether it’s an alert about a freshness break on a critical revenue table, a volume dip on a staging table that nobody really queries, or a monitor on an agent evaluating response qualities – all of …

When most people picture an AI agent failing, they picture a red alert, a 500 server error, or a massive stack trace. In reality, production failures almost never look like that. Across the deployments we monitor, the incidents that truly destroy user trust throw no technical errors at all. A query runs and the response …

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 …

In part 2 of our 4 part series, we explore what to start monitoring once you've got agent telemetry coming in to Monte Carlo.

Quick Answer Open source AI agent observability gives engineers visibility into what an agent actually did during execution, which tools it called, what it retrieved, and where it went off track. Most tooling builds on the OpenTelemetry standard to capture traces and spans across LLM calls and tool invocations. It works well in early-stage development …

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 …

Agent orchestration is the practice of coordinating multiple AI agents so they function as a single system. It governs the order agents act in, what information they share, when one agent hands a task to the next, and what rules apply across the whole group. In the last two years, AI agents have become increasingly …

Most agent observability projects stall on the assumption that instrumentation takes weeks. Here's what setup actually involves for platform agents and custom agents

Ask any engineering team running AI in production what they’d want most, and some version of self-improvement comes up for nearly everyone. The dream is to have agents that catch their own failures, learn from them, and come back sharper the next time without a team of engineers having to file ticket after ticket. When …

Agent orchestration is the practice of coordinating multiple AI agents so they function as a single system. It governs the order agents act in, what information they share, when one agent hands a task to the next, and what rules apply across the whole group. In the last two years, AI agents have become increasingly …