5+ day, 9+ hour ago   (234+ words) There is a certain type of failure mode that keeps platform teams awake. Take, for example, this scenario that a platform engineering team in the insurance sector might face: A release goes out. Somewhere in a mapping layer, however, a…...

6+ day, 8+ hour ago   (1270+ words) At the end of Part 3, your monitors were live and routed, and we said something was going to fire. Well, something just did. Maybe it came in as a Slack message or an email, or maybe a new row at the…...

6+ day, 8+ hour ago   (1019+ words) In Part 2 of this four-part series, we worked out what’s worth monitoring on your particular agent, and in what order. At this point, the traces are arriving in Monte Carlo. You can already do more than just watch them flow…...

1+ week, 3+ day ago   (1376+ words) ETL and orchestration is one of the most varied integration categories our customers ask for. Alongside the mainstream tools we cover natively (Airflow, dbt, Fivetran, Azure Data Factory, and more), enterprises run dozens of others: commercial platforms, cloud-native services, and…...

1+ week, 4+ day ago   (1018+ words) Your test suite is a record of every failure you’ve faced. You wrote a test because something broke once, and you never wanted it to happen again. Which is great, except it means your tests are always backward-looking. The failures…...

3+ week, 1+ day ago   (138+ words) Most teams can ship an AI agent. Almost none of them can tell you, with confidence, whether it’s making good decisions once it’s live. See a live demo of Monte Carlo’s Agent Trust platform, built around four layers every production…...

3+ week, 3+ day ago   (534+ words) When a monitor fires on a data table or agent, Monte Carlo streamlines the investigation process for you via our Troubleshooting Agent. It gives you a root cause, the evidence behind it, and a verification checklist. The last step in…...