Data Observability
Data Observability
What Is an AI Trace? A Practical Guide to Tracing LLMs and Agents
Your AI agent just answered a customer’s question. The request came in, the system processed it, and a response went out. All of your signals are green, indicating that latency was normal, no errors were thrown, and your HTTP response codes are clean 200s. The answer it gave, however, was also completely wrong. This is …
AI Observability
Saying bye-bye to BI
One of the casualties of the race to agentify everything has been BI. At Monte Carlo we’ve seen usage drop to a third of what it was at the start of the year. This is down to end users now asking their questions of their favourite LLM (Claude in our case) either directly or through …
Data Observability
Stop Cleaning Up Your Clickstream Data. Let Claude Ship It Clean.
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 …
Data Observability
Agent Health in practice: How we caught a stale model in our own Troubleshooting Agent
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 …
Data Observability
Monte Carlo brings native Agent Bricks observability to Databricks — zero instrumentation required
Ahead of Databricks Data + AI Summit, Monte Carlo is thrilled to launch native observability support for agents built on Agent Bricks. Agent Bricks already writes MLflow trace data to Unity Catalog Delta tables as part of its standard operation. Monte Carlo now reads those traces directly through your existing Databricks connection — no SDK …
Data Observability
Data trust used to come after the fact. With Claude, it ships with your code.
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 …
Data Quality
Data Quality Statistics & Insights From Monitoring +11 Million Tables in 2026
We looked at our telemetry to answer questions on common data quality statistics, like incident frequency, alert fatigue, monitor deployment and more.
Data Observability
Build vs. buy: The real token economics of agent observability
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 …
Data Observability
Your Data Isn’t as Clean as You Think. Here’s How Data Quality Automation Helps
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 …
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Data Platforms
Data Ingestion: 7 Challenges and 4 Best Practices
Data ingestion involves collecting data from source systems and moving it to a data warehouse or lake. Read on for the top challenges and best practices.
Data Observability
Data Observability: How to Build Your Own Data Anomaly Detectors Using SQL
How to use metadata to understand the root cause of data anomalies and take your data quality testing to the next level.
Data Observability
Demystifying Data Observability
3 practical examples on how to get started with data observability
Case Studies
How Checkout.com Achieves Data Reliability at Scale with Monte Carlo
Learn how Checkout.com gained visibility into data across domains, scaled data quality checks, and achieved reliability at scale.
Case Studies
How Swimply Built Its Hyper Growth Data Stack with Snowflake, Fivetran, and Monte Carlo
Learn how Swimply, the two-sided experiences marketplace, delivers reliable, trustworthy data with Monte Carlo, Snowflake, and Fivetran.
Case Studies
How The Farmer’s Dog Achieves Self-Serve Data Observability with Monte Carlo
How the data team at The Farmer's Dog, a fresh dog food company, achieves reliable data pipelines with automated, end-to-end data observability.