Monte Carlo, the data + AI observability platform, announced the launch of observability agents, a suite of AI agents built to accelerate monitoring and troubleshooting workflows to improve data + AI reliability. Monte Carlo’s Monitoring Agent recommends data quality monitoring rules and thresholds, which can then be deployed with the push of a button. The Troubleshooting Agent investigates, verifies, and explains the root cause of specific data quality issues while also providing the recommended next steps for resolving them. Both agents are the first of their kind in that they are not just making simplistic recommendations based on data profiles, but leveraging a sophisticated network of LLMs, native integrations and subagents to gain full visibility into the data estate across data, systems, transformation code, and model outputs. Monte Carlo’s Monitoring Agent, now generally available, identifies sophisticated patterns and relationships across a dataset that would otherwise be missed by more traditional profiling methods. Monte Carlo’s Troubleshooting Agent, with a general release scheduled for Q2 2025, investigates, verifies, and explains the root cause of specific data + AI quality issues. The agent tests hundreds of different hypotheses across all relevant tables within a dataset to understand if the root cause of a specific issue is a result of receiving bad data from the source, an ETL system failure, a transformation code mistake, or incorrect model output. The observability agents automate powerful monitoring and resolution tasks, but never directly manipulate, change, or act upon your critical data and key systems (read-only). This ensures they don’t create more reliability issues than they help resolve.