Monte Carlo has unveiled Agent Observability, a new capability designed to give data and AI teams end-to-end visibility across the agent stack, addressing one of the most pressing challenges in deploying reliable, production-ready AI systems. The launch also signals the evolution of the data observability category into data + AI observability.
With Agent Observability, Monte Carlo becomes the first vendor to unify monitoring across both data and AI stacks within a single platform. The solution allows teams to measure the quality of AI agent inputs and outputs, detect issues, and resolve failures before they erode customer trust or disrupt revenue-generating products.
“For data and AI teams, reliability isn’t a ‘nice to have,’ it’s the foundation for building scalable, adopted, revenue-driving AI products,” said Barr Moses, co-founder and CEO of Monte Carlo. “Point solutions to solve siloed problems simply won’t cut it anymore. Our customers need a unified approach to ensure their AI agents are behaving as they should, delivering trustworthy outputs, and driving real value.”
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The new capability arrives as enterprise adoption of AI agents accelerates. A recent survey found nearly 80% of data and AI leaders already use agents, yet many struggle to monitor reliability at scale. Gartner estimates that 30% of AI projects are abandoned due to data quality issues underscoring the business risk of unreliable outputs.
Monte Carlo’s Agent Observability addresses this gap with customizable evaluations that can flag poor AI outputs, declining clarity, or task failures. It also includes built-in telemetry tracking prompts, completions, latency, and errors helping teams quickly diagnose root causes while keeping sensitive data within their own infrastructure.
Already trusted by enterprises like NASDAQ, Honeywell, Roche, and Fox, Monte Carlo says Agent Observability strengthens its leadership in reliability, equipping organizations with the oversight needed to deliver accurate, secure, and scalable AI-powered products.