Monte Carlo’s New Agent Observability Delivers End-to-End Visibility Across Context, Performance, Behavior and Outputs

Monte Carlo today announced new Agent Observability capabilities that give AI and data teams unified visibility across the full lifecycle of AI agents.

Enterprises racing to deploy AI agents are discovering they lack visibility into how those agents actually operate in production environments. That gap is eroding trust, with 53% of enterprises already expecting to significantly rebuild or redesign AI agent systems they have already deployed, according to Monte Carlo’s new survey of AI engineering leaders and practitioners.

The stakes are just as high going in, as enterprises cite secure data handling (68%), clear performance and latency expectations (62.7%) and monitoring with alerting for failures (72.7%) as top requirements before an agent goes live. Yet most lack the tools to meet them.

Addressing this critical gap, Monte Carlo’s Agent Observability is now the only solution in the market to provide unified visibility across four critical pillars that determine whether AI agents can operate reliably in production: context, performance, behavior and outputs. By monitoring these interconnected elements within a single platform, AI and data teams can understand not only what an agent produces, but also why it produced it and whether the underlying system is operating as intended.

Without this end-to-end observability across the entire data and agent stack, teams struggle to detect hallucinations, diagnose performance issues, validate workflow execution or identify the root cause of failures. As a result, many promising AI initiatives stall before reaching production, limiting the ability of enterprises to realize meaningful outcomes from AI.

“AI agents are moving into production faster than most companies are prepared for,” said Barr Moses, co-founder and CEO of Monte Carlo. “The future isn’t coming — it’s already here. If you’re deploying agents without a production-grade observability system that monitors context, performance, behavior and outputs, you’re flying blind. The companies that build trustworthy AI systems will move ahead quickly, and everyone else will fall further behind.”

Customer Spotlight

Axios is using Monte Carlo Agent Observability to ensure accuracy and efficiency in its AI-powered content tagging initiatives. The company is using OpenAI to automatically tag articles so that advertising is relevant and stories reach the right audiences. Axios initially built a manual validation process using a second OpenAI call, but managing costs and gaining visibility into telemetry and logs was challenging. Monte Carlo gives Axios the observability to expand across 12 additional LLM applications.