Monte Carlo has launched Agent Observability, a unified solution that delivers comprehensive visibility across the full lifecycle of AI agents. This addresses a critical gap as enterprises rapidly deploy AI agents but struggle with production reliability, trust, and operational control. New survey data from Monte Carlo reveals that 73% of enterprises will not ship an AI agent without monitoring and alerting, while 63.4% identify lack of monitoring and observability as a primary barrier to broader AI adoption.
Enterprises are accelerating AI agent deployments but face significant risks from limited visibility into real-world operations. This lack of insight erodes confidence, with secure data handling (68%), performance expectations (62.7%), and failure monitoring (72.7%) ranking as top prerequisites for going live. Without end-to-end observability, teams struggle to detect hallucinations, diagnose latency issues, validate workflows, or pinpoint failure root causes—often stalling promising AI initiatives before production scale.
Monte Carlo Agent Observability stands out by providing a single platform view into context, performance, behavior, and outputs—the interconnected factors that determine agent reliability. This holistic approach enables AI and data teams to understand not only agent results but also the underlying reasons and system health, fostering greater trust and faster issue resolution.
AI agents depend entirely on the quality and accuracy of retrieved data. Monte Carlo allows direct evaluation of AI-generated fields against source warehouse data, with custom prompt-based checks to detect errors or hallucinations early. Expanded BigQuery and AWS Athena support brings observability natively into major cloud environments.
New Agent Metric Monitors capture key signals including latency, token usage, duration, and error rates across entire workflows. Trace-level insights help teams identify regressions, anomalies, and cost overruns proactively.
Complex agent workflows demand strict validation. Agent Trajectory Monitors verify step order, frequency, tool usage, and detect unintended loops or skipped actions. Survey findings show nearly one-third of organizations cannot quickly disable harmful agents, underscoring the need for this governance layer.
Pre-production evaluations test agents against golden datasets within CI/CD pipelines to catch regressions from prompt, model, or code changes. In production, Agent Evaluation Monitors apply LLM-based or rule-based checks, alerting on quality deviations for continuous improvement.
“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.”
Monte Carlo’s Agent Observability empowers enterprises to evaluate agents pre-deployment, monitor live performance and costs, validate workflows, and maintain output quality—accelerating confident, scalable AI agent adoption. A hosted OpenTelemetry option in AWS further simplifies deployment while keeping data in-customer environments.
About Monte Carlo
Monte Carlo created the data + AI observability category to help enterprises drive mission critical business initiatives with trusted data + AI. NASDAQ, Honeywell, Roche and hundreds of other data teams rely on Monte Carlo to detect and resolve data + AI issues at scale. Named a “New Relic for data” by Forbes, Monte Carlo is rated as the #1 data + AI observability solution by G2 Crowd, Gartner Peer Reviews, GigaOm, ISG, and others.
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