Why enterprises choose
Monte Carlo
Monte Carlo is the only agent trust platform that unifies data and agent observability to monitor, troubleshoot, and improve production AI systems.
Trusted by 400+ enterprises
Agent trust has four layers.
Most tools cover one.
An agent in production needs four things watched at once: the context it retrieves, the performance of the systems serving it, the behavior it exhibits step by step, and the output it returns.
Context
Is the data and information the agent retrieves accurate, fresh, and complete? Stale or broken context fails regardless of how capable the model is.
Performance
Is the agent completing tasks efficiently, within expected cost and latency? Slow or expensive agents don't scale, even when technically correct.
Behavior
Is the agent reasoning and acting the way it's supposed to? This is where silent failures hide. Outputs can look fine while the reasoning underneath drifts.
Output
Is what the agent ultimately produces accurate, faithful to its context, and safe? Output is the last line of defense, which is why it can't be the only one.
How Monte Carlo fills the gap
Many enterprises evaluating agent trust solutions already use adjacent tooling. Learn how these tools leave critical gaps in your stack and where Monte Carlo fills those in to provide comprehensive agent trust.
| Good at | Limitations | RecommendedThe Monte Carlo benefit | |
|---|---|---|---|
APM & infrastructure monitoring |
Good at Uptime, latency, errors, and incident routing |
Limitations Availability, not correctness. An agent’s confidently wrong answer throws no error. |
The Monte Carlo benefit Monitors what the agent concluded, why it was wrong, and how it got there, not just success/failure. |
Tracing & evaluation tools |
Good at Tracing and offline evals, golden dataset scoring. |
Limitations Outputs only, one of four trust layers. Scores the answer, not the pipeline behind it. |
The Monte Carlo benefit Context, performance and behavior in addition to outputs — provides the end-to-end picture. |
Native platform monitoring |
Good at Rich telemetry, nothing to install and easy to set up. |
Limitations Telemetry isn’t anomaly detection or root cause analysis. Typically requires querying and therefore not fit for scale. Limited to one platform. |
The Monte Carlo benefit Built for agents at real enterprise scale with anomaly detection, root cause analysis, agentic operations, and remediation. Platform agnostic for vendor neutrality. |
Building it in-house |
Good at Total control, exact fit, no procurement |
Limitations Requires expertise, effort, and cost to build and maintain. Not feasible or economical at scale. |
The Monte Carlo benefit Coverage that scales with your estate, not with the team maintaining it |
| APM & infra | Tracing & eval tools | Native platform | Build in-house | RecommendedMonte Carlo | |
|---|---|---|---|---|---|
Agent traces, tokens, latency |
APM & infra | Tracing & eval tools | Native platform | Build in-house | Monte Carlo |
Output quality evaluation |
APM & infra | Tracing & eval tools | Native platformPartial | Build in-houseBuild it | Monte Carlo |
Tool-call sequence validation |
APM & infraPartial | Tracing & eval toolsPartial | Native platform— | Build in-houseBuild it | Monte Carlo |
Anomaly detection on token usage |
APM & infraPartial | Tracing & eval tools— | Native platform— | Build in-houseBuild it | Monte Carlo |
Monitoring of the upstream data |
APM & infra— | Tracing & eval tools— | Native platformPartial | Build in-houseBuild it | Monte Carlo |
One standard across clouds and frameworks |
APM & infra | Tracing & eval toolsPartial | Native platform— | Build in-houseBuild it | Monte Carlo |
Trace store in your own cloud account |
APM & infra— | Tracing & eval toolsVaries | Native platform | Build in-house | Monte Carlo |
What it looked like in production
How the NY Jets grew their AI deployments by 10X, leaning on Monte Carlo to maintain trust and safety across both the data and agent layers of their infrastructure.
Read the case study: NY Jets
How Axios leans on Monte Carlo to accelerate newsroom velocity via more than a dozen LLM agents, maintaining reporting accuracy and integrity throughout
Read the case study: Axios
How Monte Carlo helps Nasdaq deliver trusted data across 30 exchanges and 2,200 financial institutions, in a heavily regulated industry.
Read the case study: Nasdaq
How Morningstar responds to data alerts 50% faster — and catches its earnings agent drifting toward language that reads as financial advice.
Read the case study: MorningstarMonte Carlo is the only agent trust platform that covers the full stack
With native integrations across the modern data stack, teams get lineage from raw data in the warehouse all the way to the action an agent takes. So when something breaks, you can trace it back to the exact pipeline failure, model change, or context drift that caused it.
Onboard, monitor and ensure agent trust faster than ever
Our 400+ leading enterprise customers are accelerating their time to agent trust using Monte Carlos agentic monitoring
Of all monitoring is agent-deployed
Major global retailer — 0 to comprehensive monitoring in 30 days
To steady-state coverage
Global media brand — full operational rigor on alerts
From plan to production
Leading beverage distributor — monitoring setup for a critical business domain
More real incidents detected
Global bank — and 87% fewer false positives with Monte Carlo agentic operations vs. in-house
Designed for the world’s most regulated industries
Serving customers across finance, healthcare, and beyond, Monte Carlo is designed to meet the strictest regulatory demands—including full SOC 2/3 Type II and ISO 27001:2022 compliance and customer hosted deployments to keep your data secured and your costs low.