tools

Monte Carlo Agent Observability

Monte Carlo monitors AI agents across context, performance, behavior, and output, with tracing, evaluations, alerts, and data lineage.

In inglese

Monte Carlo Agent Observability helps AI, data, and engineering teams monitor agents in production. It traces prompts, completions, user queries, tool calls, latency, errors, and retrieved context, then connects agent outputs to the data and pipelines behind them.

Teams can use customizable LLM-as-judge or deterministic evaluations, anomaly detection, alerts, and step-by-step tracing to investigate failures. The product is sold through credit-based commercial tiers with consumption-based pricing; exact prices are not published on the pricing page.

Features

Trace prompts, completions, tool calls, context, latency, and errors
Run customizable LLM-as-judge and deterministic evaluations
Use templates for relevance and prompt-adherence checks
Detect meaningful shifts with anomaly detection
Map agent decisions step by step for troubleshooting
Alert on LLM failures, tool failures, timeouts, and degradation
Ingest traces through the OpenTelemetry framework
Store telemetry in the customer's warehouse or lakehouse

Use cases

Monitor agent quality and reliability in production
Investigate incorrect answers and failed tool calls
Trace poor outputs back to stale or broken source data
Evaluate relevance, prompt adherence, and task completion
Identify latency, cost, and performance bottlenecks
Alert teams when agent behavior or output quality changes

Pros

Cons

Latest updates

2025-09-09 Monte Carlo Launches Agent Observability to Help Teams Build Reliable AI
Provides end-to-end visibility across the data + AI stack to detect, triage, and resolve AI reliability issues in production.

Capabilities

Ask in plain language — “A natural-language interface to the full platform — Assist, Troubleshoot, and Support modes.” source
Connects to your data — “Seamlessly connect to Snowflake, Databricks, AWS, and 50+ more.” source
API — “For agents you've built yourself, your team deploys the trace store into your own cloud with one Terraform module and instruments with our SDK, which the Agent Toolkit handles from your editor.” source
Self-hosted — “In your own cloud account — either your warehouse or lakehouse, or a self-hosted trace store you deploy.” source

Security

SOC 2 Type II — “Monte Carlo SOC 2 Type 2 Report 2026.pdf” source
SOC 2 — “Monte Carlo SOC 2 Type 2 Report 2026.pdf” source
ISO 27001 — “Monte Carlo Data ISO 27001 27017 27018 Certificate - 2026.pdf” source

Pricing

Prices checked: 2026-09-25

Official website