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