Observe the entire agent lifecycle
The visibility you need to rapidly scale agents in production – from data and inputs, to behavior and outputs – in one unified platform.
AI failures don’t announce themselves
Without monitoring across the entire agent stack, enterprise teams risk deploying AI that hallucinates, deviates from instructions, or leads to costly performance issues.
Monte Carlo is the only agent observability platform providing the unified view needed to ensure AI operates reliably in production.
The only platform to unlock visibility across the agentic stack
Context
Validate the data and signals powering your agents
Performance
Monitor and resolve errors, latency, and operational inefficiencies
Behavior
Observe full agent paths to ensure they don’t go off track
Outputs
Evaluate agent quality before issues reach end users
“It’s important for us to be able to monitor all dimensions of agent reliability including the context, behavior, and outputs If the injected data is incorrect, or if the trajectory is wrong and it produces a response without checking the database, then we’re not going to get the outcomes we need from our agent.”
— Shreye Saxena, Senior Data Scientist, Axios
Monitor, trace, and troubleshoot AI at scale
Silent regression. Incomplete context. System failure. Agents can break in all kinds of ways. With agent observability, you can detect issues fast and root cause in minutes.
- Leverage anomaly detection to detect meaningful shiftsÂ
- Deploy customizable LLM-as-judge evaluations, or use templates for relevancy, prompt adherence, and more
- Target specific spans and calls, scale using stratified sampling
- Map agent decisions step-by-step for explainability
- Gain insight into configuration changes for fast root cause analysisÂ
- Alert to LLM or tool failures, and timeouts
- Identify bottlenecks and performance degradations
- Maintain model flexibilityÂ
- Avoid lock-in by leveraging flexible OpenTelemetry framework
- Integrate with any agents on any platform
- Reduce data related disruption to your agents by +80%
- Understand how changes in pipelines affect agent behavior
- Enhance collaboration across data + AI workflows and teams
Integrate seamlessly with your

entire data + AI ecosystem
See Agent Observability in action
Bridge the gap between AI and its data
An agent is only as good as the context that feeds it. Gain confidence in your agents by monitoring them alongside the data and pipelines that feed them. Be proactively alerted to low quality data and be the first to know about model drift. Quickly fix issues before they impact AI products.
Investigate workflows in production
Make sure your agents behave reliably in production. Trace every run with detailed telemetry across prompts, completions, user queries, latency, and errors. Then deploy customizable LLM-as-judge or deterministic evaluations to detect low quality responses quickly and cost effectively. Get alerted to performance degradations and system failures.
Store telemetry in your own environment
Telemetry data is sensitive. Maintain security, compliance, and auditability by storing it within your trusted warehouse or lakehouse. Get the insight you need across distributed models and architectures using Monte Carlo’s easy to deploy instrumentation.
Request a demo
If you’re exploring how to operationalize AI, we can help. Set up time to talk with our experts.
Get started fast—scale faster.
Fast setup—even faster time to value. Connect to Monte Carlo in seconds, start monitoring out of the box and automatically scale with your environment.