AI agent observability across the full lifecycle
The visibility you need to scale agents in production — across the context they draw on, their performance, their behavior, and their output — 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 four layers of agent trust
None of them holds up in isolation, and none can be checked periodically. Agent trust means watching all four, continuously, in production.
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.
Axios
“We were using Monte Carlo to observe our data ecosystem and our ML model predictions, so being able to incorporate agent observability workflows in just a few clicks with the same familiarity for how we set up our monitors, and alerts, and get observability across our whole platform was attractive.”
SS
Shreye Saxena
Senior Data Scientist at 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 shifts
- 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 analysis
- Alert to LLM or tool failures, and timeouts
- Identify bottlenecks and performance degradations
- Maintain model flexibility
- 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
Integrated with your entire agent stack
Instrument any agent on any platform. Monte Carlo ingests agent traces via OpenTelemetry, so telemetry from your models, frameworks, and orchestrators lands in your own warehouse — alongside the data those agents depend on.
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. The same observability graph that traces your agents also monitors the tables, pipelines, and models they depend on — so when an answer is wrong, you can tell whether the agent went wrong or the data did.
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.
Observability is the mechanism. Trust is the outcome.
Instrument your agents in minutes and monitor them alongside the data they depend on. Agent observability is how Monte Carlo delivers agent trust across all four dimensions.
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