Introducing Agent Observability: Making AI Work in the Real World

In development, AI agents look great.

They generate the right answers, complete the right tasks, pass internal evaluations. But once they hit production, everything changes. 

Suddenly, your clean examples are replaced with noisy real-world data. Context shifts. APIs break. Models drift. And without warning, responses start to degrade. Relevance drops. Tasks go unfinished. Performance suffers, and no one knows why.

It’s no wonder a recent MIT report found that 95% of generative AI pilots are failing. Most agents don’t fail because of a single bad prompt. They fail because the systems around them are constantly in motion:

Data pipelines evolve

• Prompts are modified
• Chunking and embeddings behave unexpectedly
• Models are changed without notice
• And users ask unexpected questions

When something breaks, it’s hard to know whether the issue lies in your data, your model, your code, or somewhere in between. Even small regressions can trigger fire drills, delay business goals, and stall adoption.

Without end-to-end monitoring across your data + AI estate, the jump from experimentation to production becomes a leap of faith. That’s the gap Agent Observability was built to close.

Meet Agent Observability

Today, we’re introducing Agent Observability, a new capability from Monte Carlo that gives teams visibility into agent performance and reliability, from the data powering your agents to the outputs they generate.

Built directly into our data + AI observability platform, Agent Observability gives you a unified view of the entire stack—from source data to model output—so when issues arise, you can find them fast, fix them faster, and get back to building.

With Agent Observability, teams can:

• Monitor agent outputs and performance at scale, with alerts on quality shifts using customizable AI agent evaluation monitors, smart sampling, and anomaly detection.
• Maintain control and compliance by storing telemetry, like prompts, token usage, latency, and errors, securely in your own warehouse or lakehouse.
• Ensure the quality of model outputs and inputs by observing and monitoring the entire data + AI estate within a single platform.

This isn’t just about catching errors. It’s about building AI that’s trustworthy, explainable, and production-ready.

Monitor Agent Outputs and Performance at Scale

Waiting for users to report issues isn’t a strategy. In production, even small degradations can cascade into major business disruptions, especially when they go undetected. Manual evaluations don’t scale, and silent regressions often slip through during model updates, orchestration changes, or prompt tweaks.

Agent Observability gives you proactive, scalable monitoring so you can:

• Run custom evaluations tailored to your agents and business logic, or use built-in templates for metrics like response relevancy, helpfulness & clarity, prompt adherence, language match, task completion, and more.
• Catch silent regressions introduced during prompt changes, model updates, or orchestration tweaks.
• Scale efficiently with output sampling, token + latency tracking, and span duration monitoring so you can focus resources where it matters most.

Maintain Control and Compliance

The AI stack evolves fast. New models, orchestrators, and frameworks emerge daily. Teams need observability that’s:

• Flexible enough to support any architecture
• Secure enough to meet enterprise requirements

Agent Observability delivers both. 

Built on a flexible OpenTelemetry framework, it consolidates telemetry—prompts, completions, latency, and errors—from any model or orchestrator. 

And unlike most AI observability tools, Monte Carlo stores your telemetry within your own warehouse or lakehouse, not a third-party platform. That means:

• Greater control
• Stronger governance
• Enterprise-grade compliance
• No sacrifice in visibility

Ensure the Quality of Model Outputs and Inputs

Gain visibility into your entire data + AI lifecycle, from source to model output, so you can trust your agents in production and fix issues at the source. With Agent Observability, Monte Carlo provides:

• A single pane of glass across your entire stack, enabling consistent workflows and visibility for both data and AI teams
• Faster root cause analysis across pipelines, prompts, and models, so you can restore trust and accelerate adoption

The Future of Observability Is Data + AI

Observability isn’t optional in the AI era. It’s foundational.

Monte Carlo is the first platform to unify observability across the entire data + AI stack, providing a single source of truth for everything from pipeline health to prompt behavior.

Because your AI is only as good as the data behind it…and the monitoring that protects it.

Agent Observability is a key step in Monte Carlo’s vision: a world where data and AI operate as one system, not two disconnected workflows.

We’re the only platform delivering true observability across pipelines, prompts, models, and outputs, so you can deploy with confidence, resolve issues faster, and scale AI responsibly.

See It in Action

Want to be the first to try it out? Join the waitlist now. Ready to learn more how Agent Observability can help your team ship reliable, explainable, AI without the firefighting? Join our Live Demo to learn more.

Our promise: we will show you the product.