Operations Agent Increases Data + AI Reliability Team Velocity 50% 

Turn data health insights into actionable frameworks in half the time with an AI agent built specifically for data + AI teams

A new, powerful data + AI observability partner

Monte Carlo’s engineering team was planning to migrate a few key data ingestion pipelines— the kind of change that can ripple unpredictably across downstream assets. Analytics lead Terry Widger needed to prepare and understand the blast radius.

“An upstream raw schema was going to be restructured, and I needed to know what assets were going to be impacted. We have client-facing reports that we populate, and it doesn’t look good for a data + AI observability solution to have stale dashboards,” said Terry.

Ordinarily he’d turn to Monte Carlo’s bi-directional lineage and asset pages. And while that provides considerable time savings from more manual exploration, he decided to use Monte Carlo’s new Operations Agent. 

With just three quick questions he learned two schemas were directly downstream of the raw schema to be restructured and that all but two tables would be impacted.

“I didn’t know all of the 200 tables in this corner of our environment by hand and I was ready to dedicate my afternoon to learning,” said Terry. “Instead the Operations Agent gave me everything I needed to prepare for this migration in ten minutes.”

Introducing Operations Agent

Today, Operations Agent is now available in public preview to all Monte Carlo users. It joins Troubleshooting Agent and Monitoring Agent, both of which are generally available, to extend the AI native capabilities within the Monte Carlo platform. 

As data + AI becomes increasingly critical to business operations, teams are struggling to keep up with the operational discipline and rigor required to move from reactive firedrills to proactive execution. Significant time is spent manually stitching together lineage views, pipeline performance, quality alerts, and tribal knowledge. 

Operations Agent is an AI agent that accelerates a wide range of monitoring and incident management workflows using simple natural language queries. It deeply understands the user’s data + AI environment and removes friction by providing immediate, actionable answers.

For example users can ask “Are there any alerts assigned to me?” and request “Update this alert status to acknowledge.” 

Other examples include:

• Improve coverage: Identify unmonitored assets and get smart monitoring recommendations. Want YAML to deploy as part of your CI/CD process? Just ask.
  • Estimated time savings: 2 hours per asset. 9 hours per data product.

• Optimize incident response: Understand which audiences are getting the most alerts, and which monitors have no associated audience. Build an alert runbook or monitoring routing action plan for your team with a single prompt.
  • Estimated time savings: 8 hours per domain

• Lineage dependencies: Insightful summaries of upstream relationships, downstream impact, and everything in between. Ask what business processes are powered by a key table or agent, understand what assets will be impacted by changing schemas.
  • Estimated time savings: 4 hours per schema

• Pipeline, agent, & table health: Pinpoint failing pipelines, assets with unresolved incidents, agent reliability trends, and recurring issues. Create a two day sprint to improve data health.
  • Estimated time savings: 16 hours per pipeline health sprint

• Monte Carlo assistance: A fully integrated Monte Carlo AI-powered support assistant provides step-by-step guidance for monitors-as-code, circuit breakers, and other features — empowering technical and non-technical users alike.
  • Estimated time savings: 1 hour per operation

Context Aware, Securely Governed

Operations Agent uses Monte Carlo’s APIs to surface real-time context including information surrounding monitors, alerts, and asset metadata or lineage. Users can also select “Context” to provide comprehensive, detailed background on a specific asset as part of a request.

The agent is powered by Anthropic’s Claude Sonnet 4.5, securely hosted on AWS Bedrock, and monitored via Monte Carlo’s Agent Observability solution. 

Importantly, all information provided by the Operations Agent is governed by domain and role based access control settings and customer data is never used for model improvement.

We want to hear from you!

Operations Agent is just the latest step to creating agent-native observability. Monte Carlo has multiple agents currently in development designed to fit the changing ways data + AI teams are driving value to their organization.

If you are a user that has found a particularly interesting use case for Operations  Agent or you are tasked with improving data reliability and interested in receiving a demo–we want to hear from you! 

Join our live demo to see Operations Agent in action and experience how natural language and AI can transform the way your team observes, understands, and optimizes data quality. You can also learn more and request a more personalized demo of the larger platform by visiting the product page.

Our promise: we will show you the product.