Monte Carlo Launches Bayes, an AI Teammate That Fixes Production AI Agents and Their Data

The agent trust platform delivers self-improving agents, so enterprises can scale trust at the speed of AI

SAN FRANCISCO, Oct. 28, 2026 (GLOBE NEWSWIRE) -- Monte Carlo, the leader in agent trust, today launched Bayes, an AI teammate that brings its platform capabilities into a single system to autonomously detect, troubleshoot, and fix issues across agents and the data they run on. Customers decide how much Bayes handles on its own, and agents improve with every fix.

Enterprises are deploying agents into production faster than they can keep them reliable. In a recent Deloitte survey, 70% of leaders said they don’t feel they can trust and govern their agents, and a 2026 Sinch study found that 74% of enterprises had rolled back or shut down a customer-facing AI agent after deploying it.

A typical AI engineer can keep only one to three agents and a handful of critical pipelines under real watch, as Monte Carlo has observed across its customer base. Every new deployment adds evaluation and debugging work existing teams can’t absorb. Trust erodes, scaling stalls, and AI ambitions go unrealized.

“It’s important for us to be able to monitor all dimensions of agent reliability including the context, behavior, and outputs,” said Shreye Saxena, Senior Data Scientist at Axios, a Monte Carlo customer. “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.”

Bayes provides the unlock. Acting as the central platform brain, it rapidly learns the unique landscape of a customer’s data and AI environment and deploys appropriate monitoring strategies. Like any new teammate, it takes on as much as the customer is ready to hand over — from a single task, like troubleshooting an alert, to a standing mandate to set up monitoring and triage, to executing fixes in the background.

Bayes covers the entire stack, from the outputs agents produce, up through the decisions they make, the context they retrieve, and ultimately all the way up to the data feeding them.

“Self-improving, autonomous agents are the holy grail every enterprise is chasing, but pulling it off has been elusive,” said Sanjeev Mohan, Principal, SanjMo, and former VP of Research at Gartner. “You have to see what agents actually do at runtime and trace it back through the system, all the way to the data. Monte Carlo has spent years on data observability and then agent observability, so Bayes feels like the next logical step. It builds trust from the data up, not the top down.”

Capabilities include:

• Catching the agent failures real users hit in production, across traces and evals, without relying on golden datasets.
• Taking action and resolving issues as they emerge. Bayes triages alerts directly and delivers agent fixes as draft pull requests or tickets for engineers to merge, then confirms the fix worked – so agents improve with every cycle.
• Ensuring the data pipelines, tables, and products that agents depend on are reliable by autonomously configuring and tuning monitoring coverage based on what matters in each environment.
• Working inside the tools engineers already use. Through MCP, the AI teammate connects to AI coding assistants and agent-first tools across the CI/CD pipeline, so resolutions execute directly within existing development workflows.
  
  

“Every enterprise wants more agents in production, and every one of them hits the same wall: there aren't enough engineers to keep those agents trustworthy,” said Barr Moses, co-founder and CEO of Monte Carlo. “Bayes changes that math. It understands your agents and your data together, fixes what breaks, and gets better at your environment every day. Teams can now spend less time firefighting and more time shipping.”

Monte Carlo’s customers and partners are already seeing the impact, with Bayes helping enterprises detect 200% more incidents than their in-house systems, while flagging over 80% fewer false positive alerts.

Bayes also extends Monte Carlo’s partnership with Snowflake, which named Monte Carlo its 2026 Data & AI Governance Partner of the Year. Together, the two companies help joint customers build and run reliable agents on Snowflake.

“As enterprises put Cortex Agents into production across the AI Data Cloud, trust in both the agents and the data behind them is essential to realizing the value of AI,” said Venkat Suru, Principal Partner Solutions Engineer at Snowflake. “Snowflake provides a governed foundation for that work, and Monte Carlo extends that foundation to observe, fix, and continuously improve agents and the data that powers them. Bayes is an exciting next step in our partnership, helping joint customers scale trusted AI with confidence.”

Availability

Bayes is available today to Monte Carlo customers directly in the platform, in Slack, Microsoft Teams, and through MCP, so teams can work with it in the tools they already use. To learn more, visit montecarlo.ai.

About Monte Carlo

Monte Carlo is the agent trust platform enabling enterprises to observe, fix, and continuously improve production AI systems. Bayes, Monte Carlo’s AI teammate, learns each customer's unique environment to autonomously resolve issues as they surface across agents and the data that powers them. As a result, engineering and data teams spend less time firefighting and more time shipping, so enterprises can scale trust at the speed of AI. More than 400 enterprises, including Amazon, PepsiCo, CNN, Nasdaq, and JetBlue, rely on Monte Carlo. Learn more at montecarlo.ai.

Media Contact

Virna Sekuj
vsekuj@montecarlo.ai