Monte Carlo Bayes: an AI agent that fixes your AI agents

The newest answer to "who watches the AI agents" is another AI agent. On October 28, data observability company Monte Carlo launched Bayes, an AI agent that monitors and fixes AI agents and the data they run on. You can give it as much rope as you want. Our advice for most teams: give it very little at first.

What actually happened

SiliconANGLE reports that Bayes scans a company's environment, finds the agents it has deployed and the data they depend on, and watches for signs of unreliability. When something breaks, it troubleshoots, puts in a fix, and checks that the agent works again.

The Bayes product page lists four autonomy levels:

• Manual: your team drives every step.
• Assisted: a step between Manual and Supervised.
• Supervised: Bayes proposes fixes, your team approves.
• Autonomous: Bayes runs in the background, you handle exceptions.

Alerts arrive as one message with the root cause and a suggested fix. Per CDO Magazine, Bayes is available now to Monte Carlo customers in the platform, in Slack, in Microsoft Teams, and over MCP. The product page names Claude Code and Cursor as MCP clients.

Monte Carlo publishes no price. It says one financial-services customer saw 200% more incidents caught than with its in-house monitoring and 80% fewer false positives. The product page puts the false-positive number at 87%. Those are vendor figures from one unnamed customer, so treat them as a sales pitch, not a benchmark.

Why it matters for your business

Agent monitoring is becoming its own product category. Monte Carlo's own pitch says a typical engineer can keep an eye on only a few agents at a time. Run more than a handful and somebody must watch them, whether that is a person, a dashboard, or another agent.

Bad data breaks agents more often than bad models. A pricing agent fed a stale product table gives wrong quotes with total confidence. Monitor the inputs, not only the outputs.

Autonomy is a setting. Earn it per task. Run Supervised for 30 days. Count the proposed fixes you approved without changes. If that number is high for one type of incident, move that type up a level. Leave the rest where they are.

A fixer agent needs write access. An agent that can deploy fixes can also deploy bad ones. Scope its credentials to the systems it repairs, and log every change it makes.

Sources: SiliconANGLE, CDO Magazine, Monte Carlo.