Root Cause is Half the Job; Remediation Agent Ensures Every Analysis Ends in a Plan
When a monitor fires on a data table or agent, Monte Carlo streamlines the investigation process for you via our Troubleshooting Agent. It gives you a root cause, the evidence behind it, and a verification checklist.
The last step in the resolution process still relies heavily on human input: an engineer still has to decide what to do next, whether that’s tune the monitor or open up their IDE to make an immediate code change.
Monte Carlo’s Remediation Agent now closes that gap, bringing the process resolution one step closer to automation. Every completed Troubleshooting Agent analysis now automatically produces a remediation plan for the alert — a root-cause classification, a summary of what it proposes, and a recommended action you can hand straight to your coding agent or to Monte Carlo’s Tuning Agent. No new setup is required, nor is there a need to invoke any separate agents. If troubleshooting ran, the plan is already there when you open the alert.
Why root cause alone doesn’t resolve anything
Ask any data or AI team what happens after an incident is diagnosed and you’ll hear some version of the same thing: the hard part isn’t understanding the break, it’s routing the work.
An average incident takes many hours to resolve, and much of that is decision-making and handoffs. The responder who caught the alert isn’t the person who owns the agent. The person who owns the agent doesn’t know whether the monitor is trustworthy. No one on the team wants to close an alert they can’t prove is benign, so it then sits open. This happens again and again, adding to the pile of unresolved incidents as agents scale across the business.
Monte Carlo developed a fleet of agents running across the trust platform, coordinated by a central orchestrator, to automate away much of this manual human toil. Remediation Agent is just the latest in this endeavor.
What you can do with Remediation Agent now
Automatic plans on every analysis. Every completed Troubleshooting Agent analysis produces a remediation plan — no extra click or separate run required. The plan states the root-cause classification, summarizes what it proposes, and carries a confidence level so you know how much weight to give it.
A concrete recommended action, not a suggestion to investigate further. Plans resolve to one of a handful of actions: fix the code, re-run the pipeline, tune the monitor, or close the alert with an explanation. When the evidence is too thin to be sure, the plan says so and recommends a hand-off instead of guessing.
Hand-off to your coding agent. When the plan calls for a code fix or a pipeline re-run, you’ll see the option to open it in your AI tool — Claude Code, Cursor, Claude Desktop, or OpenAI. Your agent pulls the full plan through Monte Carlo’s MCP server: the defect, the suspect PR or query change, a concrete suggested fix, and the verification steps. Then it does the work in your environment, including opening the PR that fixes the pipeline code.
Hand-off to the tuning agent. When the plan concludes the monitor is the problem rather than the data or agent itself, you get a Tune monitor button. The tuning agent derives the configuration change from the monitor’s own alert history and applies it. This is the same conservative, evidence-based tuning flow, triggered from the moment you actually need it.
Your permissions, your environment, your call. Monte Carlo proposes; your agent executes. The MCP server runs with your account permissions — no shared credentials — and every action lands behind your coding agent’s own confirmation flow. Monte Carlo never touches your code.
From detection to resolution
Monte Carlo has spent the last year turning observability from a wall of alerts into a fleet of agents that actually do the work: Triage decides what matters, Troubleshooting explains what happened, and Tuning quiets the noise. Remediation is the piece that makes the chain truly actionable; the step where an analysis becomes a pull request, a re-run, or a closed alert.
That’s what agent trust looks like in practice. It’s not an agent that promises to fix everything, but rather a system that reasons transparently, proposes a specific action, shows you the evidence behind it, and hands execution to the tool and the human who owns it.
Remediation plans are rolling out now on the alert page for accounts with Troubleshooting Agent analyses. To hand plans off to your coding agent, connect it to Monte Carlo’s hosted MCP server — configuration only, no installs — and see the Troubleshooting Agent docs for setup. Want to see the whole loop, from detection to PR? Schedule a demo.
