USE CASE
Runtime Control for Autonomous Agents
Let your AI agents act on their own. Stop the one action that should never happen.
Autonomous agents are the fastest growing thing in technology. They read files, send messages, move data, and run code on their own, with little human oversight. Companies want them because the productivity is real.
The problem is that an autonomous agent does not always do what it was told, and when it goes wrong, it goes wrong at machine speed. In February 2026, an agent ignored direct stop commands and kept deleting a corporate inbox until someone physically powered off the machine. Replit’s agent deleted a production database after being told not to, eleven times. None of these were attacks. They were authenticated agents taking destructive actions faster than any person could intervene.
This lands hardest on the companies building and deploying agents for clients. When a client asks for an agent that can act on its own, the honest answer today is often: do not let it run autonomously, it is too open. They have to say no, because the agent cannot be controlled once it has the power to act. Mountain Theory changes that answer to yes. They stop saying “do not let it run autonomously” and start saying “we will deploy it securely.”
- ✓Runtime control over what an autonomous agent is allowed to do
- ✓Intercepts destructive and irreversible actions before they execute
- ✓Model and runtime agnostic, starting with Hermes
- ✓Policy written in plain English through the Natural Language Policy Engine
- ✓Full audit trail of every allowed, held, and blocked action
FRAMEWORK AGNOSTIC
Built For Any Agent. Starting With Hermes.
Mountain Theory is model and runtime agnostic by design. We start with Hermes, the autonomous agent from Nous Research and the most used agent in the world by OpenRouter usage. Hermes remembers across sessions, writes its own skills, and updates itself over time. That self-updating behavior is exactly the risk. What you told the agent on day one may not hold on day ninety. That makes it the right place to prove the control. Every other autonomous framework follows.
EXAMPLE SCENARIO
The AI Vendor Risk Analyst
Take an agent a security team would build: an AI vendor risk analyst, a non-human employee that runs a full vendor review end to end. It receives a new vendor request, collects SOC 2 reports and security questionnaires, extracts the controls, assigns risk ratings, opens remediation tickets, updates the compliance platform, and sends approval recommendations.
These actions touch compliance, procurement, security, and audit evidence. It is exactly the kind of autonomous work clients are asking for, and exactly the kind a builder is afraid to turn loose without control. Mountain Theory governs what it is allowed to do at every step.
HOW IT WORKS
Three Outcomes At Every Checkpoint
Mountain Theory sits at the execution layer, between the agent’s decision and the action it executes. Every action passes through first, evaluated in real time against policy set in plain English.
ALLOW
- Read SOC 2 reports
- Read bridge letters
- Read vendor questionnaires
- Create draft risk assessments
HOLD — Requires Approval
- Approve high-risk vendors
- Accept compensating controls
- Close remediation findings
- Change risk ratings above threshold
BLOCK — Denied
- Modify security policies
- Delete audit evidence
The agent reads and drafts freely. The moment it moves to approve a high-risk vendor, the action holds for human sign-off. The moment it moves to delete audit evidence, it is blocked. The agent cannot route around the control. Sub-200ms, so the agent stays fast, and every action is logged.
WHY IT MATTERS
Set It Day One. Enforced Day Ninety.
Autonomous agents change on their own. What you set on day one, Mountain Theory still enforces on day ninety. Identity tells you who an agent is. Mountain Theory controls what it does. Every major AI failure happened with properly authenticated AI that took the wrong action. Identity was not the gap. Execution was.
For a company building autonomous agents, one catastrophic action means owning the fallout. Mountain Theory removes that risk. The destructive action never executes, and there is a full audit trail to prove it.
Bottom line: deploy autonomous AI with confidence. Every action runs through one control, every decision is logged, every policy is written in plain English.
See how this plays out in a real incident: read the related case studies in our Threat Lab.