MOUNTAIN THEORY VS STRAIKER
New here? The short version: Mountain Theory checks each action an AI is about to take, against rules you write in plain English, before the action runs. Everything below compares that with what Straiker does.
Runtime protection for AI agents, aimed at enterprises and frontier labs. In August 2026 published its own definition of the 'AI agent execution gap' and in September launched Heimdall, a hardened sandbox for high-risk agent evaluation. This is a genuine head-to-head. Both are trying to control what an AI agent does, and the difference is where the control sits and what it can reach.
| Straiker | Mountain Theory | |
|---|---|---|
| What it controls | Runtime protection for AI agents, aimed at enterprises and frontier labs. In August 2026 published its own definition of the 'AI agent execution gap' and in September launched Heimdall, a hardened sandbox for high-risk agent evaluation. | The action an AI agent is about to take |
| Where it sits | Agentic AI security | Inline at execution, between the decision and the action |
| How policy is set | See vendor documentation | Plain English, no code |
| Deployment reach | See vendor documentation | Model and framework agnostic, including custom and on-prem agents |
| Best fit when | Agentic AI security | An AI acting wrongly has physical or regulatory consequences |
Why you might pick Straiker
Runtime protection for AI agents, aimed at enterprises and frontier labs. In August 2026 published its own definition of the 'AI agent execution gap' and in September launched Heimdall, a hardened sandbox for high-risk agent evaluation. If that is the problem in front of you, they are built for it and Mountain Theory is not a replacement for it.
Why you might pick Mountain Theory
Mountain Theory sits at the execution layer, between an AI system's decision and the action it would take. Every proposed action is checked against policy written in plain English and returned as ALLOW, HOLD or BLOCK. It is model and framework agnostic, so it reaches custom and on-prem agents, and it produces an append-only record built to be handed to an auditor.
The honest verdict
Similar problem statement. Evaluate on whether control is model and framework agnostic and whether policy is written by the risk owner.
What we can actually show
Proof
Four things with a published run behind them.
Claims in this category are easy to make and hard to check, so here is ours on the record. The same 10 actions were run in the same order under three configurations. Ungoverned, 10 of 10 executed. Under NVIDIA OpenShell alone, all 5 sandbox-boundary crossings were denied at the kernel, and all 3 in-bounds bad decisions still went through, including a secrets read that printed credentials to the screen. Under OpenShell plus Mountain Theory, those same 3 actions returned HOLD, HOLD and BLOCK, and the secrets read was stopped before it executed, so the credentials never printed. Terminal recordings of all three runs are published, including the two actions Mountain Theory has no policy for.
Separately, when a third-party provider updated the foundation model driving an autonomous agent, the agent began attempting multi-step actions it had never tried before. Nothing on our side changed. Every attempt was stopped on 30 and 31 July 2026, the days the behavior first appeared. No new rule, no signature, no patch.
Both products in this comparison operate at the execution layer. What execution-layer security is, and how it differs from prompt filtering and from access control.
Watch the three-configuration run against NVIDIA OpenShell
See novel agent behavior stopped the day it appeared
Ask Straiker, and every other vendor you are evaluating, for the same four things: the exact action set, the ungoverned control condition, the outcome per action including the ones the product did not stop, and the recording. A certification, an integration list or a customer logo answers a different question.
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