MOUNTAIN THEORY VS GEORDIE AI
Geordie won the RSAC 2026 Innovation Sandbox with a context engine that maps agentic footprint and applies automated mitigations. Platform-level mapping versus inline action control.
| Geordie AI | Mountain Theory | |
|---|---|---|
| What it controls | Agentic footprint and risk | The action an AI agent is about to take |
| Where it sits | Platform level, mapping | Inline at execution, between the decision and the action |
| How policy is set | Automated mitigation rules | Plain English, no code |
| Deployment reach | Enterprise agentic estate | Model and framework agnostic, including custom and on-prem agents |
| Best fit when | You want mapping plus automated mitigation | An AI acting wrongly has physical or regulatory consequences |
Why you might pick Geordie AI
Geordie won the RSAC 2026 Innovation Sandbox, which is real distribution and has historically predicted category leaders. Founder pedigree from Snyk, Veracode and Darktrace lowers buyer skepticism considerably, and they are well funded for their stage.
Why you might pick Mountain Theory
Geordie maps and mitigates at the platform level through a context engine. Mountain Theory sits between inference and execution and stops the action in real time. Mapping the footprint and gating the action are different layers of the stack.
The honest verdict
Geordie maps your agentic estate and applies mitigations at the platform level, and the RSAC win means real buyers are looking at them. Mapping tells you where the risk is. It does not stand between an agent and the action it is about to take. If you already know roughly where your exposure sits and need something to stop the action, that is a different layer of the stack.
What we can actually show
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 behaviour first appeared. No new rule, no signature, no patch.
Watch the three-configuration run against NVIDIA OpenShell
See novel agent behaviour stopped the day it appeared
Ask Geordie AI, 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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