MOUNTAIN THEORY VS ZENITY
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 Zenity does.
Zenity is the closest competitor to Mountain Theory on positioning. Both control AI agents inline rather than watching from the side, and Zenity now says it secures agents at the decision. Mountain Theory checks the action after the decision and before it executes, and is model and framework agnostic. Two differences a buyer can test: how each one reaches an agent, and what happens to an action that needs a person.
| Zenity | Mountain Theory | |
|---|---|---|
| What it controls | Agent actions across connected platforms and instrumented agents | The action an AI agent is about to take |
| Where it sits | Inline, in the platform or through the agent's telemetry and an Evaluate API | Inline at execution, between the decision and the action |
| How policy is set | Boundaries: rules as version-controlled files, an assistant drafts them | Plain English, no code |
| Deployment reach | Copilot, ChatGPT Enterprise, Agentforce, Bedrock, Foundry, Agent 365, Vertex AI, AgentKit, GitHub Copilot, Codex, MCP; custom agents that emit OpenTelemetry spans | Model and framework agnostic, including custom and on-prem agents |
| Best fit when | Your agents live in major vendor platforms or already emit OpenTelemetry | An AI acting wrongly has physical or regulatory consequences |
Why you might pick Zenity
Zenity is live inline in production with Fortune 500 customers, a Gartner Cool Vendor in Agentic AI TRiSM, and named in the 2026 Latio and SACR agent security reports. Its runtime engine, Boundaries, writes a rule once and enforces it across Copilot, ChatGPT Enterprise, Agentforce, Bedrock, Foundry and Agent 365, Vertex AI, OpenAI AgentKit, GitHub Copilot and Codex, and MCP servers. Since August a home-grown agent can connect without a dedicated connector if it emits OpenTelemetry gen_ai spans, with inline enforcement through an Evaluate API and guides for Cribl, LiteLLM and Kong. Its research arm keeps finding real holes: PleaseFix, Comet, and SalesBleed in Salesforce Agentforce on September 24th, 2026. If your agents live in those platforms, Zenity is close to one-click.
Why you might pick Mountain Theory
Zenity reaches an agent through the platform it runs on or, for a home-grown agent, through telemetry the agent emits and an Evaluate API call the developer wires in. Mountain Theory checks the action between the agent's decision and its execution, whatever built the agent and wherever it runs, including a Python script on your own hardware that emits no telemetry and routes through nothing Zenity can see. The second difference is the outcome set. Zenity's published outcomes are let it through, block it, or quarantine the agent; nothing in its public copy hands an action to a person. Mountain Theory's third outcome holds the action for a person, where your policy asks for one, and the person's decision goes on the record.
The honest verdict
Zenity covers agents on the big platforms well, and since August it can see a home-grown agent that sends it telemetry. The moment an agent runs where none of that is wired, a LangChain agent on your own infrastructure or a Python script that calls your API directly, it is outside what Zenity can reach, and it still holds your production credentials. Mountain Theory governs that agent because it checks the action itself, not the platform and not the telemetry. And when an action should not run without a person, Mountain Theory has an outcome for it: hold it, ask, record the answer. Zenity's published choices are allow, block or quarantine.
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 July 30th and 31st, 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 Zenity, 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.
Compare all 77 AI security vendors