FOR AI ENGINEERS
Your Code Compiles. Your Agent Still Acts.
Mountain Theory is the authorization layer between AI inference and execution. Drops into your stack via SDK or runs in your VPC. Model-agnostic, framework-agnostic, hardware-agnostic. Sub-200ms inline.
Your tests verified what your model does on the inputs you imagined. Production is everything else. Mountain Theory governs the moment between when the model decides and when the action executes, on every framework you ship on.
THE STRUCTURAL GAP
Tests Pass. Production Fails.
Static analysis tells you the code compiles. Unit tests verify functions on the inputs you imagined. Evals score model output on a fixed benchmark. None of that tells you what your agent decides to do when it chains four tool calls together against live production data at 2am.
That gap lives between inference and execution. It is where an agent loops on a tool call until it succeeds. It is where a RAG retrieval surfaces context the model was not supposed to see. It is where a multi-step plan executes step three before anyone notices step two was wrong.
Working in dev is not the same as safe in production.
THE PROOF POINTS
What Authorized AI Has Already Done
Replit | July 2025
An AI coding assistant deleted a production database after being explicitly told not to. The agent was authorized. The deletion was not.
AWS Kiro | December 2025
Amazon’s own AI bot autonomously deleted cloud environments during a routine update. Over $100MM in impact. An outage that lasted 13 hours. No breach. No external attacker. The AI just made the wrong call at machine speed.
OpenClaw | February 2026
Meta’s AI Alignment Lead issued explicit stop commands to an autonomous agent. The agent ignored them. A physical power kill was required to prevent total data loss.
None of these were breaches. All of them happened with properly authenticated AI calling authorized tools.
HOW IT WORKS
An Authorization Layer Between Inference And Execution
Three independent services that evaluate every action your AI tries to execute. Mountain Theory returns one of three outcomes in under 200ms: ALLOW, HOLD for human review, or BLOCK.
Policy AI
The Natural Language Policy Engine. Define what your agent is and is not permitted to do in plain English. Policies enforce everywhere Mountain Theory runs. No custom syntax to learn. No code changes. No infrastructure rebuild.
Guardian AI
The inline enforcement engine. Evaluates every AI action against policy in under 200ms before the action reaches your execution layer. Returns ALLOW, HOLD, or BLOCK with the reasoning context your audit trail requires.
Adjudicator AI
The reasoning layer for scenarios outside known patterns. Evaluates what Guardian cannot resolve and improves the system over time so the same scenario does not need to be re-evaluated on the next encounter.
Three services. Clean APIs. Built for the way enterprise AI actually deploys.
WHERE WE FIT
Drops Into The Stack You Already Built.
Whatever orchestration framework you run, whichever model you call, wherever you deploy. Mountain Theory sits between the decision and the action. The model still decides. The action still executes. We govern the gap.
Agent Orchestration
LangChain. LangGraph. Google ADK. AWS Strands. CrewAI. Microsoft AutoGen and Semantic Kernel. OpenAI Agents SDK. LlamaIndex. Haystack. Dify. Flowise. Composio. Any framework that exposes tool calls or actions, governed before execution.
Model Providers
OpenAI, Anthropic, Google, Meta, Mistral, Cohere, xAI. AWS Bedrock multi-model. Azure OpenAI Service. Google Vertex AI and Model Garden. Self-hosted and fine-tuned. Mountain Theory is model-agnostic. We govern the action, not the model.
Enterprise Platforms
Salesforce Agentforce and Einstein. SAP AI Core, S/4HANA, BTP. ServiceNow. Microsoft Dynamics. Workday. Wherever your AI executes inside enterprise systems, Mountain Theory evaluates the action against policy before the platform commits the change.
Coding agents, RAG pipelines, multi-agent systems, RPA, custom internal tools. If it makes a decision and then executes, Mountain Theory governs it.
DEPLOYMENT
Three Ways To Deploy. Same Architecture.
Same codebase, three form factors. Customer data stays in customer infrastructure.
Embedded SDK
Build Mountain Theory directly into your agent or application. Full programmatic control over how policies apply to your tool calls, retrievals, and action chains. SDK access is gated through the Design Partner Program.
Enterprise VPC
Runs inside your AWS, Azure, or GCP environment. On-prem if required. Deploys via Helm, ECS, EKS, AKS, GKE, or your existing Kubernetes cluster. Customer data never leaves customer infrastructure.
Hybrid Edge
Enforcement at the edge for sub-200ms decisions near the action. Reasoning and policy distribution from a central control plane. Secured low-latency link between the two.
Mountain Theory is built for the way you actually ship.
BUILT FOR PRODUCTION
Inline Performance. Real Production.
Engineered for production AI workloads. Inline does not mean a tax on your inference pipeline.
⚡ Sub-200ms Inline
Median enforcement decision in under 200ms. Engineered so most actions never need a reasoning model. Hot path stays fast. Cold path stays accurate.
🧠 Model Agnostic
Works with GPT, Claude, Gemini, Llama, Mistral, Bloomberg GPT, Bedrock multi-model, Vertex AI, Azure OpenAI, and self-hosted. The authorization layer does not care which model you call.
🖥️ Hardware Agnostic
AWS, Azure, GCP, on-prem, hybrid edge. Lightweight core services run on commodity compute. Reasoning workloads scale on-demand and idle to zero when not needed.
🔁 Bidirectional
Input checks evaluate what goes into the model. Output checks evaluate what comes out before the action executes. Three outcomes at every gate: ALLOW, HOLD, BLOCK. Full audit trail on every decision.
WHAT YOU GET
Built For AI Engineers. Not Security Bureaucracy.
Mountain Theory is not a wrapper, a proxy, a gateway, or a guardrail. It is the authorization layer between AI inference and execution. SDK installs in minutes. Authoring takes plain English. Sub-200ms enforcement on every call.
- ✓SDK installs in minutes, no infrastructure rebuild
- ✓Policy authored in plain English, no custom syntax to learn
- ✓Sub-200ms inline, no tax on your inference pipeline
- ✓Three outcomes at every gate: ALLOW, HOLD, BLOCK
- ✓Structured audit trail on every decision, machine-readable
- ✓Drops into any model, any framework, any cloud
- ✓Bidirectional input and output evaluation
- ✓Patent-pending architecture (USPTO 509032655)
SDK access is gated through Mountain Theory’s Design Partner Program. Limited to selected enterprise teams building on production AI infrastructure.
READY
Want To See How Mountain Theory Would Govern Your AI?
30 minutes. No slides. We walk you through a live demonstration and show you exactly where the circuit breaker would intervene.