Open Arsenal

Before execution, Stratum.

Stratum is an AI governance layer designed for MCP (Model Context Protocol) systems that need structured control over how AI agents interact with tools and external systems. It sits between model intent and execution, acting as a policy-driven boundary that determines what an AI is allowed to do, what requires approval, and what must be blocked entirely. The goal is to make autonomous and semi-autonomous AI systems safe, auditable, and predictable without limiting their usefulness.

At its core, Stratum combines permissions, policy enforcement, and context management into a single control plane. It supports fine-grained access control through RBAC and ABAC models, allowing organizations to define exactly which tools an agent can access and under what conditions. A central policy engine evaluates every request in real time, deciding whether to allow execution, require modification, request human approval, or deny the action outright.

Stratum also introduces a context filtering layer that ensures sensitive or unnecessary data is never exposed to the model in the first place. This includes redacting private information, filtering tool outputs, and defending against prompt injection attempts. By controlling context at the source, Stratum reduces risk before it ever reaches the reasoning layer.

For high-risk or regulated workflows, Stratum provides approval chains and execution sandboxing. Requests that exceed defined risk thresholds can be routed through human-in-the-loop workflows or isolated execution environments where tools run under strict constraints such as network isolation, resource limits, and timeouts. Every action is logged through an immutable audit system, creating a full trace of prompts, decisions, tool calls, and outputs for compliance and debugging.

Together, these features position Stratum as a foundational governance layer for agentic systems—ensuring that AI does not just act intelligently, but acts within clearly defined structural boundaries.