The runtime that lets AI agents act — without acting up
MCPOne is the control layer between your AI agents and everything they can touch. It wraps every tool call, every data request, and every action in a catalog of 1,170 guardrails — so agents stay scoped, safe, and fully accountable inside federal environments.
Agents are only as safe as the runtime they run in
An AI agent that can call tools, read data, and take actions is powerful — and, ungoverned, a liability. The moment an agent can do something real, someone has to be able to say what it may do, prove what it did, and stop it when it's wrong. That layer is MCPOne.
The Model Context Protocol gave agents a clean way to reach tools and data. What it didn't give agencies is the governance to trust that reach — the ability to enforce policy on every call, redact what shouldn't leave, block what shouldn't run, and keep a record a reviewer can stand behind.
MCPOne sits in that gap. It's an MCP-native runtime where every interaction passes through a policy engine before it happens, not a dashboard that reports on damage after. Agents get their capabilities; the organization keeps control.
Ungoverned vs. Governed
- An agent reads a record it was never scoped to see → blocked at the call
- A tool returns sensitive data → redacted before it reaches the model
- An action exceeds policy → halted, logged, and surfaced for review
- An auditor asks "what did it do?" → a complete, per-call trace answers
1,170 controls, organized into 73 categories
The catalog grew from 640 to 1,170 guardrails in its v3 release — a deliberately deep library so a policy owner can compose exactly the posture a mission requires, from permissive internal tooling to locked-down federal production.
12 of 73 categories shown — full catalog available under briefing
Every call is inspected before it happens
MCPOne isn't advisory. It's in the path. An agent's request to read, call, or act is evaluated against the active policy set and either allowed, transformed, or refused — with the decision written to the record either way.
Pre-execution policy check
Guardrails evaluate every tool call and data request before it runs. Enforcement happens at the gate, not in a post-hoc report.
Redact, mask, and constrain
Sensitive fields are stripped or masked on the way through, so agents get what they need and nothing they don't.
Per-agent capability sets
Each agent runs against a defined allow-list of tools and data. Capabilities are granted explicitly, never assumed.
Complete decision trail
Allowed, transformed, or refused — every guardrail decision is logged with context an auditor can follow.
Policy sets you assemble
Mix categories into a posture that fits the mission. Reuse it across agents and environments with confidence.
Protocol-native
Built on the Model Context Protocol, so it governs the tools and agents teams are already adopting — no rip-and-replace.
Specified like infrastructure, sourced from real federal work
MCPOne is engineered against a full written specification and carries genuine federal lineage — which is why it's already listed where agencies buy.
Full engineering specification
Authored as a complete Word and markdown spec — a 12-phase build plan on a TypeScript, Fastify, and PostgreSQL stack, not a prototype bolted together.
ADEPT-M lineage
MCPOne traces to Bravent's Navy SBIR heritage, giving agencies a direct, sole-source path under SBIR Phase III authority.
Listed on Tradewinds
Submitted to the Tradewinds Solutions Marketplace through the SBIR aisle, with a full supporting package — abstract, solution slide, video script, and keyword set.
Give agents the reach they need. Keep the accountability the mission demands. That's the entire job of MCPOne.— Bravent LLC · SBA 8(a) · SBIR Phase III · Tradewinds Solutions Marketplace
Run agentic AI you can actually govern.
Walk through the guardrail catalog with our team and see how MCPOne enforces policy on a live agent workflow — call by call, decision by decision.