Map agent risk to the right control layer
Use McpVanguard when MCP tool calls need runtime enforcement. Use VEX when consequential actions need explicit authorization, review, and evidence.
Block unsafe tool calls before execution.
Authorize consequential actions and preserve evidence.
Enforce live policy and preserve high-risk decision records.
A risk becomes manageable when its control point is explicit.
Start with the action that could cause harm, then place the enforcement or authorization boundary before that action reaches the system it can affect.
Agent proposes
Tool, target, arguments, and requested scope enter the workflow.
Boundary evaluates
McpVanguard or VEX applies the relevant control path.
Decision returns
The flow produces an explicit allow, warn, review, or block outcome where supported.
Team reviews
The routed action leaves a scoped record for operations or governance.
Internal copilots and knowledge agents
Metadata poisoning and unsafe retrieval can turn a helpful internal assistant into a data-leak or policy-bypass vector.
McpVanguard inspects inbound MCP requests and selected server metadata before execution, with deterministic enforcement at the tool-call boundary.
Control tool use and reduce context-driven hijacking risk before execution.
Block untrusted tool metadata before model exposure; restrict retrieval tools to approved knowledge sources; alert on sensitive data in outbound parameters.
Cloud and platform agents
Policy controls enforce network egress controls, filesystem path boundaries, and execution constraints before requests reach sensitive surfaces.
Constrain routed agent access to internal systems, metadata endpoints, or unsafe paths when the configured policy and deployment boundary support it.
Regulated workflows and approvals
VEX Protocol wraps governed actions in reviewable evidence that can support governance, audit, and post-incident review.
Clear documentation, independent reviewability, and accountable records for governed actions that matter.
Multi-agent orchestration
Governed execution makes permission boundaries explicit and keeps enforcement outside the model's own reasoning path.
Maintain clear separation of responsibility as agent systems grow more complex.
Ready to secure your AI infrastructure?
We work with teams that need a cleaner control model for agent systems before their agents act on real systems.