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.
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 eliminate context-driven hijacking before it happens.
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
Agents with network or shell access can be redirected toward internal systems, metadata endpoints, or unsafe file paths.
Policy controls enforce network egress controls, filesystem path boundaries, and execution constraints before requests reach sensitive surfaces.
Prevent agents from reaching internal systems, metadata endpoints, or unsafe paths.
Deny requests to localhost, RFC1918 ranges, and cloud metadata endpoints; normalize paths before filesystem access; require allowlisted tools per session.
Regulated workflows and approvals
Teams need more than model logs when decisions affect transactions, records, or regulated operations.
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.
Capture proposed action, authorization basis, identity context, execution outcome, and witness evidence for regulated approvals.
Multi-agent orchestration
As tasks move between agents, authority can drift and tool access can expand in ways teams did not intend.
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.
Bind each agent handoff to scoped authority, reject out-of-scope delegation, and preserve the decision chain across the workflow.
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.