Agent deployment models (including MCP) rely entirely on trust-based access controls with no auditable, least-privilege enforcement mechanism to verify or limit what an agent can actually access or modify at runtime. There is no equivalent of OS-level sandboxing, capability tokens, or permission manifests that external parties can inspect and verify. This creates an undetectable and growing gap between intended permissions and actual blast radius, which compounds as agents are granted broader system access.

Agents today operate with implicit trust and no enforceable permission boundaries, meaning a misconfigured or compromised agent can access or modify anything its host credentials allow — with no audit trail or blast-radius containment.

Engineering leads and platform teams at companies deploying AI agents (via MCP, LangChain, custom frameworks) that touch production systems, customer data, or third-party APIs.

Enterprises are blocking agent deployments specifically because they can't demonstrate least-privilege enforcement to security teams or compliance auditors; this is the missing layer that unblocks adoption, and adjacent categories (API gateways, CSPM, IAM) already command large budgets.

MVP is a lightweight proxy/sidecar that intercepts agent tool calls (starting with MCP protocol), enforces a declarative capability manifest (JSON permission scopes per agent), logs every action, and denies out-of-scope calls — ship as an open-source SDK with a hosted dashboard for audit trails.

Subset of the $18B+ cloud security and API security market; every company running production agents (tens of thousands today, millions within 2 years) needs this layer.