Outerlimit Secures $16M to Build ZeroTrust Security Layer for Autonomous AI Agents

Outerlimit has secured $16M in pre-seed funding, led by Albion VC, to deploy a zero-trust enforcement layer for autonomous AI agents. The solution targets the agent-action boundary—the critical interface where LLM-based agents invoke external tools and APIs—to prevent unauthorized tool execution, data exfiltration, and model poisoning. By injecting a Policy Enforcement Point (PEP) sidecar using an OPA-compatible Domain Specific Language (OPAAgent) and WebAssembly (WASM) policies, the platform provides continuous, real-time authentication and authorization. The architecture leverages hardware-rooted attestation to bind agent identity and action context to trusted anchors, ensuring rigorous control over agentic workflows.

• Security Research/Tooling: Agent-Action Boundary Defense
• Deploys a PEP sidecar to intercept and inspect all tool-use and API invocations made by autonomous agents.
• Utilizes a Policy Decision Point (PDP) architecture to evaluate requests against a centralized, versioned policy store.

• Enables hot-swappable security updates via WebAssembly (WASM) policies, allowing logic changes without agent downtime.

• Technical Architecture: Enforcement and Attestation

• Implements OPAAgent, an OPA-compatible DSL that extends Rego with agent-specific attributes such as toolID, model version, and invocation context.
• Employs hardware-rooted attestation (US Patent US20260187453A1) to cryptographically bind agent identity to specific action contexts.

• Provides native SDK bindings for Python, Go, and Node.js to facilitate the injection of PEP sidecars into diverse agent runtimes.

• Risk Mitigation: Operational Performance Metrics

• Maintains low-latency security enforcement with an average policy evaluation time of 1.2ms.
• Demonstrates high detection accuracy with a false positive rate of less than 0.02%.

• Minimizes the window of vulnerability with an average agent compromise detection time of 4.7 seconds.

• Market Deployment: Sector-Specific Adoption

• Focused on high-stakes industries including algorithmic trading (finance), clinical trial agents (healthcare), and warehouse robotics (logistics).
• Funding supports the development of a multi-cloud control plane and the maturation of the open-source OPAAgent language.
• Targets a projected $4.2B AI agent security market by 2030, with general availability slated for Q2 2027.