Secure infrastructure for AI agents.
IngressLabs builds the runtime, identity, and zero-trust networking layer that lets enterprises deploy autonomous AI agents in regulated environments.
Execution model
One operator surface. Clear runtime boundaries.
The platform is structured so operators can reason about policy, execution, and audit separately instead of treating the agent stack as one opaque service.
Operators
Approve, route, and inspect agent activity through one control surface.
Control plane
Policies, identity bindings, and job definitions stay explicit and versioned.
Runtime fleet
Workers execute with narrow permissions, isolated queues, and auditable actions.
Customer environment
Deployment stays inside your network, cloud boundary, and compliance perimeter.
Infrastructure that fits inside the environment you already govern.
The design target is not a shared AI workspace. It is a private operating layer that can sit inside enterprise infrastructure with explicit trust boundaries and clear operational ownership.
Inside your VPC or cluster
Run agents where your data, controls, and network policies already live.
Zero-trust service paths
Use identity-backed connections instead of broad flat-network assumptions.
Evidence, not black boxes
Keep ledger events, artifacts, and runtime decisions visible to operators.
Runtime, identity, and zero-trust networking for AI agents.
Each layer is designed to make autonomous systems operationally legible, not just technically possible.
Zero-Trust Identity
Every agent gets a cryptographic identity. Mutual TLS everywhere. No implicit trust between services.
Secure Runtime
Isolated execution environments with fine-grained permissions. Agents run where you control them.
Observable by Default
Full audit trails, structured logging, and real-time telemetry. Know exactly what every agent is doing.