The trust layer for agentic systems.

Strahl tracks the origin of every piece of data as it flows through an agent, distinguishing your system context from user input, and user input from external sources like emails, tickets, or third-party APIs. In siloed environments, this means you always know which information silo a value came from before it reaches a tool call. Agents can handle data from multiple tenants or security domains without leaking across boundaries.

Every other AI security product gives you a probability. Strahl gives you a theorem. Our noninterference property is stated and machine-checked in Lean 4, the same proof assistant used to verify production compilers and the foundations of mathematics. If the proof compiles, the guarantee holds. There is no version of this that is wrong.

AI firewalls are themselves LLMs, they can be bypassed by the same prompt injection they are designed to stop. Strahl's enforcement runs outside the model: a formal policy engine that has no language model surface. There is no prompt that defeats it.

Strahl wraps your existing agent as a transparent gateway, one import, one config file, and enforcement is live. Your agent calls tools exactly as before; Strahl intercepts, checks, and either passes the call through or blocks it with a proof, invisibly to your application logic.

Every enforcement decision produces a machine-verifiable proof artifact. The architecture is designed from the ground up to support audit workflows: structured logs, cryptographic certificates, and clear data provenance.