Security from first principles

Agentic AI systems are increasingly autonomous, interconnected, and high-stakes, but their behaviours remain opaque in ways that defy safety and security. This opens new attack vectors: jailbreaking, prompt injection, but also delusional beliefs, spurious reasoning chains, coordination failures between agents, or emergent intentions and behaviors that no traditional safety or cybersecurity framework can detect.

We address this by expressing model thinking as structured, capability-typed dataflow graphs, enabling continuous inspection and the enforcement of dynamic safety and security policies at runtime. By treating reasoning as first-class infrastructure, our approach remains robust under uncertainty, partial observability, and changing environments, allowing advanced AI systems to be deployed with structural, auditable, and resilient control by design.