Agent runtimes lack a typed, protocol-enforced separation between executable instructions and untrusted data inputs, making every agent integration vulnerable to prompt injection attacks. The same class of exploit (poisoned external content hijacking agent actions) is being independently discovered across Claude, Gemini, and Copilot deployments. Current mitigations—system prompts, fine-tuning, content filtering—are application-layer patches on an architectural gap, analogous to sanitizing SQL strings instead of using parameterized queries.

Agent runtimes conflate instructions and untrusted data in the same token stream, making every tool-using agent structurally vulnerable to prompt injection — no amount of application-layer patching fixes an architectural gap.

Platform engineers at companies deploying tool-using AI agents in production (e.g., customer support, code generation, data pipelines) who are blocked from shipping agentic features by security review.

Enterprises are actively pausing agent deployments over injection risk; security teams demand a principled mitigation before approving production access to tools like email, databases, or payment systems — this is the literal gate-blocker to enterprise agentic adoption.

Ship an open-source proxy/SDK that sits between the agent runtime and LLM API, enforcing a typed message schema where data payloads are tagged, sandboxed, and rendered as structured content blocks that models are trained to treat as non-executable — MVP targets OpenAI and Anthropic APIs with a drop-in Python/TS middleware.

Subset of the $15B+ API security market; every company calling an LLM API with external data (millions of deployments) is a potential user, analogous to how every SQL-using app needed parameterized queries.