Encrypted context, memory, and vector DB for every model and agent
Knowledge bases, vector stores, conversation history, agent memory. Every piece of context an AI reaches for is plaintext the moment it's loaded.
For agent builders
Ship agents enterprises can actually deploy.
Every enterprise buyer asks the same questions: who is this agent, what is it allowed to do, what happened when it did something wrong. Building that platform layer eats a year of roadmap.
AgentIQ and Mirror Gateway as your identity, policy, and trace primitives. Skip the platform, ship the agent.
Use cases for this audience
5 scenarios
Stop the wrong agent action at runtime, not in a dashboard
Agents act with standing access. The most you get back today is a chart explaining what already broke.
Govern what your agents reach for
MCP servers, plugins, and SaaS tools quietly extend agent blast radius. Almost nobody has a policy at that boundary.
One endpoint for every model, with policy at the door
Each team hits its own provider its own way. Keys sprayed across services, prompts egressing in the clear, no single place that can say what was asked or whether it was allowed.
Explain in plain English what an agent did and why
When an agent does something it shouldn't, your IR team is left guessing across logs that were never written for them.
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See encrypted AI security in action.
FHE-native inference. Runtime agent guardrails. Continuous red teaming. One platform. Book a working session with the team.