Control, not just visibility

VisIQ lives in the agent's decision path and controls what it can see and do. Same value in, a different value out, decided by who is asking, for what, right now.

The decision runs in-process against a locally cached rule bundle, so it costs a fraction of a millisecond per call rather than a network round trip. The only decision that waits on the network is the one that should: a human approval.

Author a rule once against one canonical event schema and it governs every framework we harness, from the day you connect. There is no per-agent wiring and no rule to port when a team picks a different SDK.

The schema is what makes that possible. A tool that both reads and writes is one event carrying both operations, evaluated once against every rule that applies to either, rather than two half-descriptions of the same call.

Permit, deny, or escalate: each verdict is sealed into a cryptographically signed, Merkle-chained, timestamp-anchored record. Nothing can be edited or backdated undetectably.

The records are mapped to 11 compliance frameworks and exportable the day you turn it on, which is the difference between having evidence and having logs you would then have to argue from.

A faithful clone runs in a sandbox where we red-team it, with prompt injection, social engineering and tool abuse, to see exactly how it behaves under attack before a real adversary finds out.

The point is the specificity. A generic model benchmark tells you about a model; attacking a copy of your agent, with your tools and your prompts wired up, tells you about the thing you are actually about to run.

When your agent installs a new skill, we set it off in the sandbox and watch. Does it reach for canaries, exfiltrate, escalate? Malicious skills are caught before they ever run for real.

This is the supply chain problem the agent era inherited from package managers, arriving with a new twist: a skill does not have to contain obvious malware to be dangerous, it only has to do something the author did not disclose.

Pattern matching before the action and AI analysis after it catch injection as it happens. Because we already sit inline, the poisoned instruction is stopped before the agent can act on it.

Detection alone would not be enough here. Knowing a message was an injection attempt is only useful if there is still time to refuse it, and that is a property of where the check sits, not of how good it is.

Real-time, pre-action protection against extraction and override, so the instructions that define your agent cannot be leaked to a curious user or quietly rewritten by an attacker.

A system prompt is the closest thing an agent has to a policy of its own. Anything that can read it can plan around it, and anything that can change it can retire every other control you put in place.