AI agents operate at machine speed. Your governance can too.
AI agents take actions — write code, send emails, execute API calls — faster than any human can review. Traditional governance fails at this speed. Compiled intercepts every action in the hot path, scores it against your policy library, and returns a verdict in under 100ms.
Press ⌘ Enter to compile
Works with the agents you're already running
Three threat classes. One scanner. Same sub-100ms path.
An agent that should read your CRM should never email customer lists to external addresses. Compile the policy once — enforce it across every message the agent sends.
Detect when adversarial content in retrieved data is attempting to hijack your agent's behavior. AUC 0.983 on held-out prompt injection corpus — trained on synthetic data alone.
Agents approved for customer service shouldn't touch financial records. Enforce the boundary mathematically, not with prompts that can be argued around.
Log-based governance reviews what your agents already did. Compiled scores every action before it executes — and returns a pass/flag verdict in under 100ms.
No LLM in the scoring path. The scanner is a matrix multiply against your antibody library. The intelligence was compiled in — it doesn't run at decision time.
Straight answers.
Under 100ms at p99, versus 300ms–2s for LLM-based alternatives. For most agent workflows, this is within measurement noise. The scanner is a local matrix multiply — no LLM inference, no API round-trip.
Yes. The scanner is a REST endpoint you deploy in your tenant. Any agent that can make an HTTP call can use it — OpenAI Assistants, proprietary enterprise frameworks, custom orchestration layers.
Start with plain English — 'don't let agents exfiltrate data.' The Compiler turns it into a working detector in 2.1 seconds. You iterate on the natural language, not on model architecture or feature engineering.
From plain-English policy to running detector in 2.1 seconds. No model training expertise required.
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