The problem
AI agents don't assist anymore. They transact, approve, and spend autonomously, at machine speed, with no human watching.

A vulnerability that steals $1,000 from a human-speed system steals $10 million from a machine-speed one. If your agent runs 1,000 actions per second and your team needs 60 seconds to investigate an alert, that's 60,000 actions before anyone understands what's happening.

LLM-based safety judges don't fix this. They're built from the same models they're meant to check. If an attacker can trick the agent, they can trick the judge.

Current guardrails aren't enough

Today's AI security relies on guardrails, monitoring, and policy enforcement. All three were designed for systems where a human is watching. Agentic commerce has no one watching. LLM-based safety judges inherit the same vulnerabilities as the AI they're checking. If an attacker can trick the agent, they can trick the guardrail too. And monitoring is reactive. By the time a dashboard flags unusual activity, thousands more compromised transactions have already gone through.

Why math, not monitoring

Visa doesn't trust that a card is legitimate, it cryptographically verifies it. Banks don't hope wire amounts weren't tampered with, they prove it mathematically. AI commerce needs the same standard. Cryptography turns trust problems into math problems. Math doesn't care about prompt injection, social engineering, or novel attacks. The proof is valid or it isn't.

No confidence scores. No "88% blocked." From watching to knowing.

Built for Production

• Jolt-Atlas. Production-ready zero-knowledge machine learning framework that enables fast, practical AI verification. Check it out here.
• Runs everywhere. Browsers, IoT, constrained environments — wherever your agents run.
• Developer-first. Train in PyTorch or TensorFlow, export to ONNX, generate proofs. No circuit expertise required.

Use cases

• Guardrails. Policies you write in English, enforced with formal verification.
• Verifiable inference. Prove a specific model ran on specific inputs with specific outputs — table stakes for regulated industries.
• Multi-agent workflows. Every handoff between agents carries a cryptographic receipt that can be verified in under a second; enabling agentic speed.
• Vericoding. Output formally verified code as agent actions.
• Privacy-preserving compliance. Prove policy adherence without revealing your models or your data.