US AI Laws & Executive Order 14110: How to Build Compliant AI Agents in 2026

• Transparency & Disclosure. AI-generated content must be identifiable. Organizations must disclose when AI is making consequential decisions
• Red-Teaming & Testing. Developers of "dual-use foundation models" must share safety test results with the government
• Accountability Mechanisms. Federal agencies must establish processes for tracking and auditing AI system behavior
• Data Privacy. Strengthened guidance on AI systems processing personal data, especially biometric and health data

• Identity — Every agent must be traceable to its deploying organization
• Audit trails — A tamper-proof record of what each agent did, when, and why
• Human oversight — The ability to intervene, suspend, or revoke agents when they behave unexpectedly
• Risk scoring — A quantitative assessment of agent reliability that's publicly verifiable

• Algorithmic impact assessments — Evaluate AI systems for bias and discriminatory outcomes before deployment
• Disclosure obligations — Notify consumers when AI is being used to make consequential decisions
• Documentation — Maintain records of how AI systems were developed, tested, and deployed
• Risk management — Implement ongoing monitoring and governance frameworks
• Opt-out rights — Provide mechanisms for consumers to request human review of AI decisions

• Notify candidates that AI is analyzing their video interviews
• Obtain consent before using AI evaluation
• Explain how the AI works in "plain language"
• Destroy video recordings within 30 days of a candidate's request

• AI transparency requirements in government procurement
• Mandatory disclosure of AI use in consumer-facing decisions
• Audit trail requirements for AI systems used by state agencies

• SB 1047 (AI Safety) — Requires safety assessments for large AI models
• AB 2013 (Training Data Transparency) — Requires disclosure of training data sources
• SB 942 (AI Watermarking) — Mandates detection mechanisms for AI-generated content
• AB 2885 (AI in Government) — Establishes AI governance frameworks for state agencies

import createVorim from "@vorim/sdk";

const vorim = createVorim({ apiKey: "agid_sk_live_..." });

const { agent, private_key } = await vorim.register({
  name: "loan-assessment-agent",
  capabilities: ["evaluate_applications", "check_credit"],
  scopes: ["agent:read", "agent:execute"],
});
// Agent now has a cryptographic identity, trust score, and permission scopes

const check = await vorim.check(agent.agent_id, "agent:execute");
if (!check.allowed) {
  console.log("Action blocked:", check.reason);
  // Log denial for audit trail — this is what regulators look for
}

await vorim.emit({
  agent_id: agent.agent_id,
  event_type: "tool_call",
  action: "evaluate_loan_application",
  resource: "application_12345",
  result: "success",
  latency_ms: 230,
  metadata: { model: "gpt-4o", decision: "approved" },
});

• Colorado: $20,000 per violation, no aggregate cap
• Illinois: $1,000 per day of non-compliance for video interview violations
• FTC enforcement: The FTC has already taken action against companies for deceptive AI practices, with settlements exceeding $5 million
• Private litigation: Class-action lawsuits targeting AI bias and discrimination are increasing rapidly
• Reputational damage: High-profile AI failures (hallucinations, biased decisions, data leaks) destroy customer trust

• Federal AI legislation is expected by late 2026 or early 2027, building on EO 14110
• More states are legislating — at least 15 states have active AI bills as of March 2026
• Sector-specific rules from the SEC, FDA, CFPB, and HHS are being drafted
• International harmonisation between US and EU frameworks will increase compliance complexity for global deployments