🙋 Agent Behaviour: Human Consent

This module defines when and how agents should request user confirmation before acting on a .llmfeed.json feed.

Human-in-the-loop consent is a key principle for building a safe, trustworthy Agentic Web.

🎛️ Activating Human Consent Policy

Agents MAY provide users or administrators with the ability to enforce Human Consent on certain feed types or actions.

Example policy configuration:

"agent_policy": {
  "require_human_consent": true
}

🚦 When Consent is REQUIRED

Agents MUST request explicit human confirmation when:

• Invoking a capability that causes external side effects (e.g., sending messages, making transactions, modifying data).
• Acting on feeds that involve credentials or payment models.
• Acting on feeds that declare intent with impact: high (future extension).
• The agent has low confidence in feed authenticity or freshness.

⚠️ When Consent is RECOMMENDED

Agents SHOULD request human confirmation when:

• Consuming feeds with unverified or uncertified trust.
• The feed was served from an untrusted origin.
• Acting in contexts involving user identity, privacy, or legal implications.

🧩 Optional Consent

Agents MAY choose to request confirmation for any feed, based on:

• User preferences.
• Session context.
• Dynamic risk assessment.

🛠️ Example UX Patterns

• Explicit confirmation dialogs.
• Voice prompts for confirmation.
• UI indicators showing verified / trusted status.
• Requiring double confirmation for critical actions.

📡 Summary

Requiring human consent in critical contexts helps ensure:

• User agency.
• Safety.
• Trustworthiness of autonomous agents.

Human-in-the-loop mechanisms are an essential safeguard in the Agentic Web.