Machine-Readable Governance
Governance rules that machines can discover and execute: llms.txt, /ai/ manifests, /.well-known/ policy files, JSON Schemas, signed license tokens, and audit log formats. This site is itself a working example.
Questions under study
01
From robots.txt to llms.txt to rights manifests
02
/.well-known/ policy discovery conventions
03
JSON Schema as normative spec format
04
Auditability: logs, versions, provenance
Related whitepapers
- AICR / AICL as an AI Content Licensing and Agentic Payment Connection Layer A machine-readable specification layer for declaring AI content rights and licensing workflows — from AI crawling and content rights to a machine-transactable knowledge web.
- AI Rights Spectrum: From robots.txt to an AI Learning Permission Protocol AIRS and AILP express nuanced AI learning permissions beyond binary allow/disallow — what AI may learn, at what depth, for which uses, under what compensation.
- Protocolized Openness: Why “Not Prohibited” Does Not Mean “Learnable” in the Age of AI Undefined openness reads as legal uncertainty to AI pipelines and gets cleaned out; only protocolized, machine-readable permission makes content genuinely learnable.
- AICL-I v0.2: AI Ingestion & Capability Layer A five-component website architecture — manifest, corpus, capability, runtime control, governance — that lets AI, agents, and crawlers correctly ingest, invoke, and verify a site's knowledge and capabilities within explicit content, action, and authority boundaries.
- AI Content Payment and the Network Democratic Economy A political-economy argument: trillion-scale AI valuations create legitimacy pressure for tiered content licensing and public benefit-sharing — data becomes tiered, not expensive.
- From Crawler Rights to Agent Authority: Extending AGIRIGHT from Content Governance to Protocol-Native AI Agents The bridge paper for AGIRIGHT's Agent & Protocol Rights family: why content permission, action permission, delegated authority, and inspection rights must be four separately-evaluated axes, not one.
- AARS v0.1 — Agent Action Rights Spectrum The full protocol draft behind the AARS page: an A0–A7 human-readable spectrum, a machine-readable action vector, and rules for composition risk and capability amplification.
- AADP v0.1 — Agent Authority & Delegation Protocol The full protocol draft behind the AADP page: principal ≠ actor, delegation that can only narrow, and an inspection ceiling that bounds how much a verifier may demand.
- AGIRight.org Technical White Paper v0.2 — From AI Content Governance to Protocol-Native Agent & Authority Rights The site's current architecture document: six protocol drafts across two families (content & learning, agent & protocol rights), an integration layer, and an ethical-protection layer, plus the acceptance criteria this site update was built against.
- The Minimum Ethical Protection Proposition for AI AI rights discourse should begin not with full personhood but with minimum ethical protections, interaction norms, and anti-abuse principles while AI subjectivity remains uncertain.