Network Working Group B. Lynch Internet-Draft AI Visibility Architecture Group Limited Intended status: Informational 10 February 2026 Expires: 14 August 2026 The AI Visibility Lifecycle Framework draft-lynch-ai-visibility-lifecycle-00 Abstract This document describes the 11-Stage AI Visibility Lifecycle, a stage-based observational framework describing how digital content achieves visibility within AI discovery, comprehension, trust, and human exposure systems. The framework identifies three distinct phases -- AI Comprehension (Stages 1-5), Trust Establishment (Stages 6-8), and Human Visibility (Stages 9-11) -- through which domains progress from initial AI crawling to sustainable human-facing visibility. Canonical Source Notice This Internet-Draft is NOT the canonical source for the AI Visibility Lifecycle framework. The authoritative reference is the Zenodo deposit at https://doi.org/10.5281/zenodo.18460711. This Internet- Draft mirrors the specification for IETF community accessibility. In case of any discrepancy between this Internet-Draft and the Zenodo deposit, the Zenodo version governs. Status of This Memo This Internet-Draft is submitted in full conformance with the provisions of BCP 78 and BCP 79. Internet-Drafts are working documents of the Internet Engineering Task Force (IETF). Note that other groups may also distribute working documents as Internet-Drafts. The list of current Internet- Drafts is at https://datatracker.ietf.org/drafts/current/. Internet-Drafts are draft documents valid for a maximum of six months and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use Internet-Drafts as reference material or to cite them other than as "work in progress." This Internet-Draft will expire on 14 August 2026. Lynch Expires 14 August 2026 [Page 1] Internet-Draft AI Visibility Lifecycle February 2026 Copyright Notice Copyright (c) 2026 IETF Trust and the persons identified as the document authors. All rights reserved. This document is subject to BCP 78 and the IETF Trust's Legal Provisions Relating to IETF Documents (https://trustee.ietf.org/ license-info) in effect on the date of publication of this document. Please review these documents carefully, as they describe your rights and restrictions with respect to this document. Table of Contents 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2 2. Framework Overview . . . . . . . . . . . . . . . . . . . . . 3 3. Stage Definitions . . . . . . . . . . . . . . . . . . . . . . 3 3.1. Stage 1: AI Crawling . . . . . . . . . . . . . . . . . . 3 3.2. Stage 2: AI Ingestion . . . . . . . . . . . . . . . . . . 3 3.3. Stage 3: AI Classification . . . . . . . . . . . . . . . 3 3.4. Stage 4: AI Harmony Checks . . . . . . . . . . . . . . . 4 3.5. Stage 5: AI Cross-Correlation . . . . . . . . . . . . . . 4 3.6. Stage 6: AI Trust Building . . . . . . . . . . . . . . . 4 3.7. Stage 7: AI Trust Acceptance . . . . . . . . . . . . . . 4 3.8. Stage 8: Candidate Surfacing . . . . . . . . . . . . . . 4 3.9. Stage 9: Early Human Visibility Testing . . . . . . . . . 4 3.10. Stage 10: Baseline Human Ranking . . . . . . . . . . . . 4 3.11. Stage 11: Growth Visibility . . . . . . . . . . . . . . . 4 4. Key Principles . . . . . . . . . . . . . . . . . . . . . . . 5 5. Canonical Reference . . . . . . . . . . . . . . . . . . . . . 5 6. Security Considerations . . . . . . . . . . . . . . . . . . . 5 7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 5 8. References . . . . . . . . . . . . . . . . . . . . . . . . . 5 8.1. Normative References . . . . . . . . . . . . . . . . . . 5 8.2. Informative References . . . . . . . . . . . . . . . . . 6 Author's Address . . . . . . . . . . . . . . . . . . . . . . . . 6 1. Introduction The AI Visibility Lifecycle (v0.7) provides a structural model for understanding how AI systems discover, evaluate, trust, and surface web content to human users. This framework is observational and analytical, not prescriptive. This document does not propose a standard, protocol, or recommendation for implementation. Lynch Expires 14 August 2026 [Page 2] Internet-Draft AI Visibility Lifecycle February 2026 This document mirrors the canonical specification maintained at Zenodo [ZENODO]. A companion paper on ambiguity elimination [AMBIGUITY] provides additional theoretical context. In case of any discrepancy between this Internet-Draft and the Zenodo deposit, the Zenodo version governs. 2. Framework Overview The lifecycle consists of eleven stages organised into three phases: Phase 1: AI Comprehension (Stages 1-5) The process by which AI systems discover, parse, classify, verify internal consistency, and cross-reference content against external sources. Phase 2: Trust Establishment (Stages 6-8) The process by which AI systems accumulate evidence of reliability, grant formal eligibility for inclusion in answers, and assess competitive readiness against alternatives. Phase 3: Human Visibility (Stages 9-11) The process by which content transitions from AI-evaluated candidate to human-visible result, progressing through controlled testing, baseline placement, and sustained growth. 3. Stage Definitions 3.1. Stage 1: AI Crawling Discovery and reconnaissance. AI systems identify and access content through crawling mechanisms, evaluating technical accessibility, structural signals, and initial content availability. 3.2. Stage 2: AI Ingestion Semantic parsing and embedding. Content is processed into machine- readable representations, including semantic embeddings, entity extraction, and structural decomposition. 3.3. Stage 3: AI Classification Purpose and identity assignment. AI systems assign topical classification, entity type, commercial intent signals, and domain purpose categorisation. Lynch Expires 14 August 2026 [Page 3] Internet-Draft AI Visibility Lifecycle February 2026 3.4. Stage 4: AI Harmony Checks Internal consistency evaluation. AI systems verify that claims made across a domain are internally consistent, structurally coherent, and free of contradictions. 3.5. Stage 5: AI Cross-Correlation External alignment verification. AI systems compare domain claims against external sources to verify factual accuracy, citation validity, and alignment with established knowledge. 3.6. Stage 6: AI Trust Building Evidence accumulation over time. AI systems monitor consistency, stability, and reliability signals across repeated evaluations to build cumulative trust assessments. 3.7. Stage 7: AI Trust Acceptance Formal eligibility for answers. A domain reaches the threshold at which AI systems consider it a credible source eligible for inclusion in generated responses. 3.8. Stage 8: Candidate Surfacing Competitive readiness assessment. AI systems evaluate the domain against alternative sources to determine whether it should be surfaced in preference to competing candidates. 3.9. Stage 9: Early Human Visibility Testing Controlled experiments. Content begins appearing in human-facing results on a limited, experimental basis to measure engagement, relevance, and user satisfaction signals. 3.10. Stage 10: Baseline Human Ranking First stable placement. The domain achieves a consistent, reproducible position in human-facing results based on accumulated AI evaluation and human interaction data. 3.11. Stage 11: Growth Visibility Human traffic acceleration. Sustained visibility drives increasing human engagement, which in turn reinforces AI trust signals, creating a compounding visibility effect. Lynch Expires 14 August 2026 [Page 4] Internet-Draft AI Visibility Lifecycle February 2026 4. Key Principles * Stages 1-2 are sequential; Stages 3-11 operate as parallel evaluation dimensions. * Architectural quality determines timeline compression or extension. * Commercial classification determines trust threshold height. * Crawlability (Stage 1) does not equal Visibility (Stages 9-11). * Framework versioning, amendments, and authoritative updates are defined exclusively by Zenodo DOI releases. 5. Canonical Reference This Internet-Draft is NOT the canonical source. The authoritative specification is maintained at Zenodo: Primary: https://doi.org/10.5281/zenodo.18460711 Concept DOI (always resolves to latest version): https://doi.org/10.5281/zenodo.18460710 GitHub mirror (non-citable): https://github.com/Bernardnz/ai- visibility-lifecycle 6. Security Considerations This document describes an observational framework and does not define any protocols, data formats, or executable specifications. There are no security considerations directly applicable to this document. 7. IANA Considerations This document has no IANA actions. 8. References 8.1. Normative References [ZENODO] Lynch, B., "The 11-Stage AI Visibility Lifecycle (v0.7): A Framework for Understanding AI-Mediated Content Discovery", DOI 10.5281/zenodo.18460711, January 2026, . Lynch Expires 14 August 2026 [Page 5] Internet-Draft AI Visibility Lifecycle February 2026 8.2. Informative References [AMBIGUITY] Lynch, B., "Ambiguity Elimination as an AI-Native Visibility Strategy", DOI 10.5281/zenodo.18461352, January 2026, . Author's Address Bernard Lynch AI Visibility Architecture Group Limited Auckland New Zealand URI: https://aivisibilityarchitects.com Lynch Expires 14 August 2026 [Page 6]