# Web agent-ready — what changes when part of the audience isn't human
Public Dossier DOS-WA-1 · Overview
Schema: 1.0.0
Dossier ID: DOS-WA-1
Slug: web-agent-ready-discover-cite-execute
Public version: 1.0.0
Locale: en
Intent: explain
Audience: Website managers and owners, builders, technical editors, and operators.
Last verified: 2026-08-07
Canonical: https://blog.nilu.pro/en/blog/web-agent-ready-discover-cite-execute/dossier/
Article summary: https://blog.nilu.pro/en/blog/web-agent-ready-discover-cite-execute/
Markdown: https://blog.nilu.pro/en/blog/web-agent-ready-discover-cite-execute/dossier.md
JSON: https://blog.nilu.pro/en/blog/web-agent-ready-discover-cite-execute/dossier.json
## Executive summary
- Part of a website's traffic and audience is non-human — crawlers, bots and, still experimentally, agents acting on behalf of a user. Vendor telemetry (Imperva) is neither a census of the web nor evidence of demand for AI agents.
- Three distinct jobs — discover, cite and execute — organize priority and budget better than treating everything as 'SEO with more schema'. For Google, optimizing for AI capabilities in Search is still SEO: no special markup or requirement for llms.txt.
- Citation is not a visit: click-through rates fall when an AI summary appears (Pew), and publishers report pressure on referrals and zero-click search (Digiday). Agent execution (WebMCP) is an origin trial and community draft — monitor it, but do not commit budget.
## Scope
Three-layer operational map — discover, cite and execute; what changes for those who operate a website when bots and agents are part of the audience; decision priority stack.
**Thesis:** Three layers — discover, cite and execute — better describe 2026 than reducing everything to 'SEO with more schema'. It is an operating model tested against available evidence, not a closed scientific thesis.
**Out of scope**
- A detailed comparison of formats, Markdown negotiation, and HTML chrome remains in the specialized Representations Dossier.
- A more detailed discussion of SEO vs GEO, CTR, and zero-click remains in the specialized SEO vs GEO Dossier; execution via WebMCP remains scoped, not implemented.
## Specialized Dossiers
- DOS-WA-2 — [Web agent-ready — machine representations: HTML, Markdown and chrome](https://blog.nilu.pro/pt-br/blog/web-agent-ready-representacoes/)
- DOS-WA-3 — [Web agent-ready — discovery vs citation: SEO, GEO and evidence](https://blog.nilu.pro/pt-br/blog/web-agent-ready-seo-vs-geo/)
## System map
```text
website audience (people + crawlers + bots +, experimentally, agents)
↓
Discover — crawling, indexing, snippet eligibility / AI features (same basics as SEO)
↓
Cite — retrieval and synthesis with attribution in a generative response (not a ranking position)
↓
Execute — agent calls actions on website via WebMCP (origin trial / community draft)
↓
cross-cutting layer: representation (semantic HTML/a11y, negotiated Markdown) supports Discover and Cite
↓
priority stack: SEO foundation → clean representations → crawl policy → actual KPIs → execution on watch
```
## DATA — evidence and measurements
### WA-OV-D01 — Google: Optimizing for AI in Search is still SEO
The Google Search Central guide to generative AI capabilities states that optimizing for these capabilities is optimizing for Search, under the same core quality and ranking systems; it identifies reliance on llms.txt, special AI markup, splitting content into chunks, or rewriting text solely for agents as misconceptions; structured data is not required for generative AI capabilities.
- **Types:** fact
- **Status:** verified
- **Confidence:** high
- **Last reviewed:** 2026-08-07
- **References:** [src-wa-google-ai-optimization-guide](#src-wa-google-ai-optimization-guide)
### WA-OV-D02 — Eligibility for AI features requires no extra technical requirement
Google Search Central's 'AI features and your website' documentation states that there is no additional technical requirement beyond being indexed and eligible for snippet, and recommends keeping important content in textual form.
- **Types:** fact
- **Status:** verified
- **Confidence:** high
- **Last reviewed:** 2026-08-07
- **References:** [src-wa-google-ai-features-website](#src-wa-google-ai-features-website)
### WA-OV-D03 — People-first and usual technical requirements extend to AI Overviews
Search Central Blog's 'Succeeding in AI search' article reiterates that people-first advice, page experience and Search standard technical requirements (crawling, indexing, snippet eligibility) extend to AI Overviews and AI Mode; Structured data is only useful when it matches visible content.
- **Types:** fact
- **Status:** verified
- **Confidence:** high
- **Last reviewed:** 2026-08-07
- **References:** [src-wa-google-succeeding-ai-search](#src-wa-google-succeeding-ai-search)
### WA-OV-D04 — Chrome describes two stages of the 'agentic web'
The Chrome for Developers blog 'A developer toolkit to make your website agent-ready' separates two stages: agents that search (searching, where the fundamentals of SEO continue to apply) and agents that use the website directly (using, focusing on accessibility tree, layout stability and WebMCP); the Lighthouse Agentic Browsing score is treated as informational while patterns still emerge.
- **Types:** fact
- **Status:** verified
- **Confidence:** high
- **Last reviewed:** 2026-08-07
- **References:** [src-wa-chrome-agent-ready-toolkit](#src-wa-chrome-agent-ready-toolkit)
**Limitations**
- Browser implementer language, not Search standard nor scientific proof of the layering model.
### WA-OV-D05 — W3C TAG discusses distinguishing humans, bots and agents in Web architecture
The W3C Technical Architecture Group recorded, at the 2026 March meeting in London, a discussion about how Web architecture should distinguish humans, bots and agents acting on behalf of users — an ongoing debate, with no final Finding published in the post.
- **Types:** fact
- **Status:** verified
- **Confidence:** high
- **Last reviewed:** 2026-08-07
- **References:** [src-wa-w3c-tag-london-2026](#src-wa-w3c-tag-london-2026)
**Limitations**
- Ongoing institutional discussion; it is not a closed conclusion of W3C.
### WA-OV-D06 — Imperva records higher-than-human automated traffic, with caveats
Imperva's Bad Bot 2025 report, based on the vendor's own network at 2024, recorded automated traffic (51%) exceeding human traffic (49%), with 37% classified as a malicious bot and 14% as a 'good' bot.
- **Types:** estimate
- **Status:** contextual
- **Measurement basis:** Telemetry from Imperva's own customer network in 2024.
- **Period:** 2024
- **Confidence:** medium
- **Risk of misinterpretation:** Do not use as proof of demand for AI agents or to budget for 'agent presence'.
- **Last reviewed:** 2026-08-07
- **References:** [src-wa-imperva-bad-bot-2025](#src-wa-imperva-bad-bot-2025)
**Limitations**
- It is not an independent census of the web; it is a sample from a security vendor's network.
- 'Automated' is not synonymous with 'AI agent'; it does not distinguish agents acting on behalf of users from ordinary crawlers and scrapers.
### WA-OV-D07 — Content negotiation is standard HTTP mechanism
MDN describes content negotiation in HTTP as the mechanism for serving different representations of the same resource at the same URI: the client sends Accept headers and the server responds accordingly, flagging caches with Vary.
- **Types:** fact
- **Status:** verified
- **Confidence:** high
- **Last reviewed:** 2026-08-07
- **References:** [src-wa-mdn-content-negotiation](#src-wa-mdn-content-negotiation)
### WA-OV-D08 — Vercel and Fastly document serving Markdown to agents via negotiation
Vercel documents serving Markdown and HTML at the same URL via Accept: text/markdown (rewrites + route handler); Fastly describes serving HTML to browsers and Markdown to crawlers from AI or Accept requests: text/markdown, with Vary, estimating a typical article in Markdown with about 20–30% the size of the equivalent HTML.
- **Types:** fact, estimate
- **Status:** contextual
- **Measurement basis:** Implementation standard documentation (Vercel) and qualitative case estimation (Fastly).
- **Confidence:** medium-high
- **Last reviewed:** 2026-08-07
- **References:** [src-wa-vercel-docs-for-agents](#src-wa-vercel-docs-for-agents), [src-wa-fastly-markdown-agents](#src-wa-fastly-markdown-agents)
**Limitations**
- The Fastly size estimate is an illustrative case, not a web average.
### WA-OV-D09 — Adoption of negotiated Markdown remains rare
Checkly measured that only 3 of 7 tested coding agents sent Accept asking for text/markdown; a snapshot Radar cited on Cloudflare's Agent Readiness blog found Markdown negotiation (Accept: text/markdown) working on only 3,9% from a sample of high-traffic sites scanned.
- **Types:** estimate, comparison
- **Status:** contextual
- **Measurement basis:** Checkly own test with 7 agents; snapshot Radar about high traffic websites.
- **Confidence:** medium
- **Last reviewed:** 2026-08-07
- **References:** [src-wa-checkly-content-negotiation-2026](#src-wa-checkly-content-negotiation-2026), [src-wa-cloudflare-agent-readiness](#src-wa-cloudflare-agent-readiness)
**Limitations**
- Small and specific samples; they do not measure the entire web or all existing agents.
### WA-OV-D10 — 97% of published llms.txt files are not read
A study by Ahrefs, with a sample of around 137 thousand websites, found that 97% of the published llms.txt files did not receive any requests in the month analyzed.
- **Types:** estimate
- **Status:** contextual
- **Measurement basis:** Sample ~137.000 sites; requests measured in a month.
- **Period:** Mês analisado no estudo Ahrefs (2026).
- **Confidence:** medium
- **Last reviewed:** 2026-08-07
- **References:** [src-wa-ahrefs-llmstxt-study](#src-wa-ahrefs-llmstxt-study)
**Limitations**
- It does not measure the intent of every existing AI agent or future periods.
### WA-OV-D11 — Paper that defined GEO reports gain 'of up to 40%' in laboratory environment
The paper that defined the term GEO (Generative Engine Optimization), by Aggarwal et al., reported a relative gain in visibility 'up to 40%' in the authors' own experimental environment, with specific pipeline (Google's top-5 reprocessed by a GPT-3.5-style model) — not in a public ranking product.
- **Types:** estimate
- **Status:** contextual
- **Measurement basis:** Authors' experimental benchmark (GEO-bench), not measured in production product.
- **Confidence:** medium
- **Risk of misinterpretation:** Do not treat it as an ROI forecast or as a citation metric in production.
- **Last reviewed:** 2026-08-07
- **References:** [src-wa-arxiv-geo-2311-09735](#src-wa-arxiv-geo-2311-09735)
**Limitations**
- It is not a public ranking API or business return prediction.
### WA-OV-D12 — ChatGPT search retrieves and synthesizes with attribution, without public scoreboard
OpenAI describes ChatGPT search as a tool that searches the web and returns answers with links to sources, with a Sources sidebar; the documentation does not publish a public citation position scoreboard.
- **Types:** fact
- **Status:** verified
- **Confidence:** high
- **Last reviewed:** 2026-08-07
- **References:** [src-wa-openai-chatgpt-search](#src-wa-openai-chatgpt-search)
### WA-OV-D13 — CTR drops when an AI summary appears
Pew Research Center found, in a correlational analysis, organic link click rates of about 8% when an AI summary appeared in Google results, versus about 15% when it did not; only about 1% of users clicked on links within the summary itself.
- **Types:** estimate
- **Status:** contextual
- **Measurement basis:** Correlational/observational analysis of search behavior.
- **Confidence:** medium
- **Last reviewed:** 2026-08-07
- **References:** [src-wa-pew-ai-summary-ctr-2025](#src-wa-pew-ai-summary-ctr-2025)
**Limitations**
- Correlation; does not establish universal causality.
### WA-OV-D14 — OpenAI distinguishes training, search, and real-time-use bots
The crawlers documentation for OpenAI distinguishes GPTBot (training), OAI-SearchBot (indexing for search), and ChatGPT-User (real-time user search), with separate robots.txt controls for each role.
- **Types:** fact
- **Status:** verified
- **Confidence:** high
- **Last reviewed:** 2026-08-07
- **References:** [src-wa-openai-crawlers-docs](#src-wa-openai-crawlers-docs)
### WA-OV-D15 — AI crawlers dominate the AI bot traffic observed by an edge vendor
Fastly (Threat Insights research) stated that AI crawlers accounted for nearly 80% of the AI bot traffic observed in its period analysis.
- **Types:** estimate
- **Status:** contextual
- **Measurement basis:** Fastly edge network telemetry in specific analysis period.
- **Confidence:** medium
- **Last reviewed:** 2026-08-07
- **References:** [src-wa-fastly-ai-crawlers-press](#src-wa-fastly-ai-crawlers-press)
**Limitations**
- Network sample from a vendor, not a web census.
### WA-OV-D16 — WebMCP is origin trial in Chrome and Draft Community Group Report in W3C
The Chrome for Developers documentation describes WebMCP as proposed in origin trial from Chrome 149, with relevant limitations — requires an open browser context, does not work without a UI (headless), and discovering capabilities requires visiting the website directly; The W3C Web Machine Learning Community Group draft explicitly states that it is a Draft Community Group Report, not a W3C Standard nor part of the Standards Track.
- **Types:** fact
- **Status:** verified
- **Confidence:** high
- **Risk of misinterpretation:** Do not treat it as an 'agent-ready' requirement today nor invest your budget in it.
- **Last reviewed:** 2026-08-07
- **References:** [src-wa-chrome-webmcp-docs](#src-wa-chrome-webmcp-docs), [src-wa-w3c-webmcp-cg-draft](#src-wa-w3c-webmcp-cg-draft)
**Limitations**
- Experimental proposal; It is not a stable cross-browser standard.
### WA-OV-D17 — Publishers report pressure from referral and zero-click associated with AI
Reports from Digiday, citing data from Digital Content Next (DCN), reported approximately -10% year-over-year median referral traffic from Google between publishers (wide range from -1% to -25%) associated with AI Overviews; another report from Digiday, citing Similarweb, found zero-click in news searches rising from around 56% to 69%, with traffic from AI platforms growing without offsetting this loss.
- **Types:** estimate, comparison
- **Status:** contextual
- **Measurement basis:** Data from publishers cohorts reported by specialized press (DCN and Similarweb, via Digiday).
- **Confidence:** medium
- **Last reviewed:** 2026-08-07
- **References:** [src-wa-digiday-dcn-referral-drop](#src-wa-digiday-dcn-referral-drop), [src-wa-digiday-similarweb-zero-click](#src-wa-digiday-similarweb-zero-click)
**Limitations**
- Specialized press reporting on specific cohorts; does not establish universal causality nor generalize to every publisher.
### WA-OV-D18 — Citation recall in generative search engines is fragile
A 2023 study (Liu et al.) measured recall citation per sentence around 51,5% in the generative search engines evaluated — nearly half of the sentences generated without an adequate verifiable citation in the snapshot studied.
- **Types:** estimate
- **Status:** contextual
- **Measurement basis:** Snapshot evaluation of per-sentence citations in generative search engines (2023).
- **Confidence:** medium
- **Last reviewed:** 2026-08-07
- **References:** [src-wa-arxiv-verifiability-2304-09848](#src-wa-arxiv-verifiability-2304-09848)
**Limitations**
- Snapshot of a specific moment; evaluated mechanisms may have changed since then.
## INFO — derived syntheses
### WA-OV-I01 — Three layers organize jobs that 'SEO with schema' collapses together
Discover, cite and execute separate distinct jobs, evidence and KPIs that are hidden when everything is treated as 'SEO with more schema'; Chrome's stage language (searching/using) and W3C TAG's architecture discussion (humans/bots/agents) support the industry vocabulary but do not prove the model as science — it remains an editorial hypothesis tested against evidence.
- **Derived from:** WA-OV-D01, WA-OV-D02, WA-OV-D04, WA-OV-D05, WA-OV-D11, WA-OV-D12, WA-OV-D16
### WA-OV-I02 — Discover is eligibility under established fundamentals, not special markup
The Discover layer is covered by the same core quality systems and ranking as Search: snippet indexing and eligibility, with people-first content — no special AI markup, no requirement for llms.txt, and no requirement for perfect HTML.
- **Derived from:** WA-OV-D01, WA-OV-D02, WA-OV-D03
### WA-OV-I03 — Cite has a laboratory method and documented UX, not public scoreboard
The Cite layer combines a laboratory method (GEO) with a documented attribution UX (Sources), but there is no public citation position leaderboard; citation does not equal visit — click-through rates drop with AI summaries, publishers report pressure from referral/zero-click, and the quality of the citation itself is fragile.
- **Derived from:** WA-OV-D11, WA-OV-D12, WA-OV-D13, WA-OV-D17, WA-OV-D18
### WA-OV-I04 — Clean representation is cross-cutting, but adoption of negotiated Markdown is still rare
Serving a clean representation — content negotiation HTTP, HTML semantic, Markdown when the stack supports it — is useful for discovery and citing, but still the exception: few agents ask for Markdown today, and most high-traffic sites scanned don't respond to Accept: text/markdown.
- **Derived from:** WA-OV-D07, WA-OV-D08, WA-OV-D09
### WA-OV-I05 — Execute (WebMCP) remains experimental and outside critical roadmap
WebMCP is origin trial in Chrome and Draft Community Group Report in W3C, with relevant operational limitations; the Execute layer should be under observation, not guide budget today.
- **Derived from:** WA-OV-D16
### WA-OV-I06 — Decision Priority Stack
The evidence supports an order of priority: (1) foundation of SEO and content people-first; (2) clean representations; (3) policy of crawl separating training bots from search/citation bots; (4) Search Console real metrics, traffic and bot bytes, not vanity llms.txt; (5) execution by agents as observation, not a roadmap bet.
- **Derived from:** WA-OV-D01, WA-OV-D02, WA-OV-D03, WA-OV-D07, WA-OV-D10, WA-OV-D13, WA-OV-D14, WA-OV-D15, WA-OV-D16
### WA-OV-I07 — llms.txt is weak 'AI presence' KPI
Google does not require llms.txt for AI features in Search, and most published files are never read — publishing a llms.txt 'because everyone has it' is not a reliable indicator of priority or outcome.
- **Derived from:** WA-OV-D01, WA-OV-D10
### WA-OV-I08 — Automated traffic pressures crawl policy and cost, does not prove demand for execution
Telemetry from vendor on automated traffic (with caveats) and on AI crawlers' share in bot traffic drives crawl policy and bandwidth/origin cost sizing — not evidence that agents are ready to perform actions on the site nor does it justify prioritizing WebMCP.
- **Derived from:** WA-OV-D06, WA-OV-D15, WA-OV-D08, WA-OV-D09
## Uncertainties
### WA-OV-UNC-01 — Whether the three-tier model improves budget decisions
- **Status:** não mensurado publicamente
- **What we know:** The three-tier model organizes jobs and evidence, but there is no usage study or editorial testing that confirms whether it improves budget decisions in practice, or just organizes the narrative without changing results.
- **Editorial treatment:** Treat the model as a working hypothesis tested against available evidence, not as a closed or scientifically proven framework.
- **Confidence:** high na lacuna
- **Last reviewed:** 2026-08-07
### WA-OV-UNC-02 — Support Link Selection Criteria in AI Responses
- **Status:** não documentado publicamente
- **What we know:** Google documents eligibility (indexed + eligible for snippet) for the Discover tier, but it doesn't publish the list of exact criteria it uses to choose which links appear in support of generative AI features.
- **Editorial treatment:** Do not invent a list of citation factors; treat link selection as a documented gap.
- **Last reviewed:** 2026-08-07
### WA-OV-UNC-03 — When WebMCP is no longer experimental
- **Status:** sem previsão pública
- **What we know:** WebMCP is in origin trial in Chrome and as Draft Community Group Report in W3C; There is no public timeline for becoming Standards Track or stable cross-browser standard.
- **Editorial treatment:** Keep Execute as a layer under observation until the status changes are documented by the responsible bodies themselves.
- **Last reviewed:** 2026-08-07
### WA-OV-UNC-04 — Converting synthetic 'AI visibility' into real result
- **Status:** não mensurado publicamente
- **What we know:** There is no public study that converts synthetic 'AI viewability' metrics (such as scores from third-party tools) into actual impressions, clicks or revenue for publishers.
- **Editorial treatment:** Prefer first-hand metrics (Search Console, traffic, conversion) to 'citation score' dashboards without method transparency.
- **Last reviewed:** 2026-08-07
### WA-OV-UNC-05 — Actual composition of 'automated' traffic
- **Status:** não desagregado publicamente
- **What we know:** Telemetry such as Imperva does not separate, within the traffic classified as automated, which fraction is an agent acting on behalf of a user and which is a crawler or common scraper.
- **Editorial treatment:** Do not use automated traffic participation as proof of agent-readiness or to budget for 'agent presence'.
- **Last reviewed:** 2026-08-07
## Methodology
- **DATA:** DATA records verifiable claims with explicit source, measurement context, limitations, and confidence.
- **INFO:** INFO records synthesis or interpretation and always declares the IDs of the claims from which it derives.
- **Uncertainty:** Uncertainty registers a gap as an editorial result; it remains explicit and is not filled with a fragile estimate.
- **Update rule:** A revision can correct or update the projection only from the canonical Dossier with editorial authorization; Material changes increment the public version and are recorded in the corrections history.
## Version and corrections
- **Public version:** 1.0.0
- **Last verified:** 2026-08-07
- **Review after:** 2026-09-06
- No public corrections recorded.
## References
1. **src-wa-google-ai-optimization-guide.** [Google Search Central — Optimizing your website for generative AI features on Google Search](https://developers.google.com/search/docs/fundamentals/ai-optimization-guide). Google. Accessed 2026-08-07.
1. **src-wa-google-ai-features-website.** [Google Search Central — AI features and your website](https://developers.google.com/search/docs/appearance/ai-features). Google. Accessed 2026-08-07.
1. **src-wa-google-succeeding-ai-search.** [Google Search Central Blog — Succeeding in AI search](https://developers.google.com/search/blog/2025/05/succeeding-in-ai-search). Google. Accessed 2026-08-07.
1. **src-wa-chrome-agent-ready-toolkit.** [Chrome for Developers — A developer toolkit to make your website agent-ready](https://developer.chrome.com/blog/agent-ready-toolkit). Chrome for Developers. Accessed 2026-08-07.
1. **src-wa-w3c-tag-london-2026.** [W3C — The W3C TAG Meeting in London, March 2026](https://www.w3.org/blog/2026/the-w3c-tag-meeting-in-london-march-2026/). W3C. Accessed 2026-08-07.
1. **src-wa-imperva-bad-bot-2025.** [Imperva — 2025 Bad Bot Report](https://www.imperva.com/resources/reports/2025-Bad-Bot-Report.pdf). Imperva. Accessed 2026-08-07.
1. **src-wa-mdn-content-negotiation.** [MDN — Content negotiation](https://developer.mozilla.org/en-US/docs/Web/HTTP/Guides/Content_negotiation). MDN. Accessed 2026-08-07.
1. **src-wa-vercel-docs-for-agents.** [Vercel — How to serve documentation for agents](https://vercel.com/kb/guide/how-to-serve-documentation-for-agents). Vercel. Accessed 2026-08-07.
1. **src-wa-fastly-markdown-agents.** [Fastly — Give AI Agents the Markdown They Actually Want](https://www.fastly.com/blog/give-ai-agents-markdown-they-actually-want). Fastly. Accessed 2026-08-07.
1. **src-wa-checkly-content-negotiation-2026.** [Checkly — The Current State of Content Negotiation for AI Agents](https://www.checklyhq.com/blog/state-of-ai-agent-content-negotation/). Checkly. Accessed 2026-08-07.
1. **src-wa-cloudflare-agent-readiness.** [Cloudflare — Introducing the Agent Readiness score](https://blog.cloudflare.com/agent-readiness/). Cloudflare. Accessed 2026-08-07.
1. **src-wa-ahrefs-llmstxt-study.** [Ahrefs — We Analyzed 137K Sites: 97% of llms.txt Files Never Get Read](https://ahrefs.com/blog/llmstxt-study/). Ahrefs. Accessed 2026-08-07.
1. **src-wa-arxiv-geo-2311-09735.** [Aggarwal et al. — GEO: Generative Engine Optimization](https://arxiv.org/abs/2311.09735). arXiv. Accessed 2026-08-07.
1. **src-wa-openai-chatgpt-search.** [OpenAI — Introducing ChatGPT search](https://openai.com/index/introducing-chatgpt-search/). OpenAI. Accessed 2026-08-07.
1. **src-wa-pew-ai-summary-ctr-2025.** [Pew Research Center — Google users are less likely to click on links when an AI summary appears in the results](https://www.pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results/). Pew Research Center. Accessed 2026-08-07.
1. **src-wa-openai-crawlers-docs.** [OpenAI — Crawler / bot documentation](https://developers.openai.com/api/docs/bots). OpenAI. Accessed 2026-08-07.
1. **src-wa-fastly-ai-crawlers-press.** [Fastly — New Fastly threat research reveals AI crawlers make up almost 80% of AI bot traffic](https://www.fastly.com/press/press-releases/new-fastly-threat-research-reveals-ai-crawlers-make-up-almost-80-of-ai-bot). Fastly. Accessed 2026-08-07.
1. **src-wa-chrome-webmcp-docs.** [Chrome for Developers — WebMCP](https://developer.chrome.com/docs/ai/webmcp). Chrome for Developers. Accessed 2026-08-07.
1. **src-wa-w3c-webmcp-cg-draft.** [W3C Web Machine Learning Community Group — WebMCP API (Draft Community Group Report)](https://webmachinelearning.github.io/webmcp/). W3C WebML CG. Accessed 2026-08-07.
1. **src-wa-digiday-dcn-referral-drop.** [Digiday — Google AI Overviews linked to publisher referral traffic drops, new data shows](https://digiday.com/media/google-ai-overviews-linked-to-25-drop-in-publisher-referral-traffic-new-data-shows/). Digiday. Accessed 2026-08-07.
1. **src-wa-digiday-similarweb-zero-click.** [Digiday — In graphic detail: AI platforms are driving more traffic, but not enough to offset zero-click search](https://digiday.com/media/in-graphic-detail-ai-platforms-are-driving-more-traffic-but-not-enough-to-offset-zero-click-search/). Digiday. Accessed 2026-08-07.
1. **src-wa-arxiv-verifiability-2304-09848.** [Liu et al. — Evaluating Verifiability in Generative Search Engines](https://arxiv.org/abs/2304.09848). arXiv. Accessed 2026-08-07.