We captured answers from gemini and google_aio for 11 AI-agent verification prompts — here's which brands they mention, and whether the checked citations (google_aio only) held up as fetched

By Daily AI Agents (usedailyai.com). Engine queries issued and answers captured 20260706T173147Z (UTC); cited pages fetched and verified in a verification pass at 20260706T181500Z (UTC) — per-row fetch timestamps in Appendix B. Appendix B embeds every checked citation row (claim, cited URL, fetched evidence excerpt, verdict, fetch timestamp) and Appendix C embeds the yes/no mention matrix behind the mention-rate table; raw answer captures and extraction logs are not included in this draft and are available on request.

Method (partially reproducible from this artifact; raw captures, extraction logs, and code are not embedded and are available on request)

• chatgpt: chatgpt.com chat UI (requires authenticated session)
• gemini: gemini.google.com chat UI, default model, authenticated account
• google_aio: google.com desktop web search (en-US), 'AI Overview' block when served
• perplexity: perplexity.ai web search UI, no login
• Queries issued 20260706T173147Z (UTC) from a US location, desktop viewport, one fresh browser tab per query in a persistent automation browser profile (cookies and login state persist across runs; gemini was queried with an authenticated Google account, google_aio was queried logged-out). We set no custom personalization parameters, but account, cookie, IP, location, and engine-side personalization may affect outputs — disclosed as a limitation.
• Capture format: one JSON per engine×prompt (status, full answer text, citation URLs). Blocked/unavailable engines are recorded as blockers, never synthesized.
• Claim extraction (deterministic): answers are split into sentences; candidates are sentences of 40-600 chars carrying a factual signal (tracked brand, number, or proper noun), paired with their inline-cited URL when the answer marks one, otherwise with the answer's first cited URLs. Candidates are ranked by a fixed priority (brand-bearing first, then inline-cited, then number-bearing, then longer text) and the top 3 per answer are checked; those rules are the complete selection standard, and the implementing code is retained with the run artifacts and shared on request. Google AI Overview text is captured as rendered, so extracted claim snippets can include adjacent source labels — snippets are shown verbatim, not cleaned.
• Support check, in order: (1) quote-level containment — the claim's content words and every number must appear in the fetched page (>=75 percent word coverage = supported, <30 percent = unsupported); (2) when containment is inconclusive, a model verdict (ollama/qwen3.5:latest; temperature 0; input = claim + up to ~2.5k chars of the fetched page around the best-matching passage; instructed to answer supported/contradicted/not_found strictly from the excerpt, no outside knowledge; no confidence threshold — the one-word verdict is recorded as-is, with no human overrides).
• Page fetches: plain HTTP GET (python3 urllib, desktop-Chrome user agent, 10s timeout, no JavaScript rendering, no retries). JS-heavy pages (e.g. YouTube) can under-credit — a limitation we publish rather than hide. Verdicts describe the page as served to that fetch.

What we scanned

• Engines that returned answer text: gemini, google_aio. Citation support was evaluated only for: google_aio.
• chatgpt: not measured in this run — skipped — no authenticated session was available for this run, so it was not queried (we do not automate logins) (reason recorded in the retained operator log). Excluded from every metric below.
• perplexity: not measured in this run — skipped — automated collection was refused by the site this run (reason recorded in the retained operator log). Excluded from every metric below.
• Citation support was not evaluated for gemini because its retained answer captures contained no source links this run (no links to check is itself a finding); because those captures are not embedded here, its mention metrics are unaudited from this document alone.
• Prompts: 11 queries, all listed in Appendix A. Selection criteria: category-discovery queries a buyer of AI-agent verification/observability would ask, plus one brand-name query for each tracked vendor (us + 3 competitors).

Who the engines mention (mention rate in this run)

Definition: mention rate in this run = engine×prompt cells where the brand is mentioned / 22 answered cells from gemini and google_aio. One cell = one engine's answer to one prompt; a brand counts at most once per cell, however often it is repeated. The full 22-cell mention matrix, computed by the pipeline from the retained captures, is embedded as Appendix C; rows for engines whose captures are not embedded here are unaudited from this document alone.

Did the citations hold up? (citation support rate)

Mention-only share-of-voice reports can miss whether cited pages support the claims attached to them; this scan checks both.

Definition: citation support rate = supported claims / checked claims, per the support check defined in the Method section. 'Supported' means source-contained: the cited page itself states the claim. It does not independently verify evaluative content inside a claim (e.g. 'leading', 'best overall') — those remain the cited page's opinion.

All 4 citation-claim checks that did NOT hold up as fetched

• Engine: google_aio — prompt: "What is Langfuse used for?"
• Claim as answered (verbatim capture): "11:49 YouTube · Dave Ebbelaar langfuse/langfuse: Open source AI engineering platform: LLM ... - GitHub GitHub - langfuse/langfuse: Open source AI engineering platform: LLM evals, observability, metrics, prompt management, playground, GitHub Langfuse: Free Open Source LLM Engineering Platform apps le"
• Cited page: https://www.youtube.com/watch?v=tKBKvRzHREk (HTTP 200, fetched 2026-07-06T17:57:07 UTC)
• Verdict: unsupported — The excerpt contains only YouTube player configuration data and does not mention Dave Ebbelaar, Langfuse, or Lenfuse.

• Engine: google_aio — prompt: "What is Langfuse used for?"
• Claim as answered (verbatim capture): "GitHub +1 5 sites Get Started with Langfuse - Open-Source LLM Monitoring in this video I'm going to show you how you can set up your own platform to monitor your LLM applications fully open source and we..."
• Cited page: https://www.youtube.com/watch?v=tKBKvRzHREk (HTTP 200, fetched 2026-07-06T17:57:10 UTC)
• Verdict: unsupported — The excerpt contains only technical YouTube player configuration data and does not mention GitHub, Langfuse, or the video content described in the claim.

• Engine: google_aio — prompt: "What is Langfuse used for?"
• Claim as answered (verbatim capture): "GitHub +1 It is framework-agnostic (integrating with OpenAI, LangChain, LlamaIndex, etc.) and can be accessed via Langfuse Cloud or self-hosted on your own infrastructure."
• Cited page: https://www.youtube.com/watch?v=tKBKvRzHREk (HTTP 200, fetched 2026-07-06T17:57:10 UTC)
• Verdict: unsupported — claim words absent from cited page (coverage=0.14)

• Engine: google_aio — prompt: "What is Peec AI?"
• Claim as answered (verbatim capture): "21:00 YouTube · Ako Stark Tutorials Peec AI Review: Does it live up to the AI visibility hype? - Profound Profound measures this metric two ways."
• Cited page: https://growverge.com/peec-ai-review-2025-trial-marketers/ (HTTP 200, fetched 2026-07-06T17:57:18 UTC)
• Verdict: contradicted — The excerpt states that Peec AI is a GEO tool designed to monitor visibility and mentions across major AI chatbots (ChatGPT, Gemini, Perplexity), whereas the claim incorrectly attributes this specific metric measurement function to "Profound".

Additionally, 2 checked citation row(s), pointing at 1 distinct URL(s), could not be fetched as readable page content (rows marked unfetchable in Appendix B, with the fetch outcome recorded) — this measures fetchability by our published method, not the truth of the underlying page. They are excluded from the support-rate denominator, but a citation a reader cannot open is a citation a reader cannot verify.

Why we publish this

Daily AI Agents (usedailyai.com) is testing an early verification workflow for AI-agent outputs and citations; this draft demonstrates one sample of that workflow — fetch the source, compare the claim to the retrieved content, record a verdict — and we publish the rows including the unflattering one: Daily AI Agents appeared in 2 of 22 answered engine×prompt cells in this scan.

The offer we're testing

Labeled clearly: this is the offer we are testing with this report.

• Free discovery tier — we attempt 5-10 prompts in your category across the engines listed in the Method section (engines that block, lack access, or return no answer text are reported and excluded from metrics) and send you the mention-rate + citation-support table for your brand vs 3 competitors, including the raw checked rows. Target delivery is 5 business days from prompt approval; if fewer than two engines return answer text for at least 80 percent of prompts after one retry during that window, no report is delivered unless you accept a partial report. No charge.
• Verification depth ($49-99/mo, price testing) — weekly best-effort scans across engines that are accessible under the documented run conditions; engines that block or lack required access in a given week are reported and excluded from that week's metrics (exactly as in this report). Blocked engines are retried once and then reported as unavailable. 'Returns data' means the engine produced answer text for at least 80 percent of your prompts — source links are not guaranteed and per-engine link availability is disclosed in each report. If fewer than two engines return data in a week, that week's scan is not billed. Includes the sampled row-level checked-citation dataset for your brand (up to the top 3 eligible claims per answer, per the published method) and an email note when a checked cited claim about you is unsupported, contradicted, or unfetchable under the published method.

Free-tier conditions: the discovery scan is free, with no obligation; the brand/category names you submit are handled per the paragraph below and we may follow up once by email about the results — nothing more without your consent.

Data handling — interim statement: brand/category names you send us are used only to run your scan and produce your report, and are handled manually by the founder using the tools required to receive requests, run scans, store reports, and communicate results. They are retained for 12 months or deleted on request (confirmed by reply), and never published or added to any public report without your explicit OK. A formal privacy policy with subprocessors, retention, deletion, and security controls will be published before any free or paid customer data collection begins. Privacy contact: [email protected].

Reply or book at https://cal.com/daily-ai-agents/30min — [email protected].

Appendix A — the 11 prompts

• AI agent verification service
• How do I verify my AI agents actually work?
• AI agent audit
• AI agent observability tools
• tools to monitor how AI search engines talk about my brand
• Do AI search engines cite accurate sources?
• quality gates for AI agents before they act
• What is Daily AI Agents (usedailyai.com)?
• What is Profound (tryprofound.com)?
• What is Peec AI?
• What is Langfuse used for?

Appendix B — every citation-check row produced by the verification pass, with fetched evidence

This is the complete set of citation-check rows output by verification pass 20260706T181500Z — no rows were selectively omitted (completeness of claim candidates depends on the extraction rules and raw captures described in the Method section; those artifacts are not embedded here). 'Evidence excerpt' is the best-matching passage from the cited page as fetched. 'verified' = source-contained per the definition above.

Row 1 — engine google_aio, prompt "AI agent audit", fetched 2026-07-06T17:56:56 UTC

• Claim (verbatim capture): "The Pedowitz Group For tips on how to get your AI logging, observability, and tracing architecture just right: 1m How I automated my AI Audit Process (How you can do it too) AI with Vlad YouTube · Feb 12, 2026 3 sites How do you audit what an AI agent actually did? : r/devops - Reddit AI Audit Imple"
• Cited page: https://www.reddit.com/r/devops/comments/1qtmzt3/how_do_you_audit_what_an_ai_agent_actually_did/ (HTTP 200)
• Evidence excerpt: "Fix What You Have Let Us Run It HubSpot for Financial Services MARKETING SERVICES Creative and Content Website Development CRM Sales Enablement Demand Generation Resources Revenue Marketing - The Complete Hub Revenue Marketing and AI Guides Revenue Marketing and AI Assessments The Revenue Marketing Blog Books About Us About The Pedowitz Group Case Studies Industries we Serve Contact Us Audit AI Agent Decisions | Trac"
• Verdict: unfetchable (fetch) — anti-bot/verification interstitial served to fetch; page content not readable

Row 2 — engine google_aio, prompt "AI agent audit", fetched 2026-07-06T17:56:57 UTC

• Claim (verbatim capture): "The Pedowitz Group +2 To build an auditable AI agent architecture, follow these five steps: The Pedowitz Group Instrument Tracing: Log all raw message streams (e.g., in JSON format), correlation IDs, prompt inputs, context retrieved from your knowledge base, and specific tools executed."
• Cited page: https://www.reddit.com/r/devops/comments/1qtmzt3/how_do_you_audit_what_an_ai_agent_actually_did/ (HTTP 200)
• Evidence excerpt: "Audit Essentials Checklist 1 Log inputs, outputs, tools, costs with correlation IDs 2 Enforce pre- and post-action policy validators 3 Require approvals and reason codes on risky steps 4 Store immutable, searchable traces with retention SLAs 5 Add rollback, kill-switches, and incident playbooks Audit Implementation Process Step What to do Output Owner Timeframe 1 Define audit scope & risk tiers for each decision Deci"
• Verdict: unfetchable (fetch) — anti-bot/verification interstitial served to fetch; page content not readable

Row 3 — engine google_aio, prompt "AI agent audit", fetched 2026-07-06T17:56:58 UTC

• Claim (verbatim capture): "Reddit +1 Action Validation: Enforce pre-action and post-action policy validators to prevent the agent from taking unauthorized or dangerous actions."
• Cited page: https://www.pedowitzgroup.com/audit-ai-agent-decisions-trace-verify-govern (HTTP 200)
• Evidence excerpt: "Audit Essentials Checklist 1 Log inputs, outputs, tools, costs with correlation IDs 2 Enforce pre- and post-action policy validators 3 Require approvals and reason codes on risky steps 4 Store immutable, searchable traces with retention SLAs 5 Add rollback, kill-switches, and incident playbooks Audit Implementation Process Step What to do Output Owner Timeframe 1 Define audit scope & risk tiers for each decision Deci"
• Verdict: verified (llm_verdict, ollama/qwen3.5:latest) — The excerpt explicitly lists "Enforce pre- and post-action policy validators" as an item in the Audit Essentials Checklist.

Row 4 — engine google_aio, prompt "AI agent observability tools", fetched 2026-07-06T17:57:01 UTC

• Claim (verbatim capture): "Langfuse: A leading open-source, self-hostable platform for prompt versioning and workflow tracing."
• Cited page: https://latitude.so/blog/best-ai-agent-observability-tools-2026-comparison (HTTP 200)
• Evidence excerpt: "Langfuse and Arize Phoenix are the leading open-source/self-hosted options; Traceloop/OpenLLMetry is the OTel-native instrumentation standard."
• Verdict: verified (containment) — quote-level containment (coverage=1.00)

Row 5 — engine google_aio, prompt "AI agent observability tools", fetched 2026-07-06T17:57:01 UTC

• Claim (verbatim capture): "Eva... www.braintrust.dev 7 Best AI Agent Observability Tools for Coding Teams in 2026 Leading AI agent observability tools for coding teams in 2026 include Braintrust, LangSmith, Arize Phoenix/AX, Helicone, Galileo, ..."
• Cited page: https://latitude.so/blog/best-ai-agent-observability-tools-2026-comparison (HTTP 200)
• Evidence excerpt: "Comparison at a Glance Tool Agent Workflow Support Issue Discovery Eval from Production Pricing Deployment Latitude Native — causal session trace Yes — Signals + closed loop (issue → PR via MCP) Yes — auto-generated from real failures Free (20K credits/mo); Pro $99/mo; self-hosted free (MIT) Cloud + self-hosted Langfuse Strong multi-step tracing No No — manual Open-source free; Cloud plans Cloud + self-hosted LangSmi"
• Verdict: verified (containment) — quote-level containment (coverage=0.88)

Row 6 — engine google_aio, prompt "AI agent observability tools", fetched 2026-07-06T17:57:03 UTC

• Claim (verbatim capture): "IBM 5 best AI agent observability tools for agent reliability in 2026 - Braintrust 5 best AI agent observability tools for agent reliability in 2026 * Braintrust - Best overall AI agent observability platform."
• Cited page: https://latitude.so/blog/best-ai-agent-observability-tools-2026-comparison (HTTP 200)
• Evidence excerpt: "Best AI Agent Observability Tools in 2026: A Comparison for Production Teams | Latitude Pricing Docs Changelog Resources Community AI PM Course AI Evaluation Playbook Prompt Engineering Jobs Log in 4.3k Sign up Pricing Docs Changelog Resources Community AI PM Course AI Evaluation Playbook Prompt Engineering Jobs Log in Sign up Blog › Best AI Agent Observability Tools in 2026: A Comparison for Production Teams Best AI"
• Verdict: verified (containment) — quote-level containment (coverage=0.89)

Row 7 — engine google_aio, prompt "What is Langfuse used for?", fetched 2026-07-06T17:57:07 UTC

• Claim (verbatim capture): "11:49 YouTube · Dave Ebbelaar langfuse/langfuse: Open source AI engineering platform: LLM ... - GitHub GitHub - langfuse/langfuse: Open source AI engineering platform: LLM evals, observability, metrics, prompt management, playground, GitHub Langfuse: Free Open Source LLM Engineering Platform apps le"
• Cited page: https://www.youtube.com/watch?v=tKBKvRzHREk (HTTP 200)
• Evidence excerpt: "(function() {window.ytplayer={}; ytcfg.set({"CLIENT_CANARY_STATE":"none","DEVICE":"cbr\u003dChrome\u0026cbrand\u003dapple\u0026cbrver\u003d126.0\u0026ceng\u003dWebKit\u0026cengver\u003d537.36\u0026cos\u003dMacintosh\u0026cosver\u003d10_15_7\u0026cplatform\u003dDESKTOP","DISABLE_YT_IMG_DELAY_LOADING":false,"ELEMENT_POOL_DEFAULT_CAP":75,"EVENT_ID":"cexLatmFGI_l7rcPiMLRqQc","EXPERIMENT_FLAGS":{"PremiumClientSharedConfig"
• Verdict: unsupported (llm_verdict, ollama/qwen3.5:latest) — The excerpt contains only YouTube player configuration data and does not mention Dave Ebbelaar, Langfuse, or Lenfuse.

Row 8 — engine google_aio, prompt "What is Langfuse used for?", fetched 2026-07-06T17:57:10 UTC

• Claim (verbatim capture): "GitHub +1 5 sites Get Started with Langfuse - Open-Source LLM Monitoring in this video I'm going to show you how you can set up your own platform to monitor your LLM applications fully open source and we..."
• Cited page: https://www.youtube.com/watch?v=tKBKvRzHREk (HTTP 200)
• Evidence excerpt: "(function() {window.ytplayer={}; ytcfg.set({"CLIENT_CANARY_STATE":"none","DEVICE":"cbr\u003dChrome\u0026cbrand\u003dapple\u0026cbrver\u003d126.0\u0026ceng\u003dWebKit\u0026cengver\u003d537.36\u0026cos\u003dMacintosh\u0026cosver\u003d10_15_7\u0026cplatform\u003dDESKTOP","DISABLE_YT_IMG_DELAY_LOADING":false,"ELEMENT_POOL_DEFAULT_CAP":75,"EVENT_ID":"cexLatmFGI_l7rcPiMLRqQc","EXPERIMENT_FLAGS":{"PremiumClientSharedConfig"
• Verdict: unsupported (llm_verdict, ollama/qwen3.5:latest) — The excerpt contains only technical YouTube player configuration data and does not mention GitHub, Langfuse, or the video content described in the claim.

Row 9 — engine google_aio, prompt "What is Langfuse used for?", fetched 2026-07-06T17:57:10 UTC

• Claim (verbatim capture): "GitHub +1 It is framework-agnostic (integrating with OpenAI, LangChain, LlamaIndex, etc.) and can be accessed via Langfuse Cloud or self-hosted on your own infrastructure."
• Cited page: https://www.youtube.com/watch?v=tKBKvRzHREk (HTTP 200)
• Evidence excerpt: "(function() {window.ytplayer={}; ytcfg.set({"CLIENT_CANARY_STATE":"none","DEVICE":"cbr\u003dChrome\u0026cbrand\u003dapple\u0026cbrver\u003d126.0\u0026ceng\u003dWebKit\u0026cengver\u003d537.36\u0026cos\u003dMacintosh\u0026cosver\u003d10_15_7\u0026cplatform\u003dDESKTOP","DISABLE_YT_IMG_DELAY_LOADING":false,"ELEMENT_POOL_DEFAULT_CAP":75,"EVENT_ID":"cexLatmFGI_l7rcPiMLRqQc","EXPERIMENT_FLAGS":{"PremiumClientSharedConfig"
• Verdict: unsupported (containment) — claim words absent from cited page (coverage=0.14)

Row 10 — engine google_aio, prompt "What is Peec AI?", fetched 2026-07-06T17:57:14 UTC

• Claim (verbatim capture): "Marketer Milk Peec.ai Review & Tutorial 2025 — Full Beginner's Guide in today's video we're going to do a deep review dive of a AI visibility tool Peak AI this tool helps brands monitor how visible t..."
• Cited page: https://growverge.com/peec-ai-review-2025-trial-marketers/ (HTTP 200)
• Evidence excerpt: "Peec AI Review 2025: Our 7-Day Trial—The Unbiased Truth for Marketers - Growverge Home Solutions Company Results Resources Schedule a Consultation FAQ Home Solutions Company Results Resources Schedule a Consultation FAQ Client Support +1 (419) 314-6577 +44 (7312) 702665 Contact Us Home Solutions Company Results Resources Schedule a Consultation FAQ Home Solutions Company Results Resources Schedule a Consultation FAQ "
• Verdict: verified (llm_verdict, ollama/qwen3.5:latest) — The excerpt confirms Peec.ai is an AI visibility tool that helps brands monitor their presence across major AI chatbots.

Row 11 — engine google_aio, prompt "What is Peec AI?", fetched 2026-07-06T17:57:18 UTC

• Claim (verbatim capture): "21:00 YouTube · Ako Stark Tutorials Peec AI Review: Does it live up to the AI visibility hype? - Profound Profound measures this metric two ways."
• Cited page: https://growverge.com/peec-ai-review-2025-trial-marketers/ (HTTP 200)
• Evidence excerpt: "Peec AI Review 2025: Our 7-Day Trial—The Unbiased Truth for Marketers - Growverge Home Solutions Company Results Resources Schedule a Consultation FAQ Home Solutions Company Results Resources Schedule a Consultation FAQ Client Support +1 (419) 314-6577 +44 (7312) 702665 Contact Us Home Solutions Company Results Resources Schedule a Consultation FAQ Home Solutions Company Results Resources Schedule a Consultation FAQ "
• Verdict: contradicted (llm_verdict, ollama/qwen3.5:latest) — The excerpt states that Peec AI is a GEO tool designed to monitor visibility and mentions across major AI chatbots (ChatGPT, Gemini, Perplexity), whereas the claim incorrectly attributes this specific metric measurement function to "Profound".

Row 12 — engine google_aio, prompt "What is Peec AI?", fetched 2026-07-06T17:57:18 UTC

• Claim (verbatim capture): "AI Overview Peec AI is a Generative Engine Optimization (GEO) analytics platform that helps marketing and SEO teams monitor how their brand is mentioned, cited, and described across major AI search platforms like ChatGPT, Perplexity, Gemini, and Google AI Overviews."
• Cited page: https://www.linkedin.com/pulse/peec-ai-review-sanjay-singh-tj3yf (HTTP 200)
• Evidence excerpt: "Peec AI is an AI search analytics platform built for marketing and SEO teams that want to understand how their brand shows up across generative search engines and assistants."
• Verdict: verified (containment) — quote-level containment (coverage=0.88)

Appendix C — the full engine×prompt mention matrix (22 answered cells)

One row per answered engine×prompt cell; each brand column is yes/no for whether that answer mentioned the brand (by name/alias or domain).