I’m watching OpenAI discontinue ChatGPT Atlas, its standalone desktop browser, and move its browser-based AI features into the new ChatGPT desktop app. That app brings together ChatGPT Work, OpenAI’s work-focused agent, and ChatGPT Codex.
The end of Atlas. I’m taking note of an Aug. 9 retirement date after OpenAI’s James Sun confirmed the plan on X.
I’m also noting Sun’s exact wording: “The current targeted date for deprecation is 8/9, and we’ll share more information in the upcoming days both in-app and via email.”
One desktop app. I see the new ChatGPT desktop app becoming OpenAI’s primary desktop product, complete with built-in browser capabilities. Instead of maintaining a separate AI browser, OpenAI is combining browsing, work-agent features, and Codex in one place.
Chrome users can keep Chrome. If I prefer using Chrome, I can access ChatGPT and Codex through OpenAI’s Chrome extension without switching to a dedicated OpenAI browser.
As AI reshapes search, every glowing path to discovery carries commercial value—turning SEO investment into a conversation about pipeline, risk, and customer acquisition costs.
Why I care. I see this as an important shift because OpenAI is moving AI browsing into the main ChatGPT experience, where more people can ask questions, research brands, and complete tasks. In my view, that gives ChatGPT another opportunity to influence discovery beyond traditional search results.
I first saw ChatGPT Atlas launch on Mac in October. OpenAI later released a dedicated Codex app and added an in-app browser in April. Now, I’m watching those capabilities move into the new unified ChatGPT desktop app.
I see the next major battleground for brands being shaped by AI. Every day, AI engines and autonomous agents decide which brands to recommend, compare, cite, and transact with on behalf of consumers. To compete, I have to make my brand the trusted choice AI selects.
This shift is already underway. Adobe data shows that AI-referred traffic to U.S. retail websites grew 4,700% year over year through mid-2025. Salesforce reports that AI and autonomous agents influenced one in five online orders globally during Cyber Week, driving an estimated $67 billion in sales.
As AI becomes the interface between consumers and brands across discovery, evaluation, and purchase, I need to think beyond traditional rankings. A new competitive layer is emerging: the AI decision layer. This is where AI systems evaluate trust, relevance, authority, and transaction readiness before deciding which brands make the shortlist.
If I fail to influence this layer, my brand may be excluded before a customer ever sees it. That makes AI visibility, credibility, and actionability core parts of modern search strategy.
How I take a brand from found to actioned
Agentic commerce readiness follows a clear sequence. I start by making sure AI engines can find my brand, then I move through the remaining stages until AI agents can understand, trust, recommend, and transact with it.
Step 1: I get found by enabling AI discovery and access
Machine accessibility is the foundation of AI visibility. If I want AI systems to discover and access my brand, I have to prioritize technical hygiene and token efficiency.
I start by allowing the right crawlers on my website. Google, OpenAI, Anthropic, and Bing need to reach my content without unintended restrictions.
Then I get the basics right. I set up XML sitemaps and robots.txt, fix crawl errors, add canonical tags, and maintain strong Core Web Vitals. I also make sure my website content is rendered server-side so agents can reliably navigate and reason over my pages.
I also pay close attention to token efficiency. Bloated HTML wastes valuable tokens that AI systems could otherwise use to understand my content, products, and brand.
To make my site more AI-ready, I publish assets that help large language model crawlers process my content more efficiently. An llms.txt file can give LLM crawlers a concise map of my website, while Markdown versions of key content can reduce token consumption and improve machine understanding.
Between consumers and brands, AI agents now act as the decision layer, handling discovery, evaluation, trust signals, and transactions before products reach the shortlist.
Step 2: I become understood by building semantic clarity
To be understood by AI engines, I need to build entity authority. This helps AI interpret who I am, what I offer, and why my brand matters.
Structured data turns my web pages into machine-readable knowledge that AI systems can understand, trust, and use. I strengthen my entity graph with comprehensive schema, trusted citations, and linked references.
I also deliver clean, server-rendered HTML that AI can access without friction. Semantic HTML, structured @graph IDs, and consistent naming help AI engines connect the right context to my brand.
Step 3: I get retrieved by structuring content for AI extraction
Traditional search ranks pages, but AI search retrieves and cites passages. That means I win on relevance, clarity, authority, and freshness rather than length alone. Original expertise, proprietary data, and real-world experience give my content a stronger chance of being selected.
To structure my content for retrieval, I use a clear heading hierarchy with H1, H2, and H3 tags. Under each heading, I create descriptive, self-contained sections that can stand on their own.
I build interconnected topic clusters instead of isolated pages because AI needs enough context to assemble complete answers.
I also front-load every section. I put the core answer and the most important metrics in the opening sentence before a model hits its token limit.
Step 4: I build trust with authority and grounding signals
Just because AI engines retrieve my content does not mean they will recommend my brand. Retrieval is only one step. Trust is what moves a brand closer to selection.
AI systems prioritize sources they can trust, so authority and credibility become decisive. Google’s experience, expertise, authoritativeness, and trustworthiness principles, known as E-E-A-T, remain some of the strongest signals influencing whether a brand is cited, referenced, or selected.
A visual roadmap for becoming the brand AI selects: first be found and understood, then retrieved, trusted, chosen and finally actioned by autonomous assistants.
Trust extends far beyond my website. AI evaluates review sentiment, location accuracy, pricing consistency, product availability, and entity alignment across the web. When those signals conflict, AI confidence decreases.
Credibility is now computational. Grounding, the process of validating responses against trusted evidence, is the bridge between visibility and recommendation.
To earn computational trust, I create original, expert-driven content that shows real experience and unique value. Then I align every external signal so reviews, listings, maps, and directories all tell one consistent story about my brand.
Step 5: I get chosen by earning machine and human preference
AI agents parse attributes, verify claims, and score confidence in milliseconds. If I cannot make my value clear to AI, my brand becomes invisible at the decision point.
But emotional preference still matters. Consumers may delegate routine purchases, yet they hold tightly to choices tied to identity. The strongest brands optimize for both machine readability and human resonance.
To earn AI recommendations, I measure AI visibility, citation, and recommendation rates through query fan-out testing. I keep brand, product, and location data consistent across every channel. I also work to earn trusted mentions and references that strengthen AI confidence in my brand.
Recommendation is no longer the finish line for AI search. Discovery, selection, and checkout can now happen inside an AI assistant without the customer ever visiting my site.
An agentic website is designed for AI agents to discover information, retrieve answers, and perform actions on behalf of users. NLWeb helps make website content conversational and machine-readable, improving how AI systems find and understand the site.
A bold Google logo sits atop layered, colorful digital documents, evoking the fast-moving world of search marketing, ad formats, campaign assets, and platform updates.
Web Model Context Protocol, or MCP, extends this capability by giving AI agents a standardized way to interact with website functions. That can include retrieving data, initiating workflows, and submitting forms.
Agentic commerce moves the full transaction inside the assistant. Google’s Universal Commerce Protocol, or UCP, enables chat-based bookings. OpenAI and Stripe’s Agentic Commerce Protocol, or ACP, pushes inventory so AI systems can surface it more easily. Agent Payments Protocol, or AP2, then lets the agent pay.
Underneath these capabilities is MCP, which enables an LLM to read products, content, and live data. This changes my website from a destination into a source of truth. It supplies the inventory, pricing, and signals that drive every agent journey.
How I measure performance in the AI decision layer
I still track traditional search metrics like rankings, sessions, and clicks. They remain useful, but they are no longer enough to measure success in AI search and agentic commerce.
For visibility, I track AI presence rate, AI share of voice, citation frequency, and agent recommendation rate.
For commerce, I track AI-influenced revenue, agent conversion rate, autonomous transaction volume, and agentic wallet share.
I also expect traffic patterns to change. Direct visits may decline as agents handle discovery, but AI-influenced transactions through machine-readable layers like WebMCP and schema endpoints can offset that loss and create new revenue paths.
With these changes in place, my website can become the trusted source AI systems rely on for both information and action.
From SEO to decision architecture
SEO remains the foundation for winning search, but a deeper shift became concrete at Google I/O 2026. AI agents now parse raw HTML, distill the browser’s native accessibility tree, and capture visual screenshots through vision models.
Together, these three paths determine whether a site is truly actionable for AI. My page can be technically flawless and still fail if its structure, semantics, or user experience breaks the chain. If I miss any stage, trust and transaction readiness suffer.
When I get these pieces right, my brand becomes discoverable, understandable, trusted, and transactable when AI agents make decisions. The brands that build these capabilities today will be the brands AI surfaces, trusts, and recommends tomorrow.
I am introducing support for Grok 4.5 in Profound, bringing SpaceXAI’s newest flagship model into workflows built for deeper, more capable AI analysis.
Grok 4.5 is designed for agentic workflows and knowledge work, which makes it a strong fit for teams and operators who need AI systems that can reason, assist, and move complex tasks forward with more context.
With this support now available in Profound, I can use Grok 4.5 as part of a broader AI workflow and explore how its capabilities help with research, strategy, automation, and day-to-day knowledge work.
I see Agentic Search Optimization (ASO) as one of the biggest shifts in AI search because AI systems are no longer only recommending options for people to review. They can now complete the action themselves. That changes the goal: instead of simply earning a recommendation, a brand needs to become the option an AI agent actually selects.
That is where ASO differs from GEO, or Generative Engine Optimization. GEO helps a brand appear in AI-generated recommendations, while ASO goes further by preparing the brand to be chosen when an AI agent evaluates options and takes action. In my view, the strongest ASO agencies are the ones that already understand GEO and can also shape the way AI agents retrieve, evaluate, and act on information.
During Q2 2026, I reviewed a dataset of 38 U.S. agencies offering ASO and GEO services. I ranked each agency using a weighted set of criteria designed to measure both current ASO capability and the underlying search expertise needed to support it.
ASO Expertise Score (25%): I scored each leadership team from 1 to 5 based on its depth of ASO knowledge, with higher marks for agencies that have published original ASO research or offer ASO as a named service.
Average Review Score (20%): I looked at aggregated ratings across major third-party review platforms to evaluate client satisfaction.
Notable Clients (20%): I considered the quality and breadth of each agency’s client roster as a signal of its ability to handle complex engagements.
AI Visibility Score (15%): I evaluated how consistently each agency’s clients appear in AI-generated results, which reflects strength in the Retrieval stage of ASO.
Media References (10%): I used industry citations and third-party references as a signal of credibility and market recognition.
Year Established (10%): I factored in accumulated experience in SEO, GEO, and related disciplines because ASO builds directly on those foundations.
Based on that methodology, these are my top Agentic Search Optimization agencies of 2026, followed by a closer look at what each firm does best.
The Top Agentic Search Optimization (ASO) Agencies of 2026
Bay Path University, Procept BioRobotics, Scholarship America
3.9
~80
2004
GEO for higher education and healthcare brands
First Page Sage
I rank First Page Sage first because it is the only agency in this group that has published original research specifically on Agentic Search Optimization. Its research draws on a study of 2,417 agentic commands across major AI platforms, and its ASO framework covers the full agentic search cycle: Retrieval, Evaluation, and Action. It also adds a Verification layer to keep brand claims consistent wherever an AI agent encounters them.
What stands out to me is the agency’s AI Belief Landscape methodology. Before creating content, First Page Sage audits what major AI models currently believe about a brand, which addresses one of the core challenges of ASO with unusual precision. The agency also has the highest media reference count in my dataset by a wide margin, giving it the strongest third-party credibility in this ranking. I see it as the best fit for companies that want a comprehensive, long-term ASO or Agentic GEO strategy grounded in a documented framework.
ASO Expertise Score: 5.0
Average Review Score: 4.9
Notable Clients: Salesforce, Logitech, Verizon, Dignity Health
Clients describe “a team with outstanding insights into the full agentic search cycle,” praise “strategies that started generating results within the first quarter,” and highlight that “the quality of AI-driven buyers was unlike anything we’d seen before.”
Genevate
I see Genevate as one of the earliest agencies built specifically for the generative AI era. It combines GEO strategy with strategic communications so brands can influence how AI platforms discover, describe, and recommend them. Its services include AI Visibility Audits, ASO and GEO strategy, reputation management, and AI workflow optimization.
Genevate earned the second-highest ASO Expertise Score in my review because it offers ASO as an explicit service. Its client portfolio currently skews toward high-intent commercial buyers rather than large enterprise accounts, which makes sense given the agency’s recent founding. I still see a clear strength here: clients often describe the founder-led model as highly engaged, strategic, and personally invested in the outcome.
ASO Expertise Score: 4.5
Average Review Score: 4.8
Notable Clients: ZipRecruiter, CBRE, Talentfoot
AI Visibility Score: 4.6
Media References: ~35
Year Established: 2025
Specialty: ASO/GEO with PR and reputation management
Genevate clients say “the team understood our goals,” credit the agency with “getting our brand into AI search recommendations,” and describe the content as “well-researched, although slightly dry.”
Siana Marketing
I include Siana Marketing because it has a clear specialization: construction, architecture, engineering, and real estate. Its GEO practice focuses on the content and authority signals that help firms appear in AI-generated recommendations when buyers are evaluating vendors, designers, or development partners in those markets.
Siana’s AI Visibility Score was one of the strongest in my dataset, suggesting that its GEO execution is translating well into ASO readiness. It is not the right fit for companies outside the AEC and real estate ecosystem, but that narrow focus is also its advantage. I value the category-specific search knowledge Siana brings because a generalist agency may not understand those buyer behaviors as deeply.
Clients say the team produces “content that shows up in AI-generated vendor recommendations.” Others note that “their strategy can feel templated.”
Signal Hill Strategies
I view Signal Hill Strategies as a lead-generation-focused agency that connects SEO, GEO, and Agentic GEO directly to qualified demand. Its engagements are built around how modern buyers research and choose, which makes the agency especially relevant for companies that want AI visibility tied to pipeline outcomes rather than vanity metrics.
Signal Hill’s AI Visibility Score reflects strong GEO and Agentic GEO execution. Clients note that its content is developed with lead generation in mind, not just clicks or impressions. Because the agency was founded recently, its client roster leans toward growth-stage companies and its media footprint is still limited. Even so, I see its ASO infrastructure as well aligned with where agentic AI search is heading.
Clients highlight that “the strategy was built around revenue goals,” credit the team’s “professionalism and communication,” and describe them as “focused on understanding our buyer.”
Onely
I rank Onely highly for companies that need the technical foundation of AI search to work correctly. Onely is a technical SEO agency focused on the backend foundations of search, and it has expanded its positioning into AI search readiness. Its work helps ensure that AI agents and crawlers can access, parse, and act on site content reliably.
Onely’s strength is also the reason it does not rank higher. Its work maps especially well to the Retrieval and Action stages of ASO because it focuses on crawlability, structure, and transactional readiness. The Evaluation stage, where an AI agent decides which vendor is the best fit for a user’s needs, depends more heavily on strategic content and authority building. For companies with complex site architecture, however, I see Onely as a technically credible choice.
ASO Expertise Score: 3.7
Average Review Score: 4.9
Notable Clients: eBay, IKEA, ServiceTitan
AI Visibility Score: 4.1
Media References: ~150
Year Established: 2019
Specialty: Technical SEO and AI search infrastructure
Clients credit Onely with “diagnosing technical crawl and indexing issues,” noting “improvements in organic traffic and site health.” Some suggest “keyword-level performance reporting could be more detailed.”
Media Cause
I include Media Cause because it brings a strong nonprofit specialization to AI search. The agency works exclusively with nonprofits, NGOs, and mission-driven organizations, offering SEO, content strategy, Google Ad Grants management, paid media, email marketing, branding, and data analytics. For nonprofits that want one agency to handle both search visibility and broader digital strategy, Media Cause offers unusual depth.
Its SEO practice is mature, and the team has published thinking on how GEO applies to nonprofits specifically. I see its mission-driven content approach as a useful foundation for the Evaluation stage of ASO, especially as donation and volunteer journeys become more agentic-ready. The limitation is clear: commercial and for-profit organizations are outside its market, no matter how well the methodology might otherwise fit.
ASO Expertise Score: 3.6
Average Review Score: 4.8
Notable Clients: AKC, NRDC, Stand Up to Cancer
AI Visibility Score: 4.0
Media References: ~200
Year Established: 2010
Specialty: Full-service digital marketing for nonprofits
Clients praise “a team that genuinely cares about mission impact,” credit Media Cause with “strong SEO results,” and note that the agency “can be slow to implement content feedback.”
WebSpero
I see WebSpero as a strong fit for specialized, lower-competition markets. The agency has built its GEO and SEO practice around niche brands, where targeted content and AI visibility work can produce meaningful returns without requiring the same level of authority-building needed in broader markets. That makes WebSpero especially relevant for growth-stage businesses in specialized categories.
WebSpero has the lowest ASO Expertise Score on my list because its GEO practice is still developing and it does not currently appear to offer ASO as a specific service. Still, I include it because niche markets often have clear buyer profiles and specific use cases, which are exactly the kinds of signals the Evaluation stage of ASO depends on. Building agentic-ready content on top of its GEO framework feels like a natural next step.
ASO Expertise Score: 3.5
Average Review Score: 4.8
Notable Clients: Ubie Health, Artsabers, K9 Academy
Clients highlight “visibility gains where other agencies had struggled to move the needle,” praise “a responsive team,” and suggest that “a broader digital strategy will need to be handled in-house or elsewhere.”
Zozimus
I include Zozimus because it brings full-service marketing depth to GEO and potential ASO work. The agency has roots in brand strategy, PR, digital marketing, SEO, and social media, and its GEO work has been especially relevant for higher education and healthcare clients. Its proprietary Zozimus Predict model adds monthly trend insights and KPI projections, which many smaller agencies do not provide.
Zozimus has the lowest AI Visibility Score in this study, which reflects a full-service model where GEO is one offering among many rather than the agency’s central focus. Even so, I see a credible ASO foundation here. Its PR and brand strategy work can support the authority signals needed for Evaluation, while its content practice can support Retrieval. I also see a natural path for Zozimus Predict to expand into agentic visibility tracking.
ASO Expertise Score: 3.6
Average Review Score: 4.4
Notable Clients: Bay Path University, Procept BioRobotics, Scholarship America
AI Visibility Score: 3.9
Media References: ~80
Year Established: 2004
Specialty: GEO for higher education and healthcare brands
Clients praise the agency’s “ability to manage creative, PR, and digital work under one roof,” while noting that “individual channels can feel less specialized than a single-discipline agency.”
I’m reading this Cornell Tech research as a clear warning: deep-research AI agents can be steered by surprisingly small edits on public, user-generated pages. In the study, a single injected Reddit-style comment could become a cited recommendation for fake products, services, or entities.
The researchers described these altered pages as “poisoned” because the added text was written to influence what an AI system cites and repeats. The weakness appears in systems that search the web, collect sources, and produce cited reports. The paper calls the attack WARP, short for Web Agent Retrieval Poisoning.
How I see injected text reaching reports. The attack does not require access to the model, prompts, search engine, or retrieval system. Instead, an attacker edits or appends text to a page the agent already tends to retrieve, such as a Reddit thread, Wikipedia page, or forum post.
When the agent later searches related topics, it may pull in that page, cite it, and repeat the attacker’s chosen message as part of an otherwise normal-looking answer.
That matters because deep-research tools often run many related searches for a single user request. The paper found that the same user-generated pages surfaced across related queries, giving poisoned content more chances to appear.
Reddit stood out as the biggest opening. Across STORM, Co-STORM, and OmniThink, 17% to 23% of retrieved URLs came from user-generated platforms, including Reddit, YouTube, Facebook, and Wikipedia.
Reddit made up the largest share of those pages. It accounted for 54% to 71% of the user-generated URLs retrieved by the three open-source systems.
The researchers did not alter live websites. Instead, they used a simulation framework called GeoStorm to insert manipulated text into retrieved content during testing.
A few words were enough. What stood out to me most is how little text the attack needed. The researchers found that snippets as short as about 13 words could influence what these systems recommended.
In one test, a 15-word sentence pushed a fake cryptocurrency, BananaCoin, into a Co-STORM report as an “emerging” long-term investment option. The report cited the altered source alongside legitimate crypto sources.
When the manipulated page was retrieved, the fake entity appeared in 38% to 51% of reports across systems. When the researchers targeted multiple pages, that range increased to 42% to 62%.
The attack still worked when systems retrieved full Reddit threads, although mention rates were lower. When injected text was added to complete Reddit threads and represented less than 4% of the retrieved content, the fake entity still appeared in 30% to 53% of reports when the page was retrieved.
The defenses struggled. Blocking user-generated domains stopped this attack path, but I see the tradeoff immediately: it also removes useful sources such as firsthand product experiences and local recommendations.
The tested text filters also failed to reliably separate injected passages from normal user content. Because the manipulated passages were fluent and written by an AI model, perplexity-based filters were more likely to flag normal user content than the injected text.
Report-level checks missed the manipulation too. The altered reports looked similar to clean reports because the agent itself folded the fake recommendation into an answer that otherwise appeared normal.
Why I care. A small edit to a public page can become part of a cited AI answer, even when the underlying source is user-generated. Misinformation planted on sites like Reddit or in forums can move from discussion threads into AI recommendations that look credible to users.
About the research. The paper, Deep-Research Agents Can Be Poisoned via User-Generated Content, was written by Tingwei Zhang, Harold Triedman, and Vitaly Shmatikov of Cornell Tech and posted to arXiv on May 22. The researchers tested the full attack on three open-source systems: STORM, Co-STORM, and OmniThink.
They also analyzed OpenAI Deep Research and Gemini Deep Research for user-generated citations, but they did not run live manipulation tests because doing so would require publishing altered content to the open web.
With Projects in Profound, I can turn my AEO data into a clear, ranked list of opportunities instead of another report I have to interpret from scratch.
Each opportunity is broken into practical tasks, with an agent ready to help do the work. That makes it easier for me to move from insight to execution without getting stuck in endless analysis.
For me, Projects is about spending less time deciding what to do next and more time acting on the opportunities that can improve visibility, performance, and momentum.
With Profound’s Agent Template Marketplace, I can start from pre-built AI agent workflows instead of building every process from scratch.
It gives me ready-to-clone templates designed for marketing, SEO, and AEO teams, so I can move from idea to live workflow in minutes.
For me, the biggest advantage is speed: I can choose a proven workflow, clone it, customize it for my team, and start using AI agents faster with less setup.
I’m looking at Yahoo! Scout as Yahoo’s most direct return to search and web discovery in years. The new AI-based answer engine is available at scout.yahoo.com, and Yahoo is also weaving it through its major properties, including Yahoo News, Yahoo Finance, Yahoo Mail and Yahoo Search. I think of it as a Yahoo-branded AI companion built to help people move through those familiar Yahoo experiences with more context and guidance.
What Yahoo Scout is. To me, Yahoo Scout is Yahoo’s version of an AI search engine and assistant, similar in broad idea to Google’s AI Mode or OpenAI’s ChatGPT, but with Yahoo’s own personality layered in. Yahoo told me it wanted Scout to feel fun, approachable and easy for people of all ages to understand.
When I first visited Yahoo Scout, the experience felt intentionally warm. The home page includes a search box, a playful slogan and an animated icon above it. Beneath the search box, Yahoo offers suggested searches that can be filtered by topics such as news, finance, sports, shopping and travel. On the left side, I could also see previous queries, making it easier to return to earlier searches and continue where I left off.
The home page also rotates through playful visual treatments. In one version I saw a cowboy hat, while other versions included a crystal ball, a gold medal, a walking cartoon brain and more.
Yahoo Scout’s advantage. The Yahoo Search team gave me early access to try Yahoo Scout. While the interface will feel familiar to anyone who has used other AI answer engines, the Yahoo-specific pieces are what stood out most to me.
Yahoo’s biggest advantage is its existing reach. The company already has a large audience across Yahoo Mail, Yahoo News, Yahoo Finance and Yahoo Search. Yahoo told me it has more than 500 million user profiles, stores signals such as queries, usage and intent, has more than one billion entities in its knowledge graph and processes 18 trillion consumer events and signals across its properties. That gives Yahoo a lot of context it can use to personalize AI search and better categorize queries.
Yahoo also told me it is the second-largest email company and the third-largest search engine.
Because Scout is connected to Yahoo’s own properties, it can bring Yahoo Finance widgets, financial data, tables, citations, weather results, news results and other rich content directly into answers.
“Search is fundamentally changing, and our team has been inspired to use our decades of experience and extremely rare assets to create something uniquely useful for Yahoo’s hundreds of millions of monthly users,” said Jim Lanzone, CEO of Yahoo. “This beta launch is just the starting point. From search to our industry-leading verticals, Yahoo Scout will help our users accomplish their goals online faster and better than ever before.”
Sending traffic to publishers. Jim Lanzone told me Scout is closely tied to Yahoo’s original mission of being a trusted guide to the internet. Because of that, Yahoo says it designed Scout with the open web in mind, including ways to send traffic downstream to content creators and publishers.
In Yahoo Scout responses, I saw large blue highlights over portions of the answer text. When I hovered over those highlights, I could click through to the source. Each response also includes a visible “featured source” area, along with tables, imagery, related news articles and other source-driven elements meant to make publisher links more prominent.
Lanzone told me early AI answer engines have not done enough to send traffic back to the sources behind their answers. Yahoo wants Scout to be an example of how that relationship can work better. Since there is not enough licensing revenue for every publisher to make deals with AI companies, Yahoo is leaning into the historical search model: give users answers, but also send meaningful traffic to the sites that produced the underlying content.
CTR expectations. I asked Yahoo what click-through rate it expects from Yahoo Scout to publishers. The honest answer was that it does not know yet. Yahoo expects to learn from real user data after launch and then iterate to improve downstream clicks.
Yahoo expects queries in Scout to be longer than queries in Yahoo Search. It also expects ad loads to be lighter, and the team hopes click-through rates will be higher than the industry average.
Yahoo also told me it plans to build a way for publishers to see impression and click data in the future. I see that as something like a Yahoo Webmaster Tools-style reporting experience, though crawling and indexing data would still be tied to Microsoft Bing because Bing powers the underlying search index.
Yahoo Scout across Yahoo properties. I expect Scout to show up throughout Yahoo’s ecosystem. Yahoo Search will use Scout-powered AI summaries. Yahoo News will provide article highlights and may include daily digest audio summaries. Yahoo Finance will add an Analyze button powered by Scout. Yahoo Mail will summarize emails and extract action items, such as adding events to a calendar.
Examples of Yahoo Scout in action. Yahoo Scout is not perfect, but for something Yahoo says was built in about six months, I came away impressed.
When I asked Yahoo Scout for help understanding how SEO works, it returned a useful response with citations throughout the summary. SEO is complex, and not everyone would agree with every part of the answer, but the citation structure made the experience more transparent.
I then asked it for sources I could use to find more content on the topic. There were clearly missed opportunities to link out more often, and I shared that feedback with Yahoo. The team agreed there was room to improve.
When I followed up by asking how I could navigate to the sources it had mentioned, Scout did provide links at that point. I also saw citation previews appear when hovering over linked highlights.
I tried several other types of searches as well. For entertainment queries, Scout pulled in news articles with larger graphics and clickable card-style formats. For finance queries, Yahoo brought in Yahoo Finance, though I was not able to generate stock charts during my own testing, even though I saw that capability in a demo. It may still have been in progress at the time.
For weather, I tested Scout on a Sunday morning as a major snowstorm was touching down in New York. I was able to get a Yahoo Weather chart, along with practical tips on how to stay warm.
For sports, I asked about Super Bowl predictions. As a lifelong Jets fan, I also asked whether the Jets had any chance of winning the Super Bowl in the next 10 years. The answer was not especially encouraging, but I was glad to see a chart embedded directly in the response.
For shopping, Scout gave me advice on how to dress for the weather. That is where Yahoo’s commerce strategy becomes more visible.
Ads and commissions. Yahoo Scout will show ads at the bottom of some responses. Commerce-related queries will also be monetized through affiliate commissions, which is already a common revenue model across the web.
Yahoo told me the ads are still powered by Microsoft Advertising, but Yahoo controls how those ads appear inside the Scout experience.
Those ads will be charged on a CPC basis, not on an impression basis like some other AI engines have announced. I also saw product results labeled with “Yahoo may earn commission from these links.”
How Yahoo Scout came together. Yahoo has been hinting for about three years that it wanted to return to the search game. In 2009, Yahoo made a deal with Microsoft to have Microsoft power Yahoo Search, which effectively ended Yahoo’s work on its own search technology. Since then, Yahoo has outsourced search technology until this new Scout effort.
About six months ago, Yahoo acquired Eric Feng’s company to lead consumer search at Yahoo. Feng co-founded the online video platform Mojiti, which Hulu acquired in 2007. He then became Hulu’s founding CTO and head of product. Before that, he worked in Microsoft Research on search-related problems.
“Yahoo’s deep knowledge base, 30 years in the making, allows us to deliver guidance that our users can trust and easily understand, and will become even more personalized over the coming months,” said Eric Feng, Senior Vice President and General Manager of Yahoo Research Group, the creators of Yahoo Scout. “Yahoo Scout now powers a new generation of intelligence experiences across Yahoo, seamlessly integrated into the products people use every day.”
Lanzone, who also has a long history in search from his years as CEO of Ask.com, told me Feng has been instrumental in building Yahoo Scout over the past six months. Yahoo says this first public release is only the beginning, and more iterations and improvements are expected.
Anthropic and Claude. Yahoo Scout is not built on Yahoo’s own LLM. Yahoo partnered with Anthropic and uses Claude as Scout’s primary foundational AI model. Anthropic, founded in 2021 by former OpenAI employees including Daniela Amodei and Dario Amodei, has become one of the leading AI companies. Amazon announced an investment of up to $4 billion in September 2023, Google committed $2 billion the following month, and as of November 2025 Anthropic had an estimated value of $350 billion.
Even though Scout uses Anthropic’s foundational AI models, Yahoo has customized the experience and combined it with proprietary Yahoo data. Running the same searches directly on Anthropic’s tools would not produce the same Yahoo Scout experience.
“When you’re serving hundreds of millions of users, you need AI that can do more than retrieve information – it has to reason, synthesize, and explain. Yahoo is building toward a more personalized, trustworthy kind of search, and Claude’s ability to deliver that quality of guidance at scale is at the heart of Yahoo Scout,” said Ami Vora, Head of Product at Anthropic.
Microsoft Bing. Microsoft Bing data is also part of Yahoo Scout. Bing provides the underlying search index, but Yahoo says the responses, ranking and overall experience are Yahoo’s. Yahoo wrote that Scout builds on its long-standing Microsoft relationship by using Microsoft Bing’s grounding API, combining that API with Yahoo’s trusted data and content ecosystem so answers are informed by authoritative sources across the open web.
Yahoo is also joining Microsoft’s Publisher Content Marketplace pilot. Microsoft says that marketplace can help support publisher revenue, and Yahoo described the move as “reflecting a shared commitment to expanding publisher reach, connecting original work with new audiences, and supporting sustainable revenue opportunities for publishers.”
Hallucinations. I asked Yahoo about hallucinations, and the company told me it has added many guardrails to reduce them as much as possible. Yahoo says its entity graph, news content and other Yahoo-specific data help ground the answers. The team believes Scout’s hallucination rate should be “very low” compared with other AI engines.
Yahoo Scout blends AI search with commerce, surfacing winter parka recommendations, affiliate shopping cards and trusted weather sources in one answer-style interface.
Agents. Many AI engines are moving toward agentic experiences that can complete tasks for users. Google, OpenAI and Microsoft are all investing heavily in this area.
Yahoo Scout already includes some agent-like elements, especially inside Yahoo Mail, where it can help add calendar events, support smart compose features and surface action items. Yahoo says more is coming on that front.
Why I care. Search is changing quickly, and I find it exciting to see Yahoo step back into the space in a meaningful way. As someone who has followed search for more than 20 years, I appreciate seeing Yahoo try to make search feel fresh again.
Seeing people such as Jim Lanzone, Eric Feng and Brian Provost work on AI search at Yahoo makes this feel like more than just another answer engine launch. I’m interested to see what Yahoo does next.
I’m excited to share that Google has taken a significant step in integrating Artificial Intelligence into publisher workflows by launching a new AI agent called Ask Ad Manager. This innovative tool leverages a Gemini-powered assistant to help us analyze performance and take action seamlessly through a user-friendly chat interface.
Google is embedding AI into publisher workflows, making it easier to analyze performance and act on insights from a chat interface.
Incorporating generative AI into Google Ad Manager, Ask Ad Manager is specifically crafted to assist publishers like myself in analyzing performance, troubleshooting issues, and navigating the Ad Manager platform effortlessly by using natural language.
The beta version is set to roll out this month, marking Google’s deeper foray into AI-supported ad operations.
What’s happening. Ask Ad Manager acts as a conversational AI agent dedicated to Google Ad Manager users who are publishers. Unlike conventional reporting tools, it allows us to pose questions in everyday language and receive tailored answers, recommendations, and reports based on our own Ad Manager data.
Google assures that this tool is engineered to help us swiftly transition from analysis to action, drastically reducing the time spent on generating reports, diagnosing issues, and navigating the Ad Manager platform.
What it can do:
Troubleshoot delivery issues. Instead of manually gathering reports to understand why certain line items are underperforming, I can now ask the AI agent questions and receive insights on the possible causes and recommended next steps.
Generate reports on demand. With a simple prompt, I can request customized metrics, benchmarks, and performance reports without the hassle of building multiple reports manually.
Navigate Ad Manager faster. Ask Ad Manager guides me to relevant pages on the platform and automatically applies suitable filters and settings rooted in the ongoing conversation.
Why we care. As a publisher managing large inventories and complex campaigns, having the capability to quickly uncover insights and diagnose issues can significantly reduce operational workloads and speed up decision-making processes.
Moreover, this feature signifies a broader trend in ad tech towards employing AI agents that not only generate information but also enhance workflows and task execution.
Looking ahead. According to Google, Ask Ad Manager marks just the start toward a future they envision as being more “agentic”, enhancing advertising operations comprehensively.
Google plans to unveil additional AI features throughout the year, incorporating developer tools like REST APIs and an MCP server aimed at supporting workflow automation and integration efforts.
They’re also working on developing specialized agents that could assist publishers and agencies in exploring inventory, negotiating deals, and executing campaigns with improved efficiency.
Bottom line.Ask Ad Manager introduces Gemini-powered assistance directly within Google Ad Manager. It offers a novel way for us publishers to access insights, resolve issues, and manage advertising operations all through natural language prompts.
In this report, I’m excited to share the findings from a research study I conducted with my team on the emerging field of Agentic Search Optimization, or ASO. We’ve developed a strategic framework that businesses and marketing agencies can leverage to stay ahead in this dynamic landscape.
What is Agentic Search Optimization?
Agentic Search Optimization, often referred to as Agentic GEO, involves optimizing your online presence so AI agents choose your products or services on behalf of users. Unlike Generative Engine Optimization (GEO), which focuses on gaining human trust after an AI recommendation, ASO targets conversions by persuading AI to recognize your offering as the best choice for users.
ASO might seem similar to GEO since both aim to drive leads or purchases, but there’s a significant difference: GEO involves human decision-making, while ASO transfers that responsibility to intelligent bots.
For instance, in ASO, a user doesn’t ask ChatGPT for the best gift card platforms. Instead, they might say, “Send $50 holiday gift cards to my remote team at their preferred stores”. The AI agent interprets, evaluates options, and makes the purchase autonomously.
So far, the ASO landscape hasn’t been thoroughly researched to identify universally accepted best practices. Our study attempts to build a framework outlining agentic search stages, determinants of company selection, and actionable tactics to influence search results.
The Study
Between March 4, 2026, and June 10, 2026, our research team conducted 2,417 agentic search commands using popular AI agents across the U.S. These commands were task delegations such as purchases, bookings, quote requests, or vendor shortlists, rather than just informational quests. We observed the entire behavior chain of agents, including sub-queries, source retrieval, candidate evaluation, and the final action or inaction.
Our analysis revealed that ASO follows three key stages: Retrieval, where AI scans the web (primarily Google) for top results and compares them to its beliefs; Evaluation, where the best company, product, or service is chosen to fit user needs; and Action, where the task is completed, often involving a transaction.
Through our research, we’ve identified three crucial insights:
Agents Review Complete Results: Across all commands, AI agents opted for the platform’s top-ranked recommendation 44.6% of the time. However, they selected options ranked 4th or lower in 38.2% of cases, demonstrating a choice based on suitability over rank.
Agents Possess Predetermined Brand Beliefs: In 81.6% of evaluations, agents relied on pre-existing brand beliefs established during their training or via web searches, indicating that brand perception heavily influences ASO.
Agents Forfeit Companies Unable to Transact: If a conversion page was machine-actionable, agents completed 78.3% of attempts. When not, completion fell drastically to 9.6% with many agents substituting transactable competitors without user input.
This study further explores the ASO process in detail, showcasing tactics that our team tested and validated in early 2026.
The Three Stages of Agentic Search
When I delegate tasks to an AI agent, it performs query interpretation, creating an average of 6.3 sub-queries. The process proceeds through three stages: Retrieval, where it constructs a result set; Evaluation, narrowing choices to the best fit; and Action, executing the conversion. During this, agents cross-reference claims with multiple sources; inaccuracies result in immediate rejection of a candidate.
To benefit from agentic search, companies must achieve two goals: securing the #1 rank on AI platforms, aiding the Retrieval stage, and clearly defining their fit, crucial for Evaluation. Technical prowess ensures seamless Action.
Stage 1: Retrieval
The Retrieval stage encompasses traditional GEO: agents scan the web and build a pool of companies or products. All previous GEO strategies apply here—Comparison blogs, metric pieces to boost rankings, and brand authority statements that AI platforms might trust help form this candidate set.
What’s innovative in ASO is understanding the AI’s pre-existing beliefs. This necessitates mapping the AI Belief Landscape, an audit scoring AI model beliefs about a brand, alongside sentences exemplifying these beliefs.
This assessment guides marketers in pinpointing areas where their brand falls short in the eyes of AI, a crucial step in adjusting perceptions during ASO.
Tactic: AI Belief Correction
AI Belief Correction involves publishing evidence to transition model beliefs from weak to strong. For instance, for a skincare brand like Rejuve, enhancing its perception involved producing detailed scientific explanations onsite and acquiring third-party verification offsite, establishing credibility.
Stage 2: Evaluation
Evaluation diverges drastically from traditional SEO. Agents, not humans, select candidates based on user knowledge. Our study showed agents broke user commands into prioritized categories: Hard Requirements, Important, Nice to Have, and Optional, with evaluations leading to a “Fit Verdict.”
Properly communicating fit information is crucial. Content detailing product suitability increases selection odds.
Tactic: Suitability Pages
Suitability Pages—criterion-specific pages that declare who a product is suited for and, critically, who it isn’t—are vital. Noting “non-fit” conditions paradoxically increases credibility by adding authenticity, improving agentic evaluation rates.
Stage 3: Action
Achieving the third stage requires technical readiness: machine-readable pages and APIs enable seamless agent transactions. The disparity in conversion rates between machine-actionable and non-actionable setups is significant, underscoring the importance of technical preparation.
The Future of Agentic Search Optimization
I anticipate that AI-driven commercial transactions will rise dramatically in the coming years. As that shift occurs, here’s what I foresee:
Suitability content will become essential: Just as landing pages are vital for SEO today, clearly defined fit will become mandatory for ASO visibility.
Tougher verification layers: Securing third-party endorsements will become even more critical, emphasizing PR’s value in ASO.
Selection share will surpass rankings: The focus will shift to actual AI agent selections over mere recommendation visibility.
Marketers excelling in GEO are already poised for agentic success, but comprehensive strategy across all stages is necessary for ultimate triumph.
Downloading This Report & Inquiries
Got questions or need a PDF copy of this report? Feel free to contact us here.
Discover more about our Agentic Search Optimization services by reaching out here.
Appendix A: Command Categories in Agentic Search Study
Category
Commands
Ecommerce purchasing
612
B2B software evaluation & signup
489
Travel booking
343
Professional services inquiries
291
Consumer & local services
274
Financial products
213
Healthcare services & products
195
Total
2,417
Appendix B: # of Commands Issued in Agentic Search Study
AI Agent
Commands Issued
Notable Behavior
ChatGPT (agent mode)
884
Most likely to verify claims against third-party sources before acting
Gemini (agentic tasks)
519
Strong integration with data feeds; likely to abandon when pages aren’t machine-actionable
Claude (browsing & computer use)
397
Thorough evaluator; applies the largest number of distinct criteria per command
Perplexity Comet
462
Widest retrieval fan-out; often selects options ranked outside top 3
Other browser agents
155
Diverse behavior observed; included for completeness
