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Discoverability

How Agents Discover Brands

Agents find brands through training data, search engines, and direct site reading. Understanding all three is the foundation of agent discoverability.

Gartner projects search engine volume will drop 25% by 2026 as agents handle more queries<sup>[1](https://www.gartner.com/en/newsroom/press-releases/2024-02-19-gartner-predicts-search-engine-volume-will-drop-25-percent-by-2026-due-to-ai-chatbots-and-other-virtual-agents)</sup>. When your brand is not visible to those agents, you lose that traffic entirely. There is no "page two" in a conversational answer. You are either in the response or you do not exist.

Three Channels, Not One

Agents discover brands through exactly three channels: training data, live search, and direct site reading. Most teams only think about one, but you need all three working together.

Training data is the knowledge baked into a model during pretraining. Large language models train on hundreds of billions of tokens of web text[2], so if your brand had a strong web presence before the training cutoff, the model already knows you. Brands that launched recently, or whose key pages sat behind login walls or JavaScript rendering, may be invisible at the model layer entirely. You cannot update training data after the fact, so this channel rewards years of consistent, crawlable web presence.

Live search is where most agents get current information. ChatGPT, Perplexity, and Copilot all call search APIs at inference time to supplement their training knowledge[3]. That means your organic search ranking still matters. Brands ranking in the top three for a query are far more likely to be cited than those on page two, which gives every dollar you have spent on SEO a second payoff: agent visibility.

Direct site reading is the newest and most controllable channel. Agent crawlers like GPTBot, ClaudeBot, and PerplexityBot visit your pages directly to extract content[4]. To serve them well, your site needs:

  • Fast server responses (under 500ms)
  • Clean HTML without JavaScript dependencies
  • Proper structured data
  • An llms.txt file that tells crawlers what matters

This is the channel where you have the most control and where most competitors are doing nothing.

Where to Focus First

Training data is out of your hands until the next model version, and search rankings take months to move. Direct site readability, however, can be fixed in a week. Add structured data, publish an llms.txt file, ensure your pages render without JavaScript, and confirm you have not blocked agent crawlers in robots.txt. These changes are low effort with immediate impact.

How Scanner Helps

Scanner audits all three channels. It checks whether agent crawlers can access your pages, whether your structured data is valid, whether your content is extractable, and whether your pages load fast enough for real-time retrieval. Run an audit on your top 20 pages and you will know exactly where agents are losing you.

Sources

  1. 1.Gartner: Search volume to drop 25% by 2026
  2. 2.Brown et al.: Language Models are Few-Shot Learners
  3. 3.OpenAI: ChatGPT Plugins
  4. 4.OpenAI: GPTBot

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System Status

Agent Discovery Channels

Web CorpusPre-trained knowledge
Brand
Web Corpus
Pre-trained knowledge
Agent
Brand
Mention
Training Data
Live SearchReal-time retrieval
Brand
Live Search
Real-time retrieval
Agent
Brand
Mention
Search Engines
Your WebsiteCrawl & extract
Brand
Your Website
Crawl & extract
Agent
Brand
Mention
Direct Site Reading

Tap or hover each lane to highlight. AI agents discover brands through three distinct channels, each with different optimization strategies.