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AI Browsers, Assistants, and Agents: How They Change B2B Information Access

Andrew McPherson

Author Introduction

I help B2B leaders prepare for a world where buyers won’t browse your site themselves, AI browsers and agents will do it for them. In this article, I explain how these systems actually navigate, interpret, and act on your digital footprint, so you can design experiences that agents and not just humans can successfully buy from.

Outline

  • Three categories of AI-powered information access tools
  • Why citation replaces the traditional click for B2B brands
  • How AI browsers enhance in-page content for users
  • How AI assistants use RAG to ground responses in sources
  • How AI agents browse, verify, and act autonomously
  • Optimisation strategies for each AI tool category
  • Programmatic accessibility as a competitive differentiator
  • Common pitfalls that reduce AI visibility

Key Takeaways

  • AI tools synthesise answers rather than return link lists
  • Citation is the new click for B2B brand visibility
  • Agent readiness now matters more than crawlability alone
  • Structured data and schema markup improve RAG retrieval
  • Machine-readable feeds enable agent-verified recommendations
  • Blocking AI crawlers sacrifices future discoverability
  • Multi-turn conversational queries reward interconnected content
  • Consistent data across all surfaces builds AI trust

What Are AI Browsers, Assistants, and Agents?

AI browsers, assistants, and agents are three distinct categories of AI-powered information access tools. Unlike traditional search engines that return ranked lists of links, these tools retrieve information, synthesise answers, execute actions, and make recommendations through conversational interfaces or automated workflows. For B2B companies across industries – software, professional services, manufacturing, distribution, and service providers – understanding these three categories and optimising for their distinct retrieval patterns is essential for maintaining AI visibility and Citation Authority.

The shift from link-based search to answer-based AI tools is already reshaping how buyers research solutions. When a potential customer asks an AI assistant to recommend vendors, the AI does not return a list of websites to visit. It synthesises an answer from multiple sources and cites the ones it trusts most. Understanding how each category of AI tool works – and what it needs from your content – is the foundation of any effective AI search optimisation strategy.

The Three Categories of AI-Powered Information Access

AI Browsers: Contextual Enhancement

AI browsers are traditional web browsers enhanced with integrated AI features. Examples include Arc Browser with its AI-powered search and summarisation, Brave Browser with its Leo assistant, and mainstream browsers like Chrome, Edge, and Safari that now include AI-driven tab management, reading assistance, and contextual recommendations. These tools augment traditional browsing with real-time AI assistance without replacing the browser’s core function.

When a user highlights text on a page, the browser can summarise, translate, or explain it. When a user opens multiple tabs on a topic, the browser may synthesise information across those tabs. These browsers typically use Retrieval-Augmented Generation (RAG) to pull context from the current page, open tabs, and indexed web content. Because the browser has direct access to page HTML and structured data, well-structured content with semantic HTML, schema markup, and clear heading hierarchy improves AI browser comprehension.

AI Assistants: Retrieval-Augmented Generation

AI assistants are conversational interfaces powered by large language models (LLMs). Examples include ChatGPT, Claude, Google Gemini, Microsoft Copilot, and Perplexity. Users interact through natural language queries, and the assistant retrieves information using RAG to ground responses in external sources. These systems prioritise conversational context and multi-turn interactions over single-query searches.

When a user submits a query, the AI assistant performs several steps: it analyses the query to identify key entities and intent, retrieves relevant content from indexed sources, ranks those sources by relevance, trust, and freshness, and then synthesises a natural language response with citations. The assistant retains conversational context, allowing follow-up queries to refine or expand the initial response. This multi-stage process means that optimisation requires addressing multiple touchpoints – content must be discoverable, verifiable, and synthesisable.

AI Agents: Autonomous Interaction and Tool Use

AI agents combine search, reasoning, and action capabilities to accomplish tasks autonomously or semi-autonomously. They browse websites, call APIs, execute workflows, and synthesise information from multiple sources. Examples include ChatGPT with Advanced Data Analysis, Claude with extended context windows, and specialised research agents like Perplexity Pro that verify claims across multiple sources.

The technical mechanism underpinning agentic behaviour is tool use. LLMs now support function calling, allowing them to invoke external tools – web browsers, API clients, database queries, and code interpreters – as part of their reasoning process. This capability is documented explicitly in Anthropic’s tool use documentation, OpenAI’s function calling guide, and Google’s function calling documentation. For B2B companies, this means your website, APIs, and documentation become interactive touchpoints for AI agents – not just static content for human readers.

Why AI Browsers, Assistants, and Agents Matter for B2B

Traditional search engine optimisation assumes users click links and read web pages. AI browsers, assistants, and agents fundamentally change this assumption. Users increasingly receive synthesised answers without clicking through to sources, or rely on AI agents to autonomously navigate sites, compare options, and execute tasks. This shift creates several critical implications for B2B companies.

Citation Becomes the New Click

When an AI assistant answers a user’s question about your industry, product category, or service offering, the citation – or lack thereof – determines whether your brand gains visibility. A company absent from AI responses loses Share of Model, even if it ranks well in traditional search results. Tracking Citation Authority across AI platforms is now a core marketing measurement.

Agent Readiness Replaces Crawlability

Traditional SEO prioritises making content discoverable to search engine crawlers. AI agents require content that is not just discoverable but actionable. They need structured data they can parse, APIs they can call, and interfaces they can navigate. A website optimised only for human readers presents friction to AI agents attempting to extract specifications, compare pricing, or validate claims.

Multi-Turn Interactions Replace Single-Query Searches

AI assistants maintain conversational context across multiple queries. A user might ask, “What are the best project management tools for remote teams?”, receive a list with citations, then follow up with, “Which of those integrate with Slack?” The assistant retains context from the first query and refines its response. Companies that provide structured, interconnected information – through internal linking, schema markup, and API accessibility – perform better in these multi-turn interactions.

Programmatic Access Becomes a Competitive Differentiator

AI agents can browse trial signup flows, parse pricing pages, extract technical specifications from documentation, and validate service capabilities through API calls. Companies that make this information programmatically accessible – through feeds, APIs, and semantic HTML – enable AI agents to provide accurate, detailed responses. Companies that block agents, require logins for basic information, or present data only in PDFs or images lose citation opportunities.

The business consequences of ignoring this shift are measurable. When a potential customer asks ChatGPT which firms specialise in a particular service area, and your firm is not cited, you lose a qualified lead. When Perplexity evaluates vendors for a specific capability and cannot parse your credentials from unstructured PDFs, a competitor with machine-readable data feeds gains the citation. Optimising for AI browsers, assistants, and agents addresses the actual mechanisms these tools use to retrieve, verify, and cite information.

How Each Category Works: Technical Mechanisms

AI Browsers: Page-Level Context

AI browsers integrate LLMs directly into the browsing experience. The technical mechanism involves combining page content – HTML, structured data, and visible text – with the user’s query. The browser sends this combined context to an LLM API, receives a response, and presents it inline. Because the browser has direct access to page HTML, well-structured content with semantic HTML and schema markup significantly improves AI browser comprehension and summarisation accuracy.

AI Assistants: The RAG Pipeline

AI assistants use a multi-stage RAG pipeline. First, the LLM interprets the user’s intent and identifies key entities – company names, product categories, geographic regions. Second, the system searches indexed web content for relevant sources, prioritising pages with high semantic similarity to the query, recent modification dates, and strong trust signals such as authoritative domains and schema markup. Third, the system ranks and filters sources. Fourth, the LLM synthesises information from top-ranked sources and generates a response with citations. Companies that optimise only for discoverability without addressing verification and synthesis lose citation opportunities at later stages of the RAG process.

AI Agents: Tool Use and Autonomous Browsing

When a user asks an AI agent to find the best software for a specific use case under a certain price point, the agent may search and retrieve initial candidates, browse pricing pages to verify current pricing, check integration lists or call product APIs to confirm feature availability, read reviews on third-party platforms, and synthesise findings into a comparison with citations.

This process relies on tool use – also called function calling. LLMs like GPT-4, Claude, and Gemini support function calling that allows them to invoke external tools as part of their reasoning process. When the model needs real-time information, it calls a web search tool. When it needs to verify pricing, it calls a browser tool to navigate the pricing page. When it needs structured data, it calls an API. Anthropic’s advanced tool use capabilities now support complex multi-step workflows including dynamic tool discovery, programmatic tool calling, and code execution – moving tool use from simple function calling toward intelligent orchestration.

How to Optimise for AI Browsers, Assistants, and Agents

Optimising for AI-powered information access requires addressing three dimensions: content structure, programmatic accessibility, and agent readiness. Each category of AI tool prioritises different optimisation factors, but all benefit from core best practices.

Universal Best Practices for All AI Tools

Implement semantic HTML and schema markup. Use semantic HTML elements to provide clear document structure. Add schema markup for all key entities. Use TechArticle or Article schema for blog posts, FAQPage schema for help centres, HowTo schema for tutorials, Product schema for products, and Service schema for service offerings. Include author, datePublished, dateModified, and about fields to provide entity context.

Create a machine-readable feed declaration file. Publish an llms.txt file at your domain root listing all structured feeds and key documentation. This file acts as a roadmap for AI agents, directing them to authoritative data sources. The llms.txt specification, proposed by Jeremy Howard of Answer.AI, has gained adoption across developer and SEO communities as a standardised method for surfacing your most important content to AI systems.

Optimise for conversational query patterns. Structure content to answer natural language questions. Use H2 headings that mirror user queries: “What is [concept]?”, “How does [feature] work?”, “What are the benefits of [service]?”. This heading structure improves RAG retrieval because AI systems match user queries to heading text when selecting relevant content chunks.

Provide explicit freshness signals. Include dateModified timestamps on all content pages and structured feeds. AI systems use modification dates as trust signals, prioritising recently updated sources over stale content. Publish a changelog or “What Changed Recently” section documenting updates with specific dates.

Optimising for AI Browsers

Use clear heading hierarchy. Ensure every page has a single H1 followed by logical H2 sections and H3 subsections. AI browsers use heading structure to generate summaries and extract key points. A page with inconsistent heading hierarchy confuses AI summarisation.

Provide inline definitions for technical terms. When introducing domain-specific terminology, provide inline definitions or link to a glossary. AI browsers extract definitions to explain terms when users highlight them. For example, define RAG on first use: “Retrieval-Augmented Generation (RAG) is the mechanism LLMs use to ground responses in external sources.”

Include summary sections. Add explicit summary sections at the end of long articles. AI browsers often extract these sections to provide quick overviews, and this practice also benefits human readers scanning for main points.

Optimising for AI Assistants

Maximise external citations. Include a minimum of three citations to authoritative external sources in every article. AI systems treat well-cited content as more trustworthy than unsourced claims. Citations to official documentation, peer-reviewed research, and industry standards carry more weight than citations to generic blogs. Use inline links with descriptive anchor text rather than generic phrases like “according to this study”.

Implement author attribution. Add author bylines with schema markup. AI systems use author credentials to evaluate content trustworthiness. A byline with Person schema linking to an author bio and professional profiles signals expertise and strengthens E-E-A-T signals.

Structure content for multi-turn conversations. Anticipate follow-up queries and link related topics. If you write about AI visibility metrics, link to related articles on Citation Authority, Share of Model, and RAG optimisation. Provide clear, factual answers early in each section – AI assistants extract answers from the first few sentences of relevant sections.

Optimising for AI Agents

Publish structured data feeds. Create JSON feeds for key business data: pricing, product specifications, service capabilities, team credentials, certifications, and coverage areas. These feeds should follow schema.org conventions and include dateModified timestamps. Ensure feeds are publicly accessible with proper CORS headers. Declare these feeds in your llms.txt file so AI agents can discover them efficiently.

Make forms and interactive elements agent-accessible. Use semantic HTML for forms with descriptive labels and aria-label attributes. Avoid CAPTCHA walls on public information – use honeypot fields to block spam without blocking legitimate AI agents. Rate-limit by IP address rather than blocking user-agent strings.

Provide API documentation. If you offer APIs, publish clear documentation with example requests and responses. Use OpenAPI (Swagger) specifications to provide machine-readable API schemas. AI agents can parse OpenAPI specs to understand available endpoints, required parameters, and expected responses.

Avoid agent-blocking patterns. Do not block user-agent strings containing “GPTBot”, “Claude-Web”, “PerplexityBot”, or other AI crawler identifiers. Blocking AI crawlers is analogous to blocking Google in the early 2000s – it sacrifices future visibility for short-term control. Use IP-based rate limiting to prevent abuse while allowing legitimate crawlers.

Common Pitfalls That Reduce AI Visibility

Relying only on visual presentation. Content formatted for visual appeal – large hero images, minimal text, information in graphics – may look good to human users but provides little substance for AI extraction. Complement visual design with semantic HTML and structured data.

Requiring login for basic information. Gating pricing, product specifications, or service capabilities behind login walls blocks AI agents. Provide public access to core information while reserving authenticated access for account-specific data.

Publishing information only in PDFs or images. AI agents struggle to extract information from PDFs and images. Provide HTML alternatives or text-based versions of key documents. If you must use PDFs, ensure they contain selectable text and provide structured metadata.

Inconsistent information across channels. When your website says one thing, your pricing feed says another, and your API returns different data, AI systems flag the inconsistency and deprioritise all sources. Maintain synchronisation across all data surfaces.

Blocking AI crawlers in robots.txt. Unless you have specific legal or competitive reasons to block AI access, do not disallow AI crawlers. Blocking reduces your AI visibility to zero for those platforms.

What Changed Recently

2026-01: OpenAI expanded function calling capabilities to support more complex tool use, including multi-step web browsing and API interaction workflows (OpenAI Function Calling Guide).

2025-Q4: Anthropic released advanced tool use features for Claude including dynamic tool discovery, programmatic tool calling, and code execution, enabling agents to handle hundreds of tools efficiently (Anthropic Advanced Tool Use).

2025-Q4: Arc Browser expanded AI summarisation features to include cross-tab synthesis and contextual recommendations.

2025-Q3: The llms.txt specification gained traction as a standardised feed declaration format for AI agent discovery, with adoption by major developer platforms including Anthropic and Cloudflare.

2025-Q3: Google confirmed that Gemini prioritises sources with structured data and API accessibility when generating responses requiring technical specifications or real-time information.

2025-Q2: Brave Browser integrated its Leo AI assistant with enhanced privacy-preserving on-device processing, expanding AI browser adoption beyond early adopters.

Related Topics

AI Data Surfaces – Learn how AI systems access information through three distinct surfaces: crawled web content, feeds and APIs, and live site interactions.

RAG (Retrieval-Augmented Generation) – Explore the technical mechanism AI assistants use to ground responses in external sources, including retrieval strategies and citation logic.

llms.txt and Feed Declaration – Implement the llms.txt specification to provide AI agents with a clear roadmap to your structured data, APIs, and documentation.

Schema Markup for AI Visibility – Master the schema.org types most relevant to B2B companies, including TechArticle, Product, Service, and FAQPage schemas.

Citation Authority – Measure how frequently AI systems cite your content and track Citation Authority improvements over time.

References

1. Anthropic. (2025). Tool Use (Function Calling). https://docs.anthropic.com/en/docs/build-with-claude/tool-use – Official documentation on Claude’s tool use capabilities, including web browsing, API calls, and multi-step workflows that enable agentic behaviour.

2. OpenAI. (2025). Function Calling Guide. https://platform.openai.com/docs/guides/function-calling – Technical documentation on function calling capabilities that enable AI agents to invoke external tools, call APIs, and execute actions as part of reasoning processes.

3. Google. (2025). Function Calling with the Gemini API. https://ai.google.dev/gemini-api/docs/function-calling – Documentation on Gemini’s function calling capabilities for connecting models to external tools and APIs.

4. Schema.org. (2025). TechArticle Schema. https://schema.org/TechArticle – Specification for technical article schema markup including required and recommended properties for author, dateModified, about, and mentions fields.

5. llms.txt Specification. (2024). The llms.txt Standard. https://llmstxt.org/ – Community-developed specification proposed by Jeremy Howard of Answer.AI for declaring structured feeds and documentation endpoints in a machine-readable format.

6. Anthropic. (2025). Introducing Advanced Tool Use. https://www.anthropic.com/engineering/advanced-tool-use – Engineering blog detailing dynamic tool discovery, programmatic tool calling, and tool use examples for building sophisticated AI agent workflows.