Emerging category
Agentic SEO — Optimising for
AI Agents & Autonomous Search
A new generation of AI agents is browsing the web, calling APIs, and making purchasing and research decisions — entirely without human input. Agentic SEO is the discipline of ensuring your brand, content, and data are structured so these agents can find, understand, and act on them.
Audit your agent-readiness freeDefinition
What is Agentic SEO?
Traditional SEO assumes a human will type a query into a search box, scan results, and click a link. Agentic SEO assumes something very different: an autonomous AI agent — running inside tools like Claude Desktop, AutoGPT, Perplexity Deep Research, or a custom enterprise workflow — is performing multi-step research, making tool calls, and synthesising answers with zero human intervention.
These agents decide which sources to trust, which brands to recommend, and which data to include in their output. If your brand isn't structured for agent consumption, it won't appear in agent-generated results — regardless of your traditional search rankings.
Agent-driven browsing
AI agents use headless browsers and web fetching tools to retrieve and parse your pages. They don't scroll, they don't click ads — they parse structured content, headings, and machine-readable data.
API and tool calls
Agents equipped with MCP tools or function-calling can query your APIs directly. If you expose structured data endpoints, agents can retrieve your product catalogue, pricing, or availability in real time.
Autonomous decision-making
Unlike a human searcher who might visit five sites, an agent may only visit one or two sources before synthesising its answer. Being that first credible, structured source is existential for discoverability.
Comparison
Traditional SEO vs. Agentic SEO
Traditional SEO
- Audience: human searchers
- Signal: backlinks, dwell time, click-through rate
- Format: ranking on a SERP list
- Intent: information discovery by a person
- Output: page visit and human reading
- Measurement: organic clicks & impressions
- Trust signal: domain authority & PageRank
Agentic SEO
- Audience: AI agents acting autonomously
- Signal: structured data, entity clarity, factual density
- Format: inclusion in agent-generated output
- Intent: task completion by a machine
- Output: agent recommendation or API call
- Measurement: AI mention rate & share of voice
- Trust signal: llms.txt, schema markup, entity consistency
Optimisation signals
What agents look for
Agent-readable content differs fundamentally from human-readable content. Here are the six signals that determine whether an AI agent will include your brand in its outputs.
llms.txt
A plain-text file at /llms.txt tells AI agents how to interact with your site: which pages are authoritative, what your brand does, and how agents should represent you. It is to agents what robots.txt is to crawlers.
Structured data & schema markup
JSON-LD schema (Organization, Product, FAQ, HowTo) gives agents machine-parseable facts about your brand. Agents consume schema directly without needing to parse prose, making it the single highest-leverage signal.
Entity consistency
Your brand name, description, founding date, logo URL, and social profiles should be identical across your website, Wikipedia, Wikidata, LinkedIn, and all press mentions. Inconsistency causes agents to distrust or omit you.
MCP tool exposure
Brands that publish MCP-compatible APIs can be queried directly by agents running Claude Desktop, Cursor, or any MCP host. This moves your brand from 'mentioned in output' to 'actively called during task completion'.
Factual density
Pages with concrete, citable facts — statistics, dates, named features, comparison tables — are far more likely to be quoted verbatim by agents than vague marketing prose. Write for citation, not engagement.
Crawl accessibility
Ensure AI crawlers (GPTBot, ClaudeBot, PerplexityBot, Google-Extended) are allowed in robots.txt, your pages render without client-side-only JavaScript, and your content loads within agent timeout windows.
Surfaceable
How Surfaceable makes you agent-discoverable
Surfaceable is purpose-built for the agentic web. Our audit engine checks every signal an AI agent evaluates, and our visibility tracker measures how often you appear in real AI-generated outputs.
Agent-readiness audit
- llms.txt detection and validation
- AI crawler allow/block status in robots.txt
- JSON-LD schema completeness check
- Entity consistency scan across your site
- JavaScript render compatibility test
- Structured data error detection
AI mention tracking
- Run real prompts across ChatGPT, Claude, Gemini, Perplexity
- Measure presence rate and position score
- Track share of voice vs competitors
- Scheduled daily or weekly runs
- Trend charts over time
- Per-platform breakdown
MCP server
- 16 SEO tools available as MCP tools
- Works with Claude Desktop and Cursor
- Run audits from inside any agentic workflow
- Programmatic access to your visibility data
- CLI for CI/CD integration
- API for custom dashboards