AI Agent Search Optimization

Overview

Optimize for AI search by improving retrieval eligibility, entity clarity, answer usefulness, and citation confidence. Do not promise rankings or guaranteed AI citations; produce testable technical fixes, content briefs, and measurement plans.

Quick Start

1. Identify the target site, market, brand/entity, competitors, and AI surfaces the user cares about.
2. Run a local technical audit when a URL is available:

python ai-agent-search-optimization/scripts/audit_site.py https://example.com --brand "Example" --markdown

1. If the site lacks /llms.txt, generate a high-signal draft:

python ai-agent-search-optimization/scripts/build_llms_txt.py --site https://example.com --name "Example" --summary "One sentence positioning." --from-sitemap --output llms.txt

1. Generate a prompt matrix for manual or automated visibility tracking:

python ai-agent-search-optimization/scripts/prompt_matrix.py --brand "Example" --category "customer support software" --competitors "Zendesk,Intercom" --output prompts.csv

1. Produce a prioritized plan with: issue, affected URL/template, AI-search impact, fix, owner, effort, validation method, and confidence.

Workflow

Use the SEARCH stack:

• Surface access: Verify robots.txt, CDN/WAF behavior, sitemap discovery, status codes, canonicalization, noindex/nosnippet, and text availability for major search and AI crawlers.
• Entity clarity: Make the brand, product, category, locations, founders, authors, credentials, sameAs links, and organization schema unambiguous.
• Answer coverage: Map query fan-out topics to pages that answer concrete comparison, pricing, best-for, how-to, troubleshooting, and local intent.
• Retrieval structure: Improve internal links, hubs, canonical pages, structured data that matches visible content, and optional LLM-friendly resources such as /llms.txt.
• Citation proof: Add first-party evidence, original data, dates, methodology, author expertise, case studies, reviews, policies, and quotable definitions.
• Human outcomes: Track AI mentions/citations alongside organic traffic, assisted conversions, signup quality, and high-intent referral behavior.

Decision Rules

• If the user asks for Google AI Overviews or AI Mode, treat it as search optimization first. Google says there are no extra technical requirements beyond Search eligibility, snippet eligibility, crawlability, and helpful content.
• If the user asks for ChatGPT Search, check whether OAI-SearchBot is allowed separately from GPTBot. Allowing search crawling does not require allowing model-training crawling.
• If the user asks for Perplexity, check PerplexityBot for search indexing and explain that Perplexity-User is for user-triggered fetches.
• If the user asks for /llms.txt, create it as a curated map for models and agents, not as a ranking guarantee. Keep it concise, current, and linked to authoritative pages.
• If the user asks to "game" AI systems, redirect toward legitimate crawlability, evidence, source quality, content usefulness, and measurement.
• If current platform behavior matters, verify official crawler/search docs before making strong claims.

Deliverables

Prefer these outputs:

• AI visibility audit: technical blockers, entity/citation gaps, content coverage, priority fixes.
• Implementation patch: robots.txt, sitemap references, llms.txt, schema updates, copy changes, internal links, template fixes.
• Content brief pack: pages to create or improve, target prompts/intents, required evidence, schema, internal links, and validation checks.
• Prompt test suite: prompts by intent class, competitor comparison prompts, expected source/citation checks, and scoring rubric.
• Measurement plan: surfaces, prompt sampling cadence, mention/citation/sentiment metrics, and analytics tie-ins.

Bundled Resources

• Read references/ai-search-principles.md when platform-specific guidance, caveats, or official source URLs matter.
• Read references/audit-framework.md when planning a full audit or explaining prioritization.
• Read references/deliverable-templates.md when producing reports, tickets, briefs, or measurement scorecards.
• Use scripts/audit_site.py for a dependency-free crawlability and AI-readiness audit of a URL.
• Use scripts/build_llms_txt.py to generate a first-pass /llms.txt file from site metadata, links, and optionally a sitemap.
• Use scripts/prompt_matrix.py to generate AI visibility prompts for repeated manual or automated testing.

Quality Bar

• Separate confirmed facts from hypotheses.
• Tie every recommendation to a likely AI retrieval, grounding, citation, or conversion effect.
• Prefer source-backed primary content over synthetic summaries.
• Avoid fake reviews, spam mentions, hidden text, cloaking, or AI-only pages that harm users.
• Make the final plan shippable: exact files, URLs, snippets, schema objects, and validation steps.