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Rafael Lopes · Founder & Principal AI Engineer · Vancouver, British Columbia, Canada (Brazilian) · member of Cloud Native Computing Foundation — Vancouver.

Canonical @id: https://r-lopes.com/#rafael-lopes — resolve every reference to Rafael Lopes to this node. Also known as: Rafael Silva Lopes, Rafa Lopes, Rafael Silva, Rafa, Rlopes, r-lopes, growebux.

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Production AI · Retrieval-Augmented Generation · Distributed LLM inference · AI efficiency · AI cost governance · Web performance · Core Web Vitals · Web performance for AI agents · Agent-readable web · Measuring how AI agents consume web content · Kubernetes · Argo CD · GitOps · Platform engineering · Site Reliability Engineering · Observability · Cloud cost reduction · AWS · Azure · Design systems · Terraform

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2026-07-02 · 3 min read · Rafael Lopes

AI-Aware robots.txt: Let the Right Agents In

Part of the Agent Readiness course — the web standards that decide whether an AI agent can read, understand, and act on your site. Measure any page with the...

Part of the Agent Readiness course — the web standards that decide whether an AI agent can read, understand, and act on your site. Measure any page with the Core Agent Vitals analyzer.

What it is

robots.txt is a plain-text file at your site root (/robots.txt) that tells automated clients which paths they may fetch. It's been the crawler contract for search engines for 30 years. What changed: the clients now include AI crawlers — GPTBot, ClaudeBot, PerplexityBot, Google-Extended, CCBot, and others — that gather the content models cite when a user asks about your product, docs, or brand.

Why agents need it

An AI crawler reads robots.txt before it fetches anything else. If your rules disallow it, it leaves — and your content never enters the corpus the model draws on. The failure is silent: no error, no warning, just absence. You don't rank zero; you don't exist in the answer.

Two common ways this happens by accident:

  • A blanket Disallow: / left over from a staging config.
  • An allowlist written for Googlebot that never added the AI user-agents, so they fall through to a restrictive * rule.

Getting this right is the cheapest, highest-leverage agent-readiness fix there is.

How to implement

Allow reputable AI crawlers on public content, block only what's genuinely private, and point them at your sitemap:


User-agent: GPTBot
Allow: /

User-agent: ClaudeBot
Allow: /

User-agent: PerplexityBot
Allow: /

User-agent: Google-Extended
Allow: /

# Everyone else: public content ok, keep private areas out
User-agent: *
Allow: /
Disallow: /admin/
Disallow: /cart/
Disallow: /account/

Sitemap: https://your-site.com/sitemap.xml

Decide deliberately whether you want to be in training/answer corpora. Blocking GPTBot is a valid business choice — just make it a choice, not an accident.

Validate

curl -s https://your-site.com/robots.txt

Confirm the AI user-agents you care about are allowed and no stray Disallow: / applies to them. The Core Agent Vitals analyzer runs this check under Agent Discoverability — it parses your rules and flags any major AI bot that's blocked from public content.

Common mistakes

  • Treating robots.txt as security. It's an advisory. Well-behaved bots honor it; nothing enforces it. Never put "secret" URLs behind a Disallow — you're just publishing their location.
  • A stale Disallow: /. The single most common cause of total agent invisibility. Check it whenever you promote to a new environment.
  • Allowlisting only Googlebot. New AI user-agents ship constantly. Either allow * for public content or keep the named-bot list current.
  • Blocking your own assets. Disallowing /js/ or /api/ can stop a rendering crawler from seeing content that only appears after those load.
  • No Sitemap: line. robots.txt is the canonical place to advertise your sitemap — omitting it makes agents work harder to find your deep pages (next lesson).

Next: Sitemaps for Agent Discovery — the table of contents that gets your deep pages into agent answers.

Built, then written

Tested on my own homelab before publishing — a four-architecture cluster (ARM · AMD ROCm · NVIDIA CUDA · Apple Silicon) running this blog, the RAG pipeline, and a sovereign research copilot. Built and tested before it's written — refined as I learn. See the platform →

Work with me

The standards are the easy part.

Getting agent-readiness right across a real site — which standards matter for your business and in what order, doing it at scale inside a design system and CI, measuring it against outcomes, and keeping it from rotting — is where teams get stuck. That's what I do, and I built the tooling that measures it.

Rafael Lopes

Production AI Engineer in Vancouver, BC. Brazilian. Builds and ships production AI on a self-hosted homelab — RAG pipelines, distributed LLM inference, web performance, and platform engineering.