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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

agents.json: Declaring What Your Site Can Do

Part of the Agent Readiness course. Measure any page with the Core Agent Vitals analyzer. This is an emerging standard — early, optional, and worth watching....

Part of the Agent Readiness course. Measure any page with the Core Agent Vitals analyzer. This is an emerging standard — early, optional, and worth watching.

What it is

agents.json is a manifest — published at /agents.json or /.well-known/agents.json — that declares the capabilities your site exposes to agents: named flows ("search products", "create booking"), the API operations each flow uses, and the parameters an agent must supply. Where OpenAPI describes individual endpoints, agents.json describes tasks — the sequences that get something done.

Why agents need it

An agent reading your OpenAPI spec knows how to call each endpoint, but not which endpoints combine to accomplish a goal, or that a goal is even offered. agents.json closes that gap: it's a discovery layer that says "this site can do X, Y, Z, and here's the flow for each." That turns your site from a collection of endpoints into a set of advertised services an agent can plan against.

It's genuinely early — support is thin and the spec is evolving. But it's cheap to publish, and early adopters get native integration as agent frameworks add support. Treat it as a low-cost bet, not a requirement.

How to implement

Start minimal: declare one real flow that wraps operations you already have in OpenAPI.

{
  "agentsJson": "0.1.0",
  "info": { "title": "Acme", "version": "1.0.0" },
  "flows": [
    {
      "id": "search-products",
      "title": "Search products",
      "description": "Find products by keyword and return name, price, and availability.",
      "actions": [
        { "operationId": "searchProducts", "sourceUrl": "https://api.acme.com/openapi.json" }
      ]
    }
  ]
}

Reference your existing OpenAPI operations by operationId so there's one source of truth for the API mechanics and agents.json only adds the task layer.

Validate

curl -s https://your-site.com/agents.json | head

Confirm valid JSON with a populated flows array. The Core Agent Vitals analyzer checks /agents.json and /.well-known/agents.json and marks it present — it's scored as an emerging/optional signal, so its absence is informational, not a failure.

Common mistakes

  • Declaring flows you don't actually support. An agent will try them. Only advertise capabilities that work end to end.
  • Duplicating your OpenAPI instead of referencing it. Inline copies drift. Point actions at operationIds in your published spec.
  • Publishing an empty manifest. A flows: [] file adds nothing. Ship it when you have at least one real flow.
  • Treating it as mandatory. It isn't yet. Do the foundational lessons (robots, sitemap, JSON-LD, llms.txt, OpenAPI) first — they pay off today.

Next: WebMCP — letting agents call your actions directly.

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.