# What Is Agent Readiness? ## Summary Agent Readiness is the degree to which an API or service can be discovered, understood, and used by AI agents without human intervention. This article explains the problem, how AgentBadge measures it, and how to improve your API's agent readiness score. ## Problem Your API might be technically excellent — fast, reliable, well-documented for humans. But when an AI agent tries to use it, the agent hits a wall. The agent doesn't know your API exists. It can't find your documentation. It doesn't understand your authentication flow. It can't parse your error messages. And no amount of LLM intelligence can fix these problems — because they're not intelligence problems. They're **infrastructure problems**. This is the gap AgentBadge addresses. ## AgentBadge Relevance AgentBadge measures Agent Readiness using a passive scanner — a tool that sends HTTP requests to your public endpoints, exactly like an AI agent would. The scanner is non-intrusive: no code changes, no SDK, no agent to deploy. Each check is a binary rule with evidence — no subjective scoring, no opinions. The same URL + same ruleset version always produces the same score. AgentBadge doesn't certify; it measures. ## Key Concepts - [Agent Readiness](/agent-guide/concepts/agent-readiness) — Definition and why it matters for the agentic web - [Scoring Engine](/agent-guide/concepts/scoring) — How APIs are scored using binary rules with evidence - [Trust Badge](/agent-guide/concepts/badge) — What the badge means (trust signal, not certification) ## Capabilities - [Passive Scanner](/agent-guide/capabilities/scanner) — Non-intrusive HTTP-based scanning of public endpoints - [CLI Tool](/agent-guide/capabilities/cli) — Local scanning via npm/npx, free and private ## CLI Commands ```bash # Scan an API npx @agentbadge/cli scan https://api.example.com # Scan with verbose output npx @agentbadge/cli scan https://api.example.com --verbose # Output JSON report npx @agentbadge/cli scan https://api.example.com --format json > report.json ``` ## API Endpoints - `GET /agent-guide/` — Agent Knowledge Layer index - `GET /agent-guide/context` — Product context and what AgentBadge does - `GET /agent-guide/learn` — Step-by-step learning path - `GET /agent-guide/knowledge-map.json` — Structured graph of concepts and capabilities - `GET /agent-guide/concepts/agent-readiness` — Agent Readiness concept - `GET /agent-guide/concepts/scoring` — Scoring engine concept - `GET /agent-guide/concepts/badge` — Trust badge concept - `GET /agent-guide/capabilities/scanner` — Passive scanner capability - `GET /agent-guide/capabilities/cli` — CLI tool capability ## Recommended Actions 1. [Read the context](/agent-guide/context) — Understand what AgentBadge does 2. [Follow the learning path](/agent-guide/learn) — Step-by-step guide to make your API agent-ready 3. [Check the knowledge map](/agent-guide/knowledge-map.json) — See how concepts connect 4. Run a scan — Web UI, CLI (`npx @agentbadge/cli scan URL`), or GitHub Action 5. Review your evidence — every finding links to the HTTP response that produced it 6. Fix the gaps — add `llms.txt`, improve OpenAPI, structure your errors 7. Display your badge — show your measured Agent Readiness score ## Knowledge Map - **Nodes**: `agent-readiness` (concept), `scanner` (capability), `scoring` (concept), `badge` (concept), `cli` (capability), `ruleset` (concept) - **Edges**: - `agent-readiness` → measured by → `scanner` - `scanner` → feeds into → `scoring` - `scoring` → produces → `badge` - `scoring` → uses → `ruleset` - `cli` → invokes → `scanner` - `agent-readiness` → quantified by → `scoring` ## Full Article > **For AI agents:** See the [Agent Knowledge Layer](/agent-guide/) for machine-readable product capabilities, scanning instructions, and API access. ### The Problem: AI Agents Can't Use Your API Your API might be technically excellent — fast, reliable, well-documented for humans. But when an AI agent tries to use it, the agent hits a wall. The agent doesn't know your API exists. It can't find your documentation. It doesn't understand your authentication flow. It can't parse your error messages. And no amount of LLM intelligence can fix these problems — because they're not intelligence problems. They're **infrastructure problems**. This is the gap AgentBadge addresses. ### What Is Agent Readiness? **Agent Readiness** is the degree to which an API or service can be discovered, understood, and used by AI agents **without human intervention**. Think of it as **SEO for AI agents**. Just as SEO made websites discoverable by search engines, Agent Readiness makes APIs discoverable by AI agents. The analogy is precise: | SEO | Agent Readiness | |-----|-----------------| | Search engines find your website | AI agents find your API | | `robots.txt` tells crawlers what to index | `llms.txt` tells agents what your API does | | Sitemap helps Google discover pages | Agent card helps agents discover capabilities | | Meta tags describe page content | OpenAPI spec describes API endpoints | | Page speed affects ranking | Machine-readability affects agent usability | | Search ranking = visibility | Agent Readiness score = usability | ### How It's Measured AgentBadge measures Agent Readiness using a **passive scanner** — a tool that sends HTTP requests to your public endpoints, exactly like an AI agent would. The scanner is **non-intrusive**: - No code changes required - No SDK to install - No agent to deploy - No access to private endpoints It checks four dimensions: 1. **Discovery** — Can agents find your API? (`llms.txt`, sitemap, well-known endpoints) 2. **Documentation** — Can agents understand your API? (OpenAPI spec, structured docs) 3. **Authentication** — Can agents authenticate? (clear auth flow, API keys) 4. **Machine-readability** — Is your API response machine-parseable? (JSON, structured errors) Each check is a **binary rule** with evidence — no subjective scoring, no opinions. The same URL + same ruleset version always produces the same score. Learn more: [Scoring Engine](/agent-guide/concepts/scoring) · [Passive Scanner](/agent-guide/capabilities/scanner) ### What the Badge Means The AgentBadge trust badge displays your Agent Readiness score. It is: - **A trust signal**, not a certification — we measure, we don't certify - **Transparent** — the ruleset is open, anyone can see how scoring works - **Evidence-based** — every finding links to the HTTP response that produced it - **Dynamic** — re-scan after changes to update your score The distinction between measurement and certification matters. We don't say "your API is safe and approved." We say "your API has `llms.txt`, an OpenAPI spec, and structured error responses — here's the evidence." Learn more: [Trust Badge](/agent-guide/concepts/badge) ### How to Check Your API You can check your API's Agent Readiness in three ways: #### Web UI Go to the AgentBadge dashboard and enter your API URL. Get a full report with scores, findings, and recommendations. #### CLI ```bash npx @agentbadge/cli scan https://api.example.com ``` The CLI is free, runs locally, and produces the same results as the web scanner. #### GitHub Action ```yaml - uses: agentbadge/scan-action@v1 with: api-url: https://api.example.com ``` Integrate scanning into your CI/CD pipeline to catch regressions before deployment. Learn more: [CLI Tool](/agent-guide/capabilities/cli) ### Call to Action AI agents are becoming a major consumer of APIs. If your API isn't agent-ready, you're invisible to an entire category of users. **Run a scan today.** Get your score. Fix the gaps. Display your badge. 1. [Read the context](/agent-guide/context) — Understand what AgentBadge does 2. [Follow the learning path](/agent-guide/learn) — Step-by-step guide 3. [Check the knowledge map](/agent-guide/knowledge-map.json) — See how concepts connect 4. Run a scan — Web, CLI, or GitHub Action --- *This article is part of the AgentBadge Content Marketing Wave 1. For AI agents, the [Agent Knowledge Layer](/agent-guide/) provides machine-readable access to all concepts, capabilities, and articles.*

Relevant Engineering Capabilities

The AgentBadge team can help with what you're reading about.

AI Agent Architecture

Confidence: 0.93

Design and implementation of AI agent infrastructure, agent APIs, machine-readable interfaces, and agent tooling.

People: Paul Status: VERIFIED

GEO Optimization

Confidence: 0.85

Generative Engine Optimization — making content discoverable by AI search engines and AI agents.

People: Paul Status: VERIFIED

Related services:

AI Agent ConsultingGEO Consulting
View all capabilities →