# AI Future ReadyAgent-readable AI reference

view: Mode[] []
---
title: "Agent Readiness Audit"
type: pricing
id: "agent-readiness-audit"
description: "Draft service package for making a website, documentation set, or knowledge base easier for AI agents to discover, parse, cite, and monitor."
last_updated: "2026-04-24"
status: "draft"
price_target:
  small_site: "$500-$2,500"
  company_docs: "$5,000+"
tags:
- "pricing"
- "audit"
- "agent-ready"
- "llms.txt"
- "service"
---

# Agent Readiness Audit

The Agent Readiness Audit is a service for teams that want their site, docs, or knowledge base to work better for AI agents.

The goal is practical: make content easy to discover, fetch, parse, cite, verify, and monitor.

## Included Review

- `llms.txt` availability and usefulness.
- Raw markdown or text access.
- Sitemap coverage.
- Robots policy for AI agents.
- Structured JSON availability.
- Metadata consistency.
- Source and freshness fields.
- Canonical ids and stable URLs.
- Per-page content hashes.
- Change feed or changed-since API.
- Internal link quality.
- Mobile and JavaScript dependency risks.
- Whether important content is blocked behind rendering or scripts.

## Deliverables

- Audit report in markdown.
- Agent access score.
- Priority fix list.
- Suggested `llms.txt`.
- Suggested metadata schema.
- Suggested JSON endpoint plan.
- Sitemap and robots recommendations.
- Example agent fetch workflow.
- Optional implementation patch or handoff checklist.

## Scoring

Draft score categories:

| Category | Weight |
|----------|--------|
| Discovery | 20 |
| Raw content access | 20 |
| Structured metadata | 20 |
| Freshness and verification | 15 |
| Change tracking | 10 |
| Citation and source quality | 10 |
| Performance and accessibility | 5 |

## Package Options

### Small Site Audit

For a marketing site, docs microsite, or small content library.

- 10 to 50 pages reviewed.
- One markdown report.
- Suggested `llms.txt`.
- Priority fix list.

### Documentation Audit

For product docs, developer docs, or knowledge bases.

- 50 to 500 pages sampled.
- Content structure review.
- Metadata and API recommendations.
- Agent workflow tests.
- Implementation plan.

### Implementation Package

For teams that want the fixes applied.

- Add or improve `llms.txt`.
- Add markdown or text export.
- Add JSON index.
- Add source/freshness fields.
- Add sitemap and robots updates.
- Add basic changed-since feed where practical.

## Good Fit

- AI product companies.
- Developer tool companies.
- SaaS docs teams.
- Agencies managing client sites.
- Research groups.
- Companies whose docs are often read by AI agents.

## Poor Fit

- Sites that do not want AI access.
- Sites that cannot expose useful content outside client-side rendering.
- Teams that want hidden paid influence over recommendations.
- Projects that need legal compliance review more than technical implementation.

## First Implementation Step

Create a short intake form or contact path with:

- Site URL.
- Content type.
- Number of pages.
- Whether raw markdown exists.
- Whether an API exists.
- Whether AI crawling is allowed.
- Main agent use case.

Agent Readiness Audit

The Agent Readiness Audit is a service for teams that want their site, docs, or knowledge base to work better for AI agents.

The goal is practical: make content easy to discover, fetch, parse, cite, verify, and monitor.

Included Review

  • llms.txt availability and usefulness.
  • Raw markdown or text access.
  • Sitemap coverage.
  • Robots policy for AI agents.
  • Structured JSON availability.
  • Metadata consistency.
  • Source and freshness fields.
  • Canonical ids and stable URLs.
  • Per-page content hashes.
  • Change feed or changed-since API.
  • Internal link quality.
  • Mobile and JavaScript dependency risks.
  • Whether important content is blocked behind rendering or scripts.

Deliverables

  • Audit report in markdown.
  • Agent access score.
  • Priority fix list.
  • Suggested llms.txt.
  • Suggested metadata schema.
  • Suggested JSON endpoint plan.
  • Sitemap and robots recommendations.
  • Example agent fetch workflow.
  • Optional implementation patch or handoff checklist.

Scoring

Draft score categories:

Category Weight
Discovery 20
Raw content access 20
Structured metadata 20
Freshness and verification 15
Change tracking 10
Citation and source quality 10
Performance and accessibility 5

Package Options

Small Site Audit

For a marketing site, docs microsite, or small content library.

  • 10 to 50 pages reviewed.
  • One markdown report.
  • Suggested llms.txt.
  • Priority fix list.

Documentation Audit

For product docs, developer docs, or knowledge bases.

  • 50 to 500 pages sampled.
  • Content structure review.
  • Metadata and API recommendations.
  • Agent workflow tests.
  • Implementation plan.

Implementation Package

For teams that want the fixes applied.

  • Add or improve llms.txt.
  • Add markdown or text export.
  • Add JSON index.
  • Add source/freshness fields.
  • Add sitemap and robots updates.
  • Add basic changed-since feed where practical.

Good Fit

  • AI product companies.
  • Developer tool companies.
  • SaaS docs teams.
  • Agencies managing client sites.
  • Research groups.
  • Companies whose docs are often read by AI agents.

Poor Fit

  • Sites that do not want AI access.
  • Sites that cannot expose useful content outside client-side rendering.
  • Teams that want hidden paid influence over recommendations.
  • Projects that need legal compliance review more than technical implementation.

First Implementation Step

Create a short intake form or contact path with:

  • Site URL.
  • Content type.
  • Number of pages.
  • Whether raw markdown exists.
  • Whether an API exists.
  • Whether AI crawling is allowed.
  • Main agent use case.