Research · October 2026

The State of AI Agent Readiness: 50 Y Combinator companies

We ran 50 well-known US YC companies through the AgentReady scanner on 2026-10-08. Even the best-funded startups leave AI agents guessing.

53/100
Average readiness score
73%
Serve an llms.txt file
1 of 41
Publish agents.md or agent.json
66%
Have usable JSON-LD

Agent manifests barely exist

Only 1 of 41 sites publish agents.md and 0 publish agent.json. AI agents have no instructions for how to act on these sites.

llms.txt is catching on

30 of 41 sites (73%) already serve llms.txt — developer tools lead adoption. Laggards are falling behind fast.

Some sites shut agents out

9 of 50 sites rejected automated visits entirely, and 4 more block named AI crawlers in robots.txt.

Top scorers

  1. #1
    Resend
    79/100
  2. #2
    Airbyte
    79/100
  3. #3
    Supabase
    71/100

Full leaderboard

#CompanyCategoryllms.txtJSON-LDScore
1Resend
resend.com
Dev toolsYesYes79
2Airbyte
airbyte.com
Dev toolsYesYes79
3Supabase
supabase.com
Dev toolsYesYes71
4Langfuse
langfuse.com
AIYesNo68
5Lattice
lattice.com
SaaSYesYes64
6Heroku
heroku.com
Dev toolsYesYes64
7Vercel
vercel.com
Dev toolsYesYes64
8Vapi
vapi.ai
AIYesYes64
9Dropbox
dropbox.com
SaaSYesYes64
10PagerDuty
pagerduty.com
Dev toolsYesYes64
11Rippling
rippling.com
SaaSYesYes64
12Replit
replit.com
Dev toolsYesYes61
13Retool
retool.com
Dev toolsYesYes61
14Modern Treasury
moderntreasury.com
FintechYesYes61
15Webflow
webflow.com
SaaSYesNo57
16Browserbase
browserbase.com
AIYesNo57
17Mercury
mercury.com
FintechNoYes57
18Ramp
ramp.com
FintechNoYes55
19Mintlify
mintlify.com
Dev toolsYesNo54
20Algolia
algolia.com
Dev toolsYesYes54
21Hex
hex.tech
AIYesYes54
22Zapier
zapier.com
SaaSYesYes54
23Brex
brex.com
FintechYesYes54
24Deel
deel.com
SaaSYesNo50
25Docker
docker.com
Dev toolsNoYes50
26Weights & Biases
wandb.ai
AINoYes50
27Front
front.com
SaaSYesNo50
28Helicone
helicone.ai
AIYesNo50
29Amplitude
amplitude.com
SaaSYesYes50
30PostHog
posthog.com
Dev toolsYesNo46
31Loom
loom.com
SaaSYesNo46
32Hasura
hasura.io
Dev toolsNoYes46
33Checkr
checkr.com
SaaSNoYes46
34Sentry
sentry.io
Dev toolsYesYes45
35Coinbase
coinbase.com
Fintech——
Blocked scan
36Mixpanel
mixpanel.com
SaaSNoNo43
37Cal.com
cal.com
SaaSYesNo39
38Stripe
stripe.com
FintechYesNo39
39Scale AI
scale.com
AINoNo36
40Airbnb
airbnb.com
ConsumerNoYes27
41Twitch
twitch.tv
ConsumerNoYes25
42OpenAI
openai.com
AI——
Blocked scan
43DoorDash
doordash.com
Consumer——
Blocked scan
44Cognition
cognition.ai
AI——
Blocked scan
45Segment
segment.com
SaaS——
Blocked scan
46Faire
faire.com
Consumer——
Blocked scan
47Gusto
gusto.com
Fintech——
Blocked scan
48GitLab
gitlab.com
Dev tools——
Blocked scan
49Instacart
instacart.com
ConsumerNoNo5
50Reddit
reddit.com
Consumer——
Blocked scan

Methodology: one automated scan per homepage using the AgentReady 10-check method. "Blocked scan" means the site refused automated requests; these sites are excluded from averages. Scores reflect a single point in time.

Analysis

Why blocking scanners is an outdated security measure

9 of the 50 companies we tested (18%) refused our scanner outright. The scanner never saw a single page. That raises a bigger question: who else is getting turned away?

What "Blocked scan" means

When a site shows "Blocked scan", our request got an access-denied response (usually HTTP 403), a bot challenge page, or a JavaScript puzzle in place of the real site. This is the job of web application firewalls and bot-management tools. They were built to stop scrapers, credential stuffing, DDoS traffic and price harvesting, so they treat any automated visitor that isn't a browser as suspicious.

That made sense when nearly all useful automated traffic came from a handful of search engines. In 2026 it no longer holds. AI assistants like ChatGPT Search, Perplexity, Claude and Gemini read live pages to answer buyers' questions. Coding copilots read docs, and shopping and booking agents act for real customers. A blanket block treats all of them like attackers.

Why it's outdated

  • You risk reduced visibility in AI answers. If an assistant can't read your pricing or docs, it may rely on older sources or cite a competitor it can read. Blocking one scanner does not prove that all AI assistants are blocked.
  • Agents can't act for your customers. A buyer's agent that hits a challenge wall can't compare your plans, check whether you integrate with their tools, or start a signup.
  • It doesn't stop determined scrapers. Serious scrapers rotate residential IPs and use headless browsers to get past challenges. Blanket rules mostly block the well-behaved bots that announce who they are.
  • Public content needs proportionate protection. Marketing pages, pricing and docs are intended to be read. They still face scraping, abuse and availability risks; caching, selective access and rate limits can protect them without blanket exclusion.

How to restructure: selective agent access

The goal isn't to open the doors to every bot. It's to split your site into zones and give each one the right level of protection.

  1. 1

    Open a dedicated machine-readable layer

    Publish /llms.txt, /agents.md, /.well-known/agent.json, /robots.txt and /sitemap.xml as static, cacheable files. Add a firewall rule that skips bot challenges for these exact paths only. They contain only what you choose to publish, run no database queries and are served from the CDN cache. They add almost no attack surface.

  2. 2

    Allow verified AI crawlers instead of all bots

    Use your firewall's verified-bot list, or check reverse DNS and published IP ranges, to allow named crawlers such as OAI-SearchBot and PerplexityBot on public pages, where the provider offers a documented verification method. Do not trust user-agent strings alone. Google-Extended is a robots.txt control token, not a separate crawler identity or firewall allowlist entry.

  3. 3

    Separate public content from the application

    Treat the marketing site, pricing and docs as a public zone. Keep strict bot protection, login walls and CAPTCHAs on the app itself: dashboards, account pages, checkout, signup forms and private APIs. Ideally put them on separate hosts or paths (for example app. vs the main domain) so the rules stay simple.

  4. 4

    Rate-limit instead of hard-blocking

    On public pages, swap the blanket 403 for per-client rate limits and gradual slowdowns. A legitimate agent reading a few pages is never interrupted. A scraper pulling thousands of URLs a minute gets throttled and then blocked.

  5. 5

    Serve real HTML, not an empty shell

    Make sure key pages render their content on the server and include JSON-LD structured data, so agents get the facts without having to run heavy JavaScript.

  6. 6

    Control use with policy, not just firewalls

    Use robots.txt to state which AI crawlers may use which sections, for example allowing live AI search while opting out of model training. Monitor bot traffic in your logs and review the allowlist every quarter.

Example firewall rule logic

IF path IN (/llms.txt, /agents.md, /.well-known/agent.json,
            /robots.txt, /sitemap.xml)          → allow, cache
ELSE IF verified_bot AND zone = public       → allow, rate-limit
ELSE IF zone = app / login / checkout / api  → full bot protection
ELSE                                         → managed challenge

The bottom line

Security and discoverability can coexist. This benchmark measures discoverability signals, not security quality, and cannot establish which companies are more secure. Keep your firewall, preserve authentication and authorization, and give verified agents a controlled front door to intentionally public information.

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We check 10 signals, including whether robots.txt blocks GPTBot, ClaudeBot or PerplexityBot. See the methodology.