--- url: https://ora.ai/research title: "Research: how ready is the web for agents?" description: "Eight cuts through the agent-readiness data: how scores spread, how grades cluster, which layers and categories lead, and who's already doing this best." crawled_at: 2026-10-18T19:27:04Z ask: https://ora-ai.ora.run/ask docs: https://ora-ai.ora.run/.well-known/ora/docs lang: en --- # How ready is the web for agents? 121,972 sites scanned · Average rank: 42 · 6 % rank B or above 01 / Distribution ## How scores and grades are distributed across the web The ecosystem average sits at 42 out of 100; half of all sites score below 42 , half above. On the letter ladder, Grade D alone holds 38 % of all sites - the curve is narrow and concentrated. 60 % sit at D or F, a long bottom tail; only 0.46 % reach A or above. ### Score distribution Smoothed density across 0 to 100 · lines mark median and peak n 111129 · Median 42 · Peak 42.48 ### Grades Sites per band, best to worst 02 / Trend ## How the web is getting more agent ready over time The ecosystem average is drifting up by 37 points . New protocols and wider adoption are pushing the curve higher; fresh long-tail scans moderate the pace. ### Ecosystem average Up 37 pts from first to latest scan 03 / Layers ## Which layers are sites strong on, and where do they fall short Every scan rolls up into four layers. Access leads at 44 % ; Payments trails at 5 % - a 39 -point spread. Only 0 of 5 layers clear the halfway mark: agent-readiness today is uneven, not whole. The cheap layers (metadata, discovery) run ahead while the layers that need real engineering (auth, integration) lag behind. ### Layer balance Average fill (0 to 100%) 04 / Categories ## Which industries are the most agent-ready Averaging every scan in a segment ranks industries by agent readiness, and the gap is wide. Commerce Tools leads at 61 - the segments at the top cluster around high-stakes, technical workflows (infrastructure, CRM, auth) where being legible and actionable to agents is fast becoming table stakes. At the other end, Climate & Energy trails at 30 , a 31 -point spread across the 27 categories we track: the segments at the bottom still feel little external push to open up for agents. ### Categories Mean score by segment 05 / Category detail ## Where each category is strong, and where it falls short Each industry has its own shape across the stack. Commerce Tools leads on Access ; Travel & Hospitality leads on Discovery ; Agent Tools leads on Usability . The heatmap shows how grades mix within each segment; the layer profile shows which layer is carrying the segment and which is holding it back. ### Category × grade How grades mix within each segment (each row sums to companies in that category) A+ · A · B · C · D · F · Community · 87 · 380 · 5391 · 36669 · 40668 · 23623 · Payments & Fintech · · · 4 · 28 · 209 · 192 · 177 · Healthcare · · · · · 8 · 106 · 189 · 297 · Developer Tools · 1 · 5 · 72 · 209 · 129 · 47 · Infrastructure · 1 · 4 · 47 · 127 · 83 · 49 · Marketing · · · 1 · 24 · 112 · 110 · 63 · AI & ML · · · 3 · 27 · 106 · 80 · 47 · Hardware & Robotics · · · · · 1 · 51 · 67 · 114 · E-commerce · 1 · · · 11 · 76 · 76 · 29 · Data & Analytics · · · 2 · 29 · 67 · 66 · 26 · Productivity · · · 1 · 22 · 88 · 50 · 22 · Communication & Support · · · 3 · 22 · 79 · 60 · 12 · Real Estate & Construction · · · · · 2 · 40 · 70 · 59 · Education · 1 · · · 4 · 51 · 54 · 54 · Legal & Compliance · · · · · 14 · 46 · 54 · 42 · Logistics & Supply Chain · · · · · 2 · 40 · 53 · 48 · Agent Tools · 2 · 12 · 39 · 47 · 30 · 12 · Gaming & Media · 1 · 2 · 1 · 27 · 52 · 49 · Climate & Energy · · · · · 1 · 23 · 29 · 67 · Security · · · · · 4 · 55 · 43 · 18 · Consumer · · · · · 2 · 34 · 35 · 32 · HR & Recruiting · · · · · 3 · 27 · 39 · 27 · Identity & Auth · · · · · 12 · 41 · 22 · 11 · News & Intelligence · · · · · 2 · 14 · 28 · 9 · CRM · 2 · 2 · 7 · 22 · 10 · 3 · Commerce Tools · · · · · 7 · 20 · 4 · 1 · Travel & Hospitality · · · · · · · 6 · 14 ### Layer profile by category Average fill (%) per layer within each segment 0% 50% 100% Disc. Pay 06 / Adoption ## Which protocols and checks sites are actually adopting A handful of protocols dominate; most are still rare. Each bar is the share of scanned sites that fully pass that check - llms.txt exists leads at 42 % ; at the other end, MPP payment protocol sits at just 0.039 % . ### Protocol adoption Share of scanned sites that fully pass each check 0% 50% 100% 07 / Positioning ## Where companies land on discoverability vs. actionability The quadrant plots every curated company on two axes: how easy it is for agents to find and understand the site (X), and how far it lets agents actually act - auth, integration, and in-agent UX (Y). Agent Native sites lead on both; the other three quadrants show where the gap is. ### Agent Readiness Quadrant All tagged companies - Discovery + Identity (X) vs Auth & Access + Agent Integration + UX (Y) ⌕ Agent Native - discoverable + capable Quiet Champions - capable + undiscovered Storefront - discoverable + underbuilt Off the Map - invisible + underbuilt Agent Readiness Quadrant summary | Quadrant | Domains | |---|---| | Agent Native (high discovery+identity, high auth+integration+ux) | 25 | | Quiet Champions (low discovery+identity, high auth+integration+ux) | 2 | | Storefront (high discovery+identity, low auth+integration+ux) | 184 | | Off the Map (low discovery+identity, low auth+integration+ux) | 476 | 08 / Market & Leaders ## Who is doing this best, and who isn't Of the 686 companies we curate across 16 categories, the top 10 are already shipping the fundamentals - clean discovery, an MCP endpoint, structured product data, working auth. The bottom 10 mostly predate these protocols and haven't been revisited. ### Top 10 Category and gap vs index average Company Category vs avg Pts Grd [1 Telnyx telnyx.com Infrastructure Infrastructure +58 100 A+](https://ora.ai/score/telnyx.com) [2 Primitive primitive.dev Developer Tools Developer Tools +56 98 A+](https://ora.ai/score/primitive.dev) [3 YouSpot youspot.com CRM CRM +56 98 A+](https://ora.ai/score/youspot.com) [4 ShipReal shipreal.dev Education Education +56 98 A+](https://ora.ai/score/shipreal.dev) [5 Apify apify.com Agent Tools Agent Tools +54 96 A+](https://ora.ai/score/apify.com) [6 ZenRows zenrows.com Agent Tools Agent Tools +54 96 A+](https://ora.ai/score/zenrows.com) [7 Lago getlago.com Payments & Fintech Payments & Fintech +51 93 A](https://ora.ai/score/getlago.com) [8 Fly.io fly.io Infrastructure Infrastructure +51 93 A](https://ora.ai/score/fly.io) [9 Monday.com monday.com Productivity Productivity +50 92 A](https://ora.ai/score/monday.com) [10 Supabase supabase.com Developer Tools Developer Tools +50 92 A](https://ora.ai/score/supabase.com) ### Bottom 10 Category and gap vs index average Company Category vs avg Pts Grd [10 New Balance newbalance.com Consumer Consumer -24 18 F](https://ora.ai/score/newbalance.com) [9 Trip.com trip.com Travel & Hospitality Travel & Hospitality -24 18 F](https://ora.ai/score/trip.com) [8 Claude claude.ai AI & ML AI & ML -25 17 F](https://ora.ai/score/claude.ai) [7 Associated Press apnews.com News & Intelligence News & Intelligence -26 16 F](https://ora.ai/score/apnews.com) [6 Height height.app Productivity Productivity -26 16 F](https://ora.ai/score/height.app) [5 H&M hm.com Consumer Consumer -27 15 F](https://ora.ai/score/hm.com) [4 Tome tome.app Productivity Productivity -28 14 F](https://ora.ai/score/tome.app) [3 Maven AGI maven-agi.com Communication & Support Communication & Support -30 12 F](https://ora.ai/score/maven-agi.com) [2 Uber Eats ubereats.com E-commerce E-commerce -32 10 F](https://ora.ai/score/ubereats.com) [1 Motion motion.com Productivity Productivity -32 10 F](https://ora.ai/score/motion.com) ## Where do you stand? 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