- Visibility26
- Citations4
- Sentiment50
- Site quality87
- AI readiness7
AI assistants name Agilent Technologies in 20% of answers, which is more often than the typical company in the healthcare sector. Its overall score is 35 out of 100, ranking it 814th of 2,047 companies in the study. The company's website passes 1 of 14 technical checks for AI tool readiness.
We asked 15 unbranded questions about the healthcare market: none named Agilent Technologies. AI assistants named it in 12 of 60 answers (20.0%). The typical healthcare company is named in 3.3% of answers. By how often it is named, Agilent Technologies ranks 9 of 105 in its sector.
Every dot is a company in the study. Hover one to see it; click to open its report.
This companySame sectorEvery other companyNever named
Five parts, each scored out of 100.
How often AI assistants name the company
How often AI uses the company's own website as a source
How positively AI describes the company
How well built the website's own pages are
How easily AI tools can read and use the website
Visibility is calculated from the answers on this page. How the score is built.
105 companies in this sector. The line on each bar is the typical company.
An answer that names the company
“Typical price range for new liquid chromatography systems in the US”
Based on current market data, here are the typical price ranges for new liquid chromatography systems in the US:
Basic HPLC Systems
New HPLC systems for analysis cost between $15,000 and $70,000, with a basic HPLC system starting around $10,000, while UHPLC (ultra-high-performance liquid chromatography), which provides higher pressure and better resolution, costs 20–30% more.
System Type Variations
Analytical HPLC: Usually cost between $20,000 and $70,000
Preparative Systems: Can range from $50,000 to $150,000
UHPLC Systems: Cost from $60,000 to $200,000
High-thro …
The AI’s answer as we received it, shortened and not checked for accuracy.
How the mentions in these answers were split between this company and its rivals
Answers naming it, out of answers received
What this website publishes for an AI agent to read, use and buy from, on a scale of six levels.
- 0
- 1
- 2
- 3
- 4
- 5
Level 1: Basic web presence. Two of robots.txt, a sitemap and Link headers are in place.
- Content Signals
The website turns away requests that do not come from a browser. AI agents that say what they are meet the same refusal.
Show every check, for your technical teamHide the checks
- study: 92.0%robots.txtPassed
A valid robots.txt with crawl rules at the site root.
- study: 86.8%SitemapPassed
An XML sitemap listing the pages, ideally referenced from robots.txt.
- study: 0.3%Link headersNot met
Send Link headers such as rel="api-catalog" or rel="describedby" on the homepage.
- study: 5.5%DNS for AI DiscoveryNot present
Advertise agent endpoints with SVCB records under _agents.
- study: 1.9%Markdown negotiationNot met
Return a markdown version when asked with Accept: text/markdown, for example at the CDN.
- study: 25.1%llms.txtNot met
Publish an llms.txt with a short summary and the key links.
- study: 93.4%AI bot rulesPassed
Explicit robots.txt rules for AI crawlers such as GPTBot, ClaudeBot and PerplexityBot.
- study: 1.4%Content SignalsNot met
Add a Content-Signal line for search, ai-input and ai-train to robots.txt.
- study: 0.2%Web Bot AuthNot present
Only relevant if you run your own agents or crawlers: publish a signing key directory.
- study: 0.3%API CatalogNot met
List public APIs in a linkset at /.well-known/api-catalog.
- study: 11.8%OAuth discoveryNot met
Publish OpenID Connect or OAuth server metadata under /.well-known.
- study: 10.3%OAuth Protected ResourceNot met
Serve protected resource metadata at /.well-known/oauth-protected-resource.
- study: 0.0%auth.mdNot met
Publish an auth.md describing how agents sign in.
- study: 0.2%MCP Server CardNot met
Offer an MCP server and publish its server card under /.well-known.
- study: 0.1%A2A Agent CardNot met
Publish an agent card at /.well-known/agent-card.json.
- study: 0.2%Agent SkillsNot met
Publish a skills index with the main tasks agents can do.
- study: 33.7%WebMCPCould not check
Register key actions such as search or cart as WebMCP tools.
- study: 0.1%ARD manifestNot met
Publish an ai-catalog.json listing every agent interface.
Score 32 of 100 · 97% of checks could run · checked on October 5, 2026 · www.agilent.com · 2,047 companies scanned
1 of 14 passed. This list is for your technical team. The percentage is how many companies in the study pass each check.
These 14 checks ran during the audit and are what the AI readiness part of the overall score counts. The scan above ran later and is stricter on Link headers and Markdown, so a check can pass here and not there.
Show the 14 checksHide them
- Not metrobots.txt publishedstudy: 85.0%
- Not metXML sitemapstudy: 73.2%
- Not metHTTP Link headers (RFC 8288)study: 29.0%
- Not metMarkdown content negotiationstudy: 8.3%
- Not metllms.txt publishedstudy: 21.7%
- PassedToken budget (page weight)study: 95.2%
- Not metExplicit AI bot rulesstudy: 13.0%
- Not metAPI Catalog (.well-known/api-catalog)study: 0.2%
- Not metOAuth Authorization Server discoverystudy: 10.1%
- Not metOAuth Protected Resource discoverystudy: 9.3%
- Not metMCP Server Cardstudy: 0.0%
- Not metA2A Agent Cardstudy: 0.0%
- Not metWebMCP toolsstudy: 4.2%
- Not metAgent Skills declaredstudy: 0.0%
How many times each website was listed as a source in these answers
In the study as S&P 500.
- Common issues when running samples on capillary electrophoresis instruments and how to fix them
- Is investing in a multi-attribute method platform for quality control worth it for small biotech labs?
- Typical price range for new liquid chromatography systems in the US
- What laboratory solutions are best tailored for detecting forever chemicals in food samples?
- What can cause signal drift in gas chromatography workflows and how do you troubleshoot it?
- How does high-resolution mass spectrometry compare to ICP-MS for trace metals analysis?
- Best instruments for optimizing sample throughput in regulated laboratory environments
- Are advanced chromatography systems necessary for routine food safety testing?
- Pros and cons of automated liquid handling systems vs manual pipetting in research labs
- What are some lower-cost options for labs needing DNA sample integrity analysis?
- Recommended solutions for clinical diagnostics labs needing high-throughput sample analysis
- Alternatives to high-end spectrometry platforms for environmental sample analysis
- Which instruments streamline the workflow for molecular diagnostics in clinical labs?
- How do maintenance costs compare across major types of scientific analyzers in academic settings?
- What are the best laboratory instruments for next-generation sequencing sample quality control?
Columns, left to right: OpenAI, Claude, Perplexity, Gemini. The AI assistants often disagree. Across the study, on 40% of the questions where a company is named, only one of the four names it.
A badge of this result that you can show on your own website. Copy the code below.
<a href="https://aeo-audit.rezolve.com/us/agilent.com"><img src="https://aeo-audit.rezolve.com/us/badges/agilent.com.svg" alt="Overall score 35 of 100, US AI Visibility Study 2026" width="280" height="64"></a>