Deterministic discovery
Ensure bots and agents reliably find your policy, sitemap, and machine-readable references.
AgentScan runs a focused production scan for the signals agents actually use to discover and parse content. You get clear pass/fail outputs and direct implementation prompts for your team.
Ensure bots and agents reliably find your policy, sitemap, and machine-readable references.
Support response formats agents can parse predictably, including markdown negotiation.
Define how AI crawlers may access and use content with clear bot directives and signals.
Most technical audits treat AI-agent access as a side note. We built this project to make agent-readiness a first-class release concern with a clear, repeatable signal set that can be tested before and after every deployment.
AI agents rely on machine-readable policies and content paths to browse safely and summarize accurately. Missing crawl directives, weak discovery signals, or poor negotiation behavior can reduce visibility and break automated flows.
01
Run the scanner before release to establish a readiness baseline for your domain.
02
Apply generated prompts, redeploy, and rescan to confirm pass status on target checks.
03
Include scans in recurring QA to detect regressions after CMS, CDN, or routing changes.
The production profile checks six high-impact signals: robots.txt, sitemap discovery, Link headers, markdown negotiation, AI bot directives, and Content-Signal declarations.
No. AgentScan is purpose-built for AI-agent content behavior, not traditional backlink or keyword scoring. It focuses on machine-readable delivery and agent crawl policy.
Results are session-based in your browser for immediate review. You can rescan any public URL at any time.
Engineering, platform, and content teams use it before releases, migrations, and CDN changes to prevent AI-readiness regressions.