The web is being read by agents. Most sites can't be read.
Search is being replaced by answers. When someone asks ChatGPT, Claude, or Perplexity about your product, an AI agent fetches your site — and it does not see what your browser sees. It usually does not run JavaScript, it reads on a tight token budget, and it gives up in seconds. If your content sits behind a WAF challenge, a client-only render, or tens of thousands of tokens of markup, the agent quotes a competitor instead of you.
Every other tool tells you what AI says about you — a symptom you cannot act on. AgentView verifies what agents can actually read from your origin, and proves the fix. We fetch your site exactly as ClaudeBot and GPTBot do, diff it against a real browser, score ten checks across three pillars — Reach, Read, Understand — and hand you copy-paste fixes. Then we re-fetch and prove each fix went green.
It is deterministic on purpose. A verification you cannot reproduce is a vibe, not a fact. We dogfood it: AgentView scores 98 — #3 on our own public leaderboard, behind only Cloudflare and Vercel.
Where we're going
- Continuous monitoring — the standards keep moving, and every deploy can silently break what agents read. We catch it and send you the new fixes.
- An MCP server — your own coding agent writes the fixes, with your codebase context. You review, it ships.
- A GitHub PR gate — block agent-readability regressions at the pull request, before they ship and cost you citations.
Who's building it
AgentView is an early-stage product built on a simple conviction: the shift from search to answers is the biggest change to web distribution in two decades, and the sites that win will be the ones agents can actually read.
Anand Vallamsetla
Founder · Engineer in Residence at AI Fund (Andrew Ng's venture studio) · ex-Google
Anand has spent his career building software infrastructure. At Google he was a Senior Application Engineering Manager for six years, leading senior engineers across distributed platform systems and APIs serving 15,000+ users, with $500M+ in engineering ROI. At Charles Schwab he worked on the Pivotal Cloud Foundry platform — the cloud-native PaaS layer enterprise teams deploy on — and spoke publicly on the twelve-factor cloud-native principles behind it (including at the Austin Java User Group). That platform discipline now shapes how he builds AI: he treats evaluation as infrastructure, not an afterthought — designing eval-gated, guardrail-aware AI systems built for production, including a cross-model agent-interoperability framework (structured tool execution across Claude and Gemini) and Co-Dialectic, his open-source prompt-engineering plugin. He is the founder of exponentialos.io, the agentic-infrastructure layer he is building for the machine economy, and holds an MBA from UC Berkeley's Haas School of Business. He writes on shipping dependable AI — including “Building a Reliable AI Agent: 10 Architectural Decisions”. AgentView is where that reliability discipline meets the new reader on the web: the machine.
Questions or want to talk? Reach us at hello@getagentview.com.