Avoid AI-agent site amnesia — precompute & cache website knowledge

Autonomous AI agents often suffer from "site amnesia"—they lack a reliable, queryable snapshot of a site's rendered DOM, dynamic content, and user flows, which forces repeated page navigations, flaky automations, and excess LLM calls. This matters most for digital-first companies that run repeatable agent workflows across hundreds to thousands of pages; using the provided TAM (5,000,000 addressable firms at an illustrative $12,000 ACV) puts the market opportunity at roughly $60B. You could build a developer-focused platform that precomputes and caches site knowledge: periodic headless-rendered DOM snapshots, rendered text and structured metadata, embeddings, incremental change detection, and a compact vector+document store with APIs and connectors into RAG layers and agent runtimes. That product reduces redundant browsing, cuts latency and token usage for retrieval-first agents, and leverages current enablers—autonomous-agent adoption, RAG as standard practice, and mature Playwright/Puppeteer cloud runners. The timing is favorable because enterprises are actively deploying agent-based automation and need deterministic, low-latency context (market score 95/100, revenue potential 88/100), while competition remains medium. To win you must solve hard operational challenges—scaling capture cost, managing freshness and storage, and meeting security/compliance (VPC/on‑prem options)—but those same engineering investments create defensibility through integrations, SLAs, and measurable ROI for customers.