You are helping me build the following AI agent workflow.

## Goal
Webpage Scraper and RAG Knowledge Base: Ingests web pages via URL into a vector store and answers questions using retrieved context with reranking.

## Specification
- What it does: You can instantly turn any webpage into a searchable knowledge base by sending its URL via webhook. This agent scrapes the content with Firecrawl, stores it in Pinecone, and answers your questions using high-quality context retrieved and reranked by Cohere before generating responses.
- Trigger: Triggered by an event (Event · on webhook URL submission)
- Autonomy: Fully hands-off
- Expected setup effort: a few focused days
- Tools/services involved:
  - Firecrawl: Scraping web pages into markdown
  - OpenAI Embeddings: Generating vector embeddings from content
  - Pinecone Vector Store: Storing and retrieving vector embeddings
  - OpenRouter Chat Model: Answering questions via RAG agent
  - Cohere Reranker: Improving retrieval quality for answers

## Known pitfalls, handle each one explicitly in your implementation
1. OpenRouter model selection affects response quality and cost, so monitor usage metrics and switch models based on performance needs.

## Reference implementation
https://n8n.io/workflows/13964 (workflow template)
Fetch and inspect this before building. If it matches my stack, adapt it;
if not, rebuild the pattern with my tools.

## Process requirements
1. Before building: ask me which of the listed tools I actually use and
   what my platform is (n8n / Make / code / other). Do not assume.
2. Adapt the pattern to my answers; do not force the reference stack.
3. Address every pitfall above; tell me how you handled each.
4. Provide a test plan I can run before letting this touch real data.
5. Ask before any step that sends messages, modifies data, or spends money.

Source: https://usecasesforagents.com/use-case/webpage-scraper-and-rag-knowledge-base/ via usecasesforagents.com