How to make an AI agent contextually aware while operating on a specific website

To make an AI agent contextually aware while operating on a specific website, the core challenge is ensuring it has access to, understands, and leverages structured and unstructured data from the site in real time. Below is a step-by-step technical strategy to accomplish this:

1. DOM & Page State Monitoring

Goal: Allow the agent to "see" what the user sees.

Approach:

  • Inject a JavaScript agent into the site (browser extension, script tag, or embedded widget).

  • Use the MutationObserver API to track DOM changes.

  • Capture elements like:

    • document.title, window.location.href

    • meta tags (for SEO/context)

    • Content inside <main>, <article>, <section>, etc.

    • Product or article schemas via JSON-LD, Microdata, or RDFa

2. Extract Structured Context (Schema.org, Microdata, JSON-LD)

Goal: Get machine-readable semantic data from the site.

Approach:

  • Parse all embedded application/ld+json or itemprop data.

  • Use a schema-aware parser like extruct (Python) or schema-dts (JavaScript/TypeScript).

  • Normalize the extracted data into a knowledge object:

{
  "page_type": "Product",
  "name": "Nike Air Max 90",
  "price": "139.99",
  "category": "Shoes",
  "brand": "Nike",
  "availability": "InStock"
}

3. Inject Context into the Prompt

Goal: Dynamically enrich prompts to the LLM.

Approach:

  • Construct contextual prompts like..

  • Optionally use contextual memory with embeddings (e.g., via vector search using a local DB or Pinecone/Weaviate/FAISS).

You are an AI assistant on the product page for "Nike Air Max 90". The current price is $139.99, and the product is in stock. The user is browsing the "Men's Shoes" category.

User asks: "Do you have this in blue?"

4. Capture User State + Session History

Goal: Understand past interactions for continuity.

Approach:

  • Track scroll depth, clicked items, selected filters, inputs typed.

  • Store in sessionStorage, and transmit to backend or in-prompt memory.

  • Example:

    json

    CopyEdit

    { "recent_clicks": ["Running Shoes", "Size 10", "Blue"], "filters_applied": ["Color: Blue", "Size: 10"] }

{
  "recent_clicks": ["Running Shoes", "Size 10", "Blue"],
  "filters_applied": ["Color: Blue", "Size: 10"]
}

5. Contextual RAG (Retrieval-Augmented Generation)

Goal: Supplement LLM output with relevant knowledge base retrieval.

Approach:

  • Build a page-specific vector index of:

    • Product descriptions

    • Support docs

    • FAQs

    • Reviews

  • Use tools like:

    • LangChain + FAISS

    • LlamaIndex (for local indexing)

  • Retrieve top-k relevant chunks and inject them into LLM prompt for grounded responses.

6. Agent Architecture

Goal: Modular pipeline where each agent handles a context-processing task.

[DOM Agent] → [Schema Agent] → [Session Agent] → [Embedding + RAG Agent] → [Prompt Generator] → [LLM Inference Engine]

Each component feeds context into the next, ensuring complete contextual awareness.

7. Deployment Modalities

Options:

  • As a browser extension (ideal for agent assistive browsing)

  • Embedded script/widget on site (React/Vue component)

  • Custom LLM endpoint with context-aware middleware

8. Privacy & Security

Important: Follow data privacy guidelines (e.g., GDPR):

  • Anonymize session data

  • Do not track personal data unless explicitly consented

  • Offer opt-in for chat functionality

AI AgentFrancesca Tabor