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
, orRDFa
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
oritemprop
data.Use a schema-aware parser like
extruct
(Python) orschema-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