How to Give Your AI Agent Access to Glassdoor Data

Disclaimer: This guide covers accessing publicly available data. Always review a site's robots.txt and Terms of Service before automated access.

TL;DR

To give your AI agent access to Glassdoor data, route target URLs through a managed extraction API that handles JavaScript rendering and returns structured JSON. This prevents raw HTML from bloating the context window and ensures reliable data retrieval for RAG pipelines without building custom scraping infrastructure.

Why AI agents need Glassdoor data

Agents require external knowledge to reason effectively about real-world entities. Publicly available workplace data provides critical context for several agentic workflows.

Company research pipelines: Agents compiling technical briefs on target organizations need public review metrics and benefit listings to assess company health.

Salary intelligence: RAG systems answering compensation queries require current public salary ranges across specific roles to provide accurate, grounded answers.

Culture signal monitoring: LLMs analyzing sentiment can process public interview experiences and management ratings to score organizational transparency and interview difficulty over time.

Why raw HTTP requests fail for agents

Agents using standard HTTP libraries like Python's requests encounter immediate roadblocks when targeting modern web applications. Glassdoor relies heavily on client-side JavaScript to render job listings, salary tables, and review content. A standard HTTP GET request returns an empty HTML document filled with script tags, not the actual data.

Even if an agent successfully retrieves the rendered HTML, feeding that raw markup into an LLM context window is extremely inefficient. A standard Glassdoor page contains hundreds of kilobytes of nested <div> tags, CSS classes, and navigation menus.

This raw markup wastes token limits. A 300KB HTML file consumes roughly 75,000 tokens. Sending that to a modern LLM incurs high inference costs for pure noise. Agents need the underlying signal. Failed requests break agent autonomy loops and force costly retries, degrading pipeline reliability.

Connecting your agent to Glassdoor via AlterLab

You need a translation layer between the raw web and your LLM. The Extract API docs detail how to convert unstructured web pages into strict JSON schemas. This data maps directly to Pydantic models or tool call arguments.

By defining a schema, you instruct the extraction layer to find the specific data points on the page, regardless of the underlying DOM structure.

from alterlab import AlterLabSync
import json

client = AlterLabSync("YOUR_API_KEY")

schema = {
    "company_name": "string",
    "overall_rating": "number",
    "recent_public_reviews": ["string"]
}

result = client.scrape(
    url="https://glassdoor.com/Reviews/Example-Company-Reviews.htm",
    extraction_schema=schema
)

print(json.dumps(result["filtered_content"], indent=2))

If you prefer to handle the request via the command line or integrate it into a shell-based pipeline, the same extraction can be triggered using cURL.

curl -X POST https://api.alterlab.io/api/v1/extract \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://glassdoor.com/Reviews/Example-Company-Reviews.htm",
    "schema": {
      "company_name": "string",
      "overall_rating": "number"
    }
  }'

Using the Search API for Glassdoor queries

Autonomous agents rarely start with exact URLs. They usually start with a query, such as a company name or a specific job role. You can combine a standard web search API with domain filtering to locate the exact public profile URL before extracting its contents.

Using the Search API allows your agent to find the correct entry point automatically.

from alterlab import AlterLabSync

client = AlterLabSync("YOUR_API_KEY")

search_results = client.search(
    query="site:glassdoor.com/Overview public software engineer salary Acme Corp"
)

if search_results["filtered_content"]:
    target_url = search_results["filtered_content"][0].url
    print(f"Agent found target URL: {target_url}")
    # Proceed to extraction step

MCP integration

The Model Context Protocol (MCP) standardizes how agents interact with external tools and data sources. Instead of writing custom API wrappers for every LLM, you can expose web data directly to local models or desktop applications using standardized servers.

Integrating this protocol allows coding assistants and autonomous desktop agents to query web data natively. Read the AlterLab for AI Agents guide to configure the MCP server for your specific agent environment.

Building a company research pipeline

Let us build a complete Python script that combines these concepts. This pipeline takes a company name, searches for its public profile, extracts the data into a structured schema, and prepares it for an LLM prompt.

from alterlab import AlterLabSync
import json

def research_company(company_name: str, api_key: str) -> dict:
    client = AlterLabSync(api_key)
    
    # Step 1: Find the public URL
    search_query = f"site:glassdoor.com/Overview {company_name} working at"
    search_results = client.search(query=search_query)
    
    if not search_results["filtered_content"]:
        return {"error": "Could not locate public profile."}
        
    target_url = search_results["filtered_content"][0].url
    
    # Step 2: Extract structured data
    schema = {
        "company_name": "string",
        "industry": "string",
        "employee_count": "string",
        "public_rating": "number"
    }
    
    extraction = client.scrape(url=target_url, extraction_schema=schema)
    
    # Step 3: Format for LLM context
    return {
        "source_url": target_url,
        "structured_data": extraction["filtered_content"]
    }

# Example agent tool execution
if __name__ == "__main__":
    result = research_company("Example Corp", "YOUR_API_KEY")
    print("Data ready for LLM context window:")
    print(json.dumps(result, indent=2))

This pipeline isolates the complexity of web traversal. The LLM only receives the clean JSON dictionary, keeping the context window focused entirely on the extracted facts rather than raw HTML parsing.

When operating autonomous agents at scale, error rates compound. A failed extraction step means a failed LLM inference step, driving up your total cost per task. Review the AlterLab pricing documentation to understand how costs scale with reliable request volume.

Key takeaways

Agents require structured data, not raw markup. Feeding raw HTML into a context window wastes tokens and degrades model reasoning.

Use schema-based extraction APIs to enforce strict JSON output. This guarantees your LLM receives predictable data formats for tool calls and RAG pipelines.

Combine domain-specific search queries with targeted extraction to build robust, autonomous research tools.

Read the Getting started guide to install the client library and integrate web extraction into your agent architecture.