AI agents that browse the web autonomously need a continuous stream of categorized URL intelligence to know which domains they can visit and which ones are off-limits. Our pre-categorized URL data feeds integrate directly with your access management layer, giving every agent a live, structured map of the internet organized by IAB taxonomy, page types, and reputation signals.
Traditional identity and access management systems were designed for human users logging into known applications. Autonomous AI agents break every assumption those systems were built on.
When an enterprise deploys an AI agent to perform web research, competitive analysis, or procurement workflows, that agent interacts with hundreds of external domains per session. Traditional IAM systems can authenticate the agent and authorize its role, but they have zero visibility into the quality, safety, or category of the external websites the agent visits. The access management system sees "Agent-007 made an outbound HTTP request" but cannot tell whether the destination was a public news site, a competitor's login portal, or a phishing domain spoofing a vendor.
Our categorized URL data feed delivers 102 million domains with full IAB taxonomy classifications, web filtering categories, page-type labels, reputation scores, and popularity rankings. This feed plugs directly into your access management pipeline as a policy decision point. Before an agent navigates to any URL, your access management system queries the feed, retrieves the domain's category and page type, evaluates it against the agent's authorized scope, and returns an allow or deny decision.
The feed ships as a downloadable database file (CSV or JSON) for local deployment, and is supplemented by a real-time API for domains not yet in the feed. Updates are available quarterly, ensuring your category data stays current with the evolving web. The result is a deterministic, sub-millisecond access control layer that operates without model inference, without external API latency, and without the non-determinism of LLM-based URL evaluation.
Three integration architectures that transform raw URL feeds into agent-aware access control
Download the full 102M domain feed and load it into your data infrastructure. Import into Redis for sub-millisecond lookups, PostgreSQL for complex policy queries, or Elasticsearch for full-text category search. Your access management system queries the local store on every agent navigation event, ensuring zero external dependencies in the decision path.
Stream URL categorization events into your access management pipeline using webhooks or message queues. When an agent requests navigation, the event triggers a feed lookup, the category data enriches the access request, and the policy engine evaluates the enriched request against the agent's authorized scope. The entire flow completes in under 5ms.
Segment the URL feed by category and assign feed segments to agent roles. A financial research agent receives the Business and Finance category subset. A marketing agent receives Shopping, Advertising, and Entertainment subsets. Each agent's local feed contains only the domains it is authorized to visit, reducing lookup table size and enforcing least-privilege access by design.
Production-ready snippets to connect categorized URL feeds to your agent access layer
import json
import redis
class FeedBasedAccessController:
"""Controls agent access using pre-categorized URL feed data."""
def __init__(self, redis_host="localhost", redis_port=6379):
self.store = redis.Redis(host=redis_host, port=redis_port)
def load_feed(self, feed_path):
"""Ingest categorized URL feed into Redis for fast lookup."""
with open(feed_path, "r") as f:
for line in f:
entry = json.loads(line)
domain = entry["domain"]
self.store.hset(f"feed:{domain}", mapping={
"iab_category": entry["iab_category"],
"page_type": entry.get("page_type", "unknown"),
"web_filter": entry.get("web_filter", "uncategorized"),
"reputation": str(entry.get("reputation", 0))
})
def check_access(self, agent_role, target_url):
"""Evaluate agent access against feed data and role policy."""
from urllib.parse import urlparse
domain = urlparse(target_url).netloc
feed_data = self.store.hgetall(f"feed:{domain}")
if not feed_data:
return {"action": "deny", "reason": "Domain not in feed"}
category = feed_data[b"iab_category"].decode()
page_type = feed_data[b"page_type"].decode()
# Role-based policy evaluation
role_policies = {
"research_agent": {
"allowed_categories": ["Technology", "Business", "Science"],
"blocked_page_types": ["login", "checkout", "admin"]
},
"marketing_agent": {
"allowed_categories": ["Shopping", "Advertising", "Entertainment"],
"blocked_page_types": ["login", "settings", "admin"]
}
}
policy = role_policies.get(agent_role, {})
if page_type in policy.get("blocked_page_types", []):
return {"action": "deny", "reason": f"Blocked page type: {page_type}"}
cat_match = any(c in category for c in policy.get("allowed_categories", []))
if not cat_match:
return {"action": "deny", "reason": f"Category not in scope: {category}"}
return {"action": "allow", "category": category, "page_type": page_type}
# Usage
controller = FeedBasedAccessController()
controller.load_feed("/data/categorized_url_feed.jsonl")
result = controller.check_access("research_agent", "https://techcrunch.com/ai")
print(result) # {"action": "allow", "category": "Technology", "page_type": "article"}
class URLFeedAccessManager {
constructor(apiKey) {
this.apiKey = apiKey;
this.feedCache = new Map();
}
async enrichFromFeed(targetURL) {
const domain = new URL(targetURL).hostname;
// Check local feed cache first
if (this.feedCache.has(domain)) {
return this.feedCache.get(domain);
}
// Fallback to real-time API for uncached domains
const response = await fetch(
"https://www.websitecategorizationapi.com" +
"/api/iab/iab_web_content_filtering.php",
{
method: "POST",
headers: { "Content-Type": "application/x-www-form-urlencoded" },
body: new URLSearchParams({
query: targetURL,
api_key: this.apiKey,
data_type: "url",
expanded_categories: "1"
})
}
);
const data = await response.json();
const enrichment = {
domain: domain,
iabCategory: data.iab_classification?.[0]?.[0]
?.replace("Category name: ", "") || "Unknown",
filterCategory: data.filtering_taxonomy?.[0]?.[0]
?.replace("Category name: ", "") || "Unknown",
pageType: data.page_type || "unknown",
timestamp: new Date().toISOString()
};
this.feedCache.set(domain, enrichment);
return enrichment;
}
async evaluateAccess(agentRole, targetURL, policyRules) {
const feedData = await this.enrichFromFeed(targetURL);
if (policyRules.blockedPageTypes.includes(feedData.pageType)) {
return { action: "block", reason: `Page type: ${feedData.pageType}` };
}
if (policyRules.blockedCategories.includes(feedData.filterCategory)) {
return { action: "block", reason: `Category: ${feedData.filterCategory}` };
}
return { action: "allow", feedData };
}
}
Purpose-built domain databases for AI agent filtering. Includes IAB categories, 20+ page types, reputation scores, and popularity rankings. One-time purchase with perpetual license.
10 Million Domains with Page-Type Intelligence
One-time purchase: Perpetual license | Optional Updates: $1,599/year
20 Million Domains with Full Intelligence Suite
One-time purchase: Perpetual license | Optional Updates: $2,999/year
50 Million Domains with Complete Intelligence Suite
One-time purchase: Perpetual license | Optional Updates: $4,999/year
Also available: Enterprise URL Database up to 102M domains from $2,499. View all database tiers →
Search any IAB or Web Filtering category to see how many domains are in our 102M Enterprise Database — the same data powering your categorized URL feeds for agent access management.
How 102 million domains from our main Enterprise Database are distributed across IAB v3 taxonomy classifications
Spanning Tier 1 through Tier 4 classifications from our 102M Enterprise Database
Charts display domain counts for the top 50 out of 700+ categories in our 102M Enterprise Database. To check the number of domains for the remaining 650+ categories, use the Category Counter tool above .
The traditional approach to web access management relies on real-time proxy inspection: a user requests a URL, the proxy intercepts the request, evaluates it against a policy, and either forwards or blocks it. This model works for human users because humans browse at human speed — a few hundred pages per day at most. AI agents break this model because they navigate at machine speed — potentially thousands of pages per minute.
Pre-categorized URL feeds solve the speed problem by moving the classification step from real-time to batch processing. Instead of classifying each URL at request time, the feed delivers the classification data in advance. Your access management layer loads the feed into a local data store, and every agent URL check becomes a simple key-value lookup. The result is sub-millisecond policy evaluation with zero external network calls, zero model inference, and zero latency variance.
Enterprise deployments rarely run a single agent. A typical mid-market deployment includes research agents, procurement agents, competitive intelligence agents, and customer service agents — each with different access requirements. A monolithic URL filter that applies the same rules to all agents is either too restrictive (blocking domains that some agents legitimately need) or too permissive (allowing domains that some agents should never visit).
Feed-based access management solves this by segmenting the URL feed by agent role. The full 102M domain feed is the master dataset. From this master, you derive role-specific feeds: the research agent's feed includes Technology, Science, and Business categories. The procurement agent's feed includes Shopping, Business Services, and Financial Services. The customer service agent's feed includes Support, Documentation, and FAQ page types. Each agent's local lookup table contains only the domains it is authorized to access, enforcing least-privilege at the data layer.
Most enterprises already operate identity and access management platforms — Okta, Azure AD, Ping Identity, or custom LDAP directories. These platforms manage authentication (who is the agent?) and authorization (what resources can the agent access?). URL feeds add a third dimension: destination awareness (where is the agent going?). The integration pattern maps IAM roles to feed segments: when the IAM system authenticates an agent with role "financial-analyst," the access management layer loads the financial-analyst feed segment, which contains only Business, Finance, and Economics category domains.
This three-dimensional access control — identity, authorization, and destination — creates a complete governance model. The IAM system answers "is this agent allowed to browse?" The URL feed answers "is this agent allowed to browse here?" The combination ensures that agent web access is both authenticated and contextually appropriate.
The internet is not static. New domains are registered at approximately 50,000 per day. Existing domains change ownership, content, and purpose. A URL feed that was accurate six months ago may have significant gaps today. Our feed update cadence is quarterly, with each update incorporating newly registered domains, reclassified existing domains, and removed expired domains. For organizations requiring faster update cycles, the real-time API serves as the gap-filler — any domain not in the current feed version is classified on demand via the API, and the result can be cached locally until the next feed update arrives.
One of the strongest arguments for feed-based access management is the audit trail it produces. Every agent navigation event generates a log entry that includes the target URL, the feed-derived category, the page type, the reputation score, the agent's role, the policy rule that was evaluated, and the resulting decision (allow, block, or flag). This structured log is exactly what compliance teams need to demonstrate that agent web access stayed within approved boundaries during audits. Compare this to a model-based approach where the classification decision is probabilistic and non-reproducible — you cannot re-run the same LLM classification six months later and guarantee the same result.
Enterprise web proxies like Zscaler, Netskope, and Palo Alto Prisma Access already categorize URLs for human users. Some teams consider routing agent traffic through these existing proxies. While this approach reuses existing infrastructure, it introduces three problems. First, proxy-based classification adds 50-200ms of latency per request, which compounds rapidly when an agent makes hundreds of requests per minute. Second, proxy licenses are priced per user seat — adding thousands of agent sessions dramatically increases costs. Third, proxies are designed for interactive browsing, not programmatic navigation — they struggle with headless browser sessions, API-driven requests, and the high request rates that agents generate.
A feed-based approach avoids all three problems. The data is local, so latency is sub-millisecond. The feed is a one-time purchase, so per-request costs are zero. And the feed integrates at the application layer, not the network layer, so it works with any agent architecture — headless browsers, API clients, or programmatic HTTP libraries.
The most sophisticated feed integration pattern embeds category awareness directly into the agent orchestration layer. Instead of treating URL filtering as a post-hoc compliance check, you make category data a first-class input to the agent's planning process. When the agent's reasoning engine generates a plan that includes visiting a set of URLs, the orchestration layer enriches each URL with feed data before the plan executes. The agent can then adjust its plan based on category information — for example, skipping a domain that the feed classifies as "login page" and choosing an alternative source that is classified as "documentation."
This pattern transforms the feed from a passive filter into an active planning input, improving both the safety and the effectiveness of agent workflows. Agents make better navigation decisions because they have structured intelligence about their destinations before they visit them.
The primary buyers of feed-based agent access management are enterprise security and compliance teams that have already deployed or are planning to deploy autonomous AI agents in production. These teams recognize that existing web filtering infrastructure was designed for human browsing patterns and cannot scale to agent traffic volumes without fundamental architectural changes.
Platform vendors building agent orchestration products — companies creating the next generation of AI agent frameworks — need feed data to offer their customers built-in access governance. Without this data, their platforms require customers to build custom filtering from scratch, which slows adoption and increases deployment risk.
Managed service providers operating AI agents on behalf of multiple clients need feed-based access management to enforce client-specific policies. Each client may have different category restrictions, and the feed segmentation model allows a single MSP deployment to serve multiple policy configurations from the same master dataset.
Deploy pre-categorized domain intelligence as the foundation of your AI agent access management. One-time purchase, perpetual license, 102 million domains classified and ready to feed into your governance pipeline.