** Enhancing Web Browsing with an Intelligent Browser Interceptor

defrecord: Intelligent Browser Interceptor

defrecord: Intelligent Browser Interceptor

Introduction: Browser Interception in Agentic AI Systems

Browser interception represents a critical infrastructure component for modern agentic AI systems. As autonomous agents increasingly interact with web-based resources, the ability to intercept, transform, and intelligently process browser traffic becomes essential. Unlike traditional middleware or proxy patterns, intelligent browser interceptors must maintain rich semantic context about web interactions while enabling AI agents to make informed decisions about content extraction, transformation, and routing.

The challenge lies in building interceptors that are both flexible enough to handle diverse web content patterns and structured enough to integrate seamlessly with AI agent architectures. This is where Clojure's defrecord shines, offering a compelling alternative to traditional object-oriented approaches or raw map-based implementations.

Why defrecord for Interceptor Patterns?

Clojure's defrecord provides an optimal foundation for building browser interceptors in agentic systems for several compelling reasons. First, defrecord instances combine the performance benefits of Java classes with the flexibility of Clojure's immutable data structures. They participate in protocol dispatch, enabling polymorphic behavior without the overhead of traditional OOP hierarchies.

Second, defrecord naturally enforces structural contracts while remaining open to extension through protocols. This aligns perfectly with the interceptor pattern, where we need guaranteed fields for core functionality (like URL patterns, transformation rules, and metadata) while supporting protocol-based extension for domain-specific behaviors.

Third, defrecord instances are immutable by default, making them ideal for concurrent agent systems where multiple AI agents may reference the same interceptor configuration without risk of state corruption. This immutability also enables powerful debugging and replay capabilities, essential when diagnosing agent behavior in production environments.

Finally, defrecord integrates seamlessly with Clojure's data-oriented ecosystem. Interceptor configurations can be serialized to EDN, versioned alongside code, and manipulated using the full power of Clojure's sequence abstractions.

Architecture: Protocols and Structure

The architecture of an intelligent browser interceptor system centers on three core protocols that define the contract for browser interception in agentic environments:

The Interceptable protocol defines the fundamental capability to intercept and process browser requests and responses. This protocol must support both synchronous interception (blocking until processing completes) and asynchronous interception (allowing agents to process in the background).

The Transformable protocol governs how intercepted content is transformed for AI consumption. This includes extracting semantic content from HTML, normalizing diverse data formats into standardized structures, and enriching raw browser data with contextual metadata that AI agents can leverage.

The RouteDecidable protocol enables intelligent routing decisions based on intercepted content. This is particularly crucial for MCP (Model Context Protocol) integration, where different types of web resources may need to be routed to specialized tools or agent capabilities.

Integration with AI Agents and MCP

Browser interceptors serve as the bridge between web-based information sources and AI agent capabilities. When integrated with the Model Context Protocol, interceptors become specialized MCP tools that expose web browsing capabilities to AI agents in a structured, controllable manner.

An MCP-integrated browser interceptor can expose operations like intelligent page navigation, semantic content extraction, form interaction, and dynamic content monitoring. The interceptor translates these high-level operations into browser automation primitives while maintaining the semantic context that AI agents need to make informed decisions.

The protocol-based architecture enables seamless composition with other MCP tools. An agent might use a browser interceptor tool to extract data from a web application, then pass that data to a database tool or analysis tool, all while maintaining consistent error handling and state management across the tool chain.

Implementation: Core Protocols

(ns intelligent-browser.interceptor
  (:require [clojure.string :as str]))

;; Core protocols defining interceptor capabilities
(defprotocol Interceptable
  "Protocol for intercepting and processing browser traffic"
  (intercept-request [this request]
    "Process an outgoing browser request, returning transformed request or nil to block")
  (intercept-response [this response]
    "Process an incoming browser response, returning transformed response")
  (should-intercept? [this url]
    "Predicate determining if this interceptor should process the given URL"))

(defprotocol Transformable
  "Protocol for transforming web content for AI consumption"
  (extract-semantic-content [this html-content]
    "Extract semantic content from HTML for agent processing")
  (normalize-data [this raw-data]
    "Normalize diverse data formats into standard structure")
  (enrich-metadata [this content context]
    "Enrich content with contextual metadata for AI agents"))

(defprotocol RouteDecidable
  "Protocol for intelligent routing of intercepted content"
  (determine-route [this content]
    "Decide routing based on content characteristics")
  (select-tool [this operation-type]
    "Select appropriate MCP tool for the operation"))

Implementation: BrowserInterceptor defrecord

;; Core interceptor implementation using defrecord
(defrecord BrowserInterceptor [name
                                url-patterns
                                transform-rules
                                metadata
                                agent-context]
  Interceptable
  (intercept-request [this request]
    (when (should-intercept? this (:url request))
      (-> request
          (assoc :intercepted-by name)
          (assoc :agent-context agent-context)
          (update :headers merge {"X-Agent-Interceptor" name}))))

  (intercept-response [this response]
    (let [content-type (get-in response [:headers "content-type"])]
      (cond-> response
        (str/includes? content-type "text/html")
        (update :body #(extract-semantic-content this %)))))

  (should-intercept? [this url]
    (some #(re-matches % url) url-patterns))

  Transformable
  (extract-semantic-content [this html-content]
    {:main-content (extract-main-content html-content)
     :metadata (extract-page-metadata html-content)
     :links (extract-links html-content)
     :timestamp (System/currentTimeMillis)
     :interceptor name})

  (normalize-data [this raw-data]
    (let [data-type (detect-data-type raw-data)]
      (case data-type
        :json (normalize-json raw-data)
        :html (normalize-html raw-data)
        :text (normalize-text raw-data)
        raw-data)))

  (enrich-metadata [this content context]
    (assoc content
           :agent-context agent-context
           :enrichment-timestamp (System/currentTimeMillis)
           :confidence-score (calculate-confidence content)
           :suggested-actions (suggest-actions content context)))

  RouteDecidable
  (determine-route [this content]
    (let [content-type (:type content)]
      (get-in transform-rules [:routing content-type] :default)))

  (select-tool [this operation-type]
    (get-in transform-rules [:mcp-tools operation-type])))

;; Helper functions for semantic extraction
(defn extract-main-content [html]
  ;; Simplified - would use proper HTML parsing
  (str/replace html #"<[^>]*>" ""))

(defn extract-page-metadata [html]
  {:title (extract-title html)
   :description (extract-description html)})

(defn extract-links [html]
  ;; Would use proper HTML parsing to extract href attributes
  [])

(defn detect-data-type [data]
  (cond
    (string? data) (if (str/starts-with? (str/trim data) "{") :json :text)
    (map? data) :map
    :else :unknown))

(defn calculate-confidence [content]
  ;; Simplified confidence calculation
  0.85)

(defn suggest-actions [content context]
  ;; Would integrate with AI agent to suggest next actions
  [:extract-data :summarize :store])

Implementation: MCP Tool Integration

;; MCP tool wrapper for browser interceptor
(defn create-mcp-browser-tool
  "Create an MCP tool that exposes browser interceptor capabilities"
  [interceptor]
  {:name "intelligent_browser"
   :description "Intelligent browser with AI-powered interception and extraction"
   :input-schema
   {:type "object"
    :properties
    {:operation {:type "string"
                 :enum ["navigate" "extract" "monitor" "interact"]}
     :url {:type "string"}
     :options {:type "object"}}
    :required ["operation" "url"]}

   :handler
   (fn [params]
     (let [{:keys [operation url options]} params]
       (case operation
         "navigate"
         (let [request {:url url :method "GET"}
               intercepted (intercept-request interceptor request)]
           {:status "success" :request intercepted})

         "extract"
         (let [response {:url url :body (:content options)}
               processed (intercept-response interceptor response)]
           {:status "success" :content processed})

         "monitor"
         {:status "monitoring" :url url :interceptor (:name interceptor)}

         "interact"
         {:status "interacting" :url url :action (:action options)})))})

;; Example: Creating an interceptor for documentation sites
(def docs-interceptor
  (map->BrowserInterceptor
   {:name "docs-interceptor"
    :url-patterns [#"https://clojure\.org/.*"
                   #"https://.*\.github\.io/.*"
                   #"https://cljdoc\.org/.*"]
    :transform-rules
    {:routing {:documentation :knowledge-base
               :api-reference :code-index
               :tutorial :learning-path}
     :mcp-tools {:navigate "intelligent_browser"
                 :search "semantic_search"
                 :store "knowledge_store"}}
    :metadata {:purpose "Extract and index technical documentation"
               :priority :high}
    :agent-context {:agent-id "doc-indexer"
                    :session-id (str (random-uuid))}}))

;; Create MCP tool from interceptor
(def docs-browser-tool (create-mcp-browser-tool docs-interceptor))

Practical Applications

Intelligent browser interceptors built with defrecord enable several powerful use cases in agentic AI systems. Documentation indexing agents can use interceptors to automatically extract and structure technical documentation from diverse sources. Research agents can intercept academic paper repositories, extracting citations, methodologies, and results into structured knowledge graphs.

Monitoring agents can use interceptors to track changes in web-based dashboards or status pages, triggering alerts or automated responses based on semantic content changes rather than simple text matching. Form-filling agents can leverage interceptors to understand complex web forms and interact with them intelligently, maintaining context about multi-step processes.

The protocol-based design enables teams to extend interceptors with domain-specific capabilities without modifying core implementations. A finance team might add a FinancialDataExtractable protocol, while a healthcare team adds HIPAA-compliant sanitization through a SanitizeHealthData protocol.

Conclusion

Clojure's defrecord provides an elegant foundation for building intelligent browser interceptors in agentic AI systems. By combining structural guarantees, protocol-based polymorphism, and immutable semantics, defrecord enables interceptor implementations that are both performant and maintainable. When integrated with the Model Context Protocol, these interceptors become powerful tools that bridge the gap between web-based information sources and AI agent capabilities, enabling sophisticated autonomous systems that can navigate, extract, and reason about web content with minimal human intervention.