{"id":4460,"date":"2025-07-22T10:42:24","date_gmt":"2025-07-22T10:42:24","guid":{"rendered":"https:\/\/www.intnews.it\/?p=4460"},"modified":"2025-08-28T12:31:05","modified_gmt":"2025-08-28T12:31:05","slug":"architectural-evolution-of-google-search-integration-of-advanced-llms-and-agentic-frameworks","status":"publish","type":"post","link":"https:\/\/intnews.it\/en\/architectural-evolution-of-google-search-integration-of-advanced-llms-and-agentic-frameworks\/","title":{"rendered":"Architectural evolution of Google Search: integration of advanced LLMs and agentic frameworks"},"content":{"rendered":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>Google is implementing a significant evolution of its search architecture, transitioning from a traditional information retrieval engine to a multimodal reasoning and action system. Recent integrations for users of premium U.S. plans, based on the <strong>Gemini 2.5 Pro<\/strong> model, introduce a deep query engine and an agentic framework capable of executing real-world tasks, thereby redefining the search engine&#8217;s operational paradigm.<\/p>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p><strong>Integration of Gemini 2.5 Pro for Complex Query Decomposition<\/strong><\/p>\n\n\n\n<p>The core of the update is the integration of the Large Language Model (LLM) <strong>Gemini 2.5 Pro<\/strong>, accessible through an experimental interface called &#8220;AI Mode.&#8221; Unlike previous models, Gemini 2.5 Pro is designed to handle queries that require <strong>multi-step reasoning<\/strong> and complex problem decomposition. The model is capable of:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Performing computational tasks<\/strong>: Solving mathematical and logical problems that require intermediate calculations.<\/li>\n\n\n\n<li><strong>Interpreting and generating code<\/strong>: Addressing programming-related prompts by providing functional code snippets.<\/li>\n\n\n\n<li><strong>Generating synthesized responses with citations<\/strong>: The outputs are not simple text extractions but synthesized responses accompanied by a graph of references\u2014an approach similar to <strong>Retrieval-Augmented Generation (RAG)<\/strong> that mitigates the risk of hallucinations and ensures the traceability of sources.<\/li>\n<\/ul>\n\n\n\n<p>This transforms the search process from keyword matching and ranking into a conversational interface capable of articulated problem-solving.<\/p>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p><strong>Deep Search: A Federated Query and Synthesis Engine<\/strong><\/p>\n\n\n\n<p>In parallel, Google is introducing <strong>Deep Search<\/strong>, an advanced search architecture designed for open-ended or ambiguous prompts that cannot be satisfied by a single query. Its operation is based on:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Parallelized query execution<\/strong>: The system launches hundreds of exploratory queries simultaneously to map a broader knowledge domain.<\/li>\n\n\n\n<li><strong>Integration of heterogeneous sources<\/strong>: Deep Search can aggregate and correlate data from unstructured (web text), semi-structured (tables, lists), and structured (Knowledge Graph) sources.<\/li>\n\n\n\n<li><strong>Synthesis and reporting<\/strong>: The output is not a list of links (SERP) but a structured analytical report that presents a summary of the gathered information, highlighting connections and providing verified sources.<\/li>\n<\/ul>\n\n\n\n<p>This tool is optimized for complex analysis tasks, such as market research, financial planning, or academic reviews, reducing the cognitive load and manual data aggregation required from the user.<\/p>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p><strong>Agentic Framework for Real-World Task Execution<\/strong><\/p>\n\n\n\n<p>The most disruptive innovation is the introduction of <strong>agentic capabilities<\/strong>, wherein the AI autonomously executes actions on behalf of the user. The automatic calling function is a direct evolution of <strong>Google Duplex<\/strong> technology and relies on a complex technology stack:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Natural Language Understanding (NLU)<\/strong>: For parsing user input and extracting key entities (e.g., service type, preferences, temporal parameters).<\/li>\n\n\n\n<li><strong>Dialog Management System<\/strong>: A system that governs the conversational flow with the human interlocutor, capable of handling deviations and requests for clarification.<\/li>\n\n\n\n<li><strong>Natural Language Generation (NLG) and Text-to-Speech (TTS)<\/strong>: To generate natural and contextually appropriate speech, identifying itself as an AI assistant.<\/li>\n<\/ul>\n\n\n\n<p>The workflow involves acquiring parameters from the user, autonomously executing the call, extracting relevant information during the dialogue, and finally, structuring this data into a synthetic output delivered to the user via messaging APIs (SMS or email).<\/p>\n\n\n\n<p>Furthermore, the platform provides a control interface for businesses via <strong>Google Business Profile<\/strong>, allowing them to implement an <strong>opt-out<\/strong> mechanism from the AI calling service\u2014a crucial aspect for the system&#8217;s governance and adoption. This release strategy, limited to specific categories and geographies, follows an A\/B testing approach to validate the system&#8217;s robustness and acceptance before a potential large-scale deployment.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Google is implementing a significant evolution of its search architecture, transitioning from a traditional information retrieval engine to a multimodal reasoning and action system. Recent integrations for users of premium&#8230;<\/p>\n","protected":false},"author":6,"featured_media":4455,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[84],"tags":[],"class_list":{"0":"post-4460","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-technologies"},"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Architectural evolution of Google Search: integration of advanced LLMs and agentic frameworks - INT News<\/title>\n<meta name=\"description\" content=\"Google is implementing a significant evolution of its search architecture, transitioning from a traditional information retrieval engine to a multimodal\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/intnews.it\/evoluzione-architetturale-di-google-search-integrazione-di-llm-avanzati-e-framework-agentici\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Architectural evolution of Google Search: integration of advanced LLMs and agentic frameworks - 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