Generative AI and Search Engine Orchestration: A Unified Framework for Intent-Driven Internet Task Routing and Hybrid Execution

The rapid development of generative artificial intelligence (GenAI) has begun to transform how people access, interpret, and produce information on the Internet. Traditional search engines primarily support information discovery through ranked web documents, links, and navigational results, whereas generative AI systems support natural-language interaction, synthesis, explanation, and content creation. Although these technologies are increasingly converging, users are still frequently required to determine which system to use, formulate requests according to the capabilities of a particular platform, and manually transfer information between search and generation environments. This separation introduces interaction costs and creates a mismatch between the user's underlying objective and the technical capabilities through which that objective is pursued. This paper proposes Generative AI and Search Engine Orchestration (GASEO), a unified framework that treats generative AI and traditional search engines as complementary Internet task-execution capabilities coordinated through a single natural-language entry point. Rather than requiring users to select a tool before submitting a request, GASEO interprets the underlying task, estimates the capabilities required to complete it, and dynamically selects generative execution, search execution, or hybrid execution. Its central design principle is that users should express what they want to accomplish, while the system determines how the task should be executed. The framework introduces a capability-oriented intent representation, an intent-to-capability routing mechanism, uncertainty-aware hybrid execution, adaptive execution sequencing, result coordination, provenance preservation, and post-execution recovery. It distinguishes between requests requiring content generation, requests requiring access to external web resources, and compound requests requiring both. A dedicated uncertainty mechanism is proposed for cases in which the system cannot reliably determine whether a single execution capability is sufficient. In such cases, the system may invoke both capabilities, request clarification, or execute a low-cost preliminary operation before committing to a final route. Importantly, GASEO is not defined as a conventional search engine with an AI-generated summary placed above ranked results, nor is it limited to retrieval-augmented generation. It is conceptualized as an orchestration layer that determines which Internet task capabilities should be invoked, in what sequence, and how their outputs should be presented while preserving the independent value of original search results. This paper develops the framework's architecture, decision model, execution strategies, evaluation methodology, research propositions, and implementation considerations. It presents a research framework and does not claim completed experimental validation or demonstrated performance superiority.