Advancing the Search Frontier with AI Agents (2311.01235v2)

Abstract: As many of us in the information retrieval (IR) research community know and appreciate, search is far from being a solved problem. Millions of people struggle with tasks on search engines every day. Often, their struggles relate to the intrinsic complexity of their task and the failure of search systems to fully understand the task and serve relevant results. The task motivates the search, creating the gap/problematic situation that searchers attempt to bridge/resolve and drives search behavior as they work through different task facets. Complex search tasks require more than support for rudimentary fact finding or re-finding. Research on methods to support complex tasks includes work on generating query and website suggestions, personalizing and contextualizing search, and developing new search experiences, including those that span time and space. The recent emergence of generative AI and the arrival of assistive agents, based on this technology, has the potential to offer further assistance to searchers, especially those engaged in complex tasks. There are profound implications from these advances for the design of intelligent systems and for the future of search itself. This article, based on a keynote by the author at the 2023 ACM SIGIR Conference, explores these issues and how AI agents are advancing the frontier of search system capabilities, with a special focus on information interaction and complex task completion.

Advancing the frontier of search systems with AI agents is a multifaceted endeavor that leverages recent advancements in generative AI and autonomous agents to enhance search capabilities, especially for complex tasks.

The paper Advancing the Search Frontier with AI Agents explores how contemporary search systems fail users due to their inability to understand complex tasks fully and provide relevant results. The emergence of assistive AI agents, based on advancements in generative artificial intelligence, offers promising solutions. These agents can contextualize and personalize search results, improve query suggestions, and create new search experiences that span time and space (White, 2023 ).

The integration of generative AI with autonomous agents is also explored in WebArena: A Realistic Web Environment for Building Autonomous Agents. This paper highlights the creation of a realistic web environment to test language-guided agents performing tasks across varied websites. The results show that while GPT-4-based agents manage a 14.41% success rate, human agents achieve 78.24%, indicating a significant gap yet to be bridged, underscoring the complexity of real-world web tasks (Zhou et al., 2023 ).

Additionally, the Agents: An Open-source Framework for Autonomous Language Agents paper presents a tool designed to help both researchers and non-specialists develop and deploy autonomous language agents efficiently. This open-source library emphasizes planning, memory, tool usage, and multi-agent communication, which are pivotal for building sophisticated AI agents capable of handling diverse and dynamic tasks (Zhou et al., 2023 ).

Furthermore, the AutoAgents: A Framework for Automatic Agent Generation paper introduces a framework designed to dynamically generate and coordinate multiple specialized AI agents based on task requirements. This approach enhances the adaptability and efficiency of AI agents in complex task environments, further pushing the boundaries of what AI agents can achieve in collaborative and dynamic scenarios (Chen et al., 2023 ).

In summary, these research efforts collectively illustrate the advancements in AI agents, their potential applications in enhancing search systems, and the challenges that remain. By fostering more complex and adaptive AI agent behaviors through realistic training environments and robust frameworks, the search frontier can be significantly advanced, making search systems more intuitive and effective for end users.