Note: OpenAI leads the general AI space, but AI companies are developing deep research tools and experimenting with AI-powered academic searching in support of research.
Also: This open textbook (or wiki channel) is intended to help librarians and other information professionals learn about AI. It is not, in itself, meant to be seen as promotion of AI.
Introduction
Agentic AI (see Wikipedia) refers to systems featuring agents who act autonomously, perceive their surrounding environment, make decisions, and take actions to achieve specific goals. AI agents are used to perform small tedious tasks during web browsing and potentially even perform browser actions on behalf of the user. An example of an advanced agentic AI system is Auto-GPT, an open-source tool that employs large language models (LLMs) to autonomously perform complex, multi-step tasks by breaking them into sub-goals, reasoning through each step, and utilizing external tools like web browsers, APIs, or file systems to achieve objectives. For instance, if tasked with "research and create a market analysis report," Auto-GPT can independently search the web, gather data, analyze it, and generate a structured report without needing constant human input. Its ability to plan, self-correct, and interact with external environments makes it a strong example of advanced agentic AI.
Another example of agentic AI in action is Undermind.ai, developed by MIT researchers, an AI-powered research assistant designed to streamline academic discovery by reading and analyzing hundreds of scientific papers, synthesizing findings, and delivering precise, relevant insights.
"evolution of Agentic AI, including Generative AI (GenAI) agents, has outpaced understanding of its applications, challenges, and strategic implications. This review explores Agentic AI focusing on its key attributes—autonomy, reactivity, proactivity, and learning ability; we identify a research gap in synthesizing the diverse capabilities of Agentic AI (e.g., multimodal processing, hierarchical architectures, and machine learning outsourcing) and actionable strategies for adoption. Findings reveal Agentic AI improves productivity, reduces costs, and drives innovation, though challenges such as privacy, security, and ethical concerns remain. Future research should focus on case studies to deepen understanding, explore impact (e.g., privacy, data security, labor market effects), and investigate integration."
"...AI Agents [are] modular systems driven by Large Language Models (LLMs) and Large Image Models (LIMs) for narrow, task-specific automation. Generative AI is positioned as a precursor, with AI Agents advancing through tool integration, prompt engineering, and reasoning enhancements. In contrast, Agentic AI systems represent a paradigmatic shift marked by multi-agent collaboration, dynamic task decomposition, persistent memory, and orchestrated autonomy."
"...Rapid advances in large language models (LLMs) have led to the emergence of agentic artificial intelligence (AI) systems capable of autonomously performing complex scientific tasks. This review examines recent developments in agentic AI, highlighting their transformative potential for ophthalmology research and clinical practice, and discusses associated ethical considerations."
Disclaimer
Note: Please use your critical reading skills while reading entries. No warranties, implied or actual, are granted for any health or medical search or AI information obtained while using these pages. Check with your librarian for more contextual, accurate information.
