Towards AGI: Future Directions, Theoretical Limits, and Deployment Challenges of Autonomous AI Agents

Lakshay Kumar

Keywords: AGI, Autonomous AI Agents, Generative AI, Large Language Models, AI Systems, LangChain, Multi-Agent Systems, Artificial Intelligence

TL;DR: This paper explores autonomous AI agent architectures integrating planning, memory, and external tools toward future AGI systems.

Abstract: Artificial General Intelligence (AGI) aims to develop intelligent systems capable of performing diverse cognitive tasks with human-like adaptability. While modern AI systems have achieved strong performance in narrow domains through transformer architectures and large language models, they remain limited in reasoning flexibility and cross-domain adaptation. This paper investigates Autonomous AI Agents as a promising architectural approach toward more general intelligent systems. The proposed architecture integrates task planning, vector-based memory retrieval, external tool interfaces, and language-model-driven reasoning. A prototype autonomous research assistant agent was implemented using LangChain, FAISS vector storage, and GPT-class language models. Experimental evaluation on structured research tasks demonstrates strong performance in summarization, information retrieval, and multi-step reasoning workflows. The results highlight both the capabilities and current limitations of autonomous agent architectures and discuss future directions for scalable AGI-oriented systems.

Submission Number: 8