From LLM to LLM-Driven Agent Building Autonomous AI Assistants for Everyday Users

Faruk Baran Öncel

Faruk Baran Öncel 232 Labs - 232 Elektronik Hiz. ve Tic. A.Ş., Türkiye

Keywords: Large Language Models, LLM-driven agents, autonomous AI, AI assistants, Lang Graph, CrewAI, memory-augmented models, reasoning and planning, tool integration, everyday users, human-AI interaction, task automation, agentic frameworks, multimodal agents, embodied agents, artificial general intelligence

Abstract

AI development took an important step forward when Large Language Models (LLMs) were adapted to make autonomous AI assistants suitable for use in real life situations. Previously, LLMs exhibited powerful text generation, yet they had no ability to act on their own or use other tools without help. At the same time, today’s agents make use of LLMs, structured tools, logical thinking and learning to perform complicated duties requiring very little intervention from humans.

With the use of function calling, multimodal interfaces and long context abilities, the need for extensive agent orchestration from 2024–25 has greatly decreased. This new technology supports those without specialized IT expertise, giving them improved digital experiences, helping hands and increased productivity.

It is important to note that having tools, memory modules and understanding current settings are changes that bridge this gap. At the same time, there are continuing worries about whether data is reliable, if things are transparent and how well autonomous modes work safely. American lawmakers are relying more on modern materials like the EU AI Act, the White House Executive Order and the NIST AI Risk Management Framework when it comes to ethical issues.

With more developments in AI, it is becoming clear that more valuable progress will come from integrating robots and software into human cooperation than from simply improving the core models. As a result, interactions between people and AI start to feel easier, more interactive and more fun.

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