Most of you have probably heard the term AI Agent.
But what exactly are AI Agents? Are you ready to build one, hire one, or work with one?
At TeleMARS, we’ve been developing a few AI Agent applications, and we’re excited to announce the launch of our AI Agent Diary.
Through this series, we’ll share our project experiences and lessons learned, helping you benefit from AI Agents more effectively and avoid the mistakes we made along the way.
Why AI Agents?
Since ChatGPT’s breakthrough demonstrated the effectiveness of LLMs (Large Language Models), many businesses and individual innovators have been exploring opportunities in this space.
However, many quickly realize that building a general-purpose generative AI model like ChatGPT requires massive resources—including computing power, infrastructure, data, and specialized talent. Very few organizations have access to resources on this scale.
Fine-tuning existing models to enhance specific capabilities has become an attractive approach to innovation in this field. Yet, model tuning also demands substantial resources, which most medium-sized businesses cannot easily afford at this stage.
Using existing generative AI models, including LLMs, in a business environment to create new capabilities and add value presents more accessible opportunities for a broader range of innovation initiatives. This is where the concept of the AI Agent has emerged.
What is an AI Agent?
An AI Agent is fundamentally a software application that harnesses the power of generative AI models to solve complex problems with greater efficiency and intelligence. By integrating with these models, these applications can process vast amounts of data and automate intricate tasks that would otherwise require significant time and resources.
