AI agents represent a convergence of advancements in artificial intelligence (AI), particularly in the field of large language models (LLMs), with established principles of agent design. While traditional AI systems often focus on specific capabilities like language modeling or image recognition, AI agents leverage LLMs to create systems that can reason, plan, and act in dynamic environments. This shift towards agentic behavior marks a significant step towards the pursuit of artificial general intelligence (AGI).

Here's a breakdown of how AI agents relate to various AI concepts:

Artificial Intelligence (AI): AI agents fall under the broader umbrella of AI, aiming to create systems that exhibit intelligent behavior. However, while early AI systems often relied on handcrafted rules, AI agents utilize LLMs to achieve more flexible and adaptable intelligence.

Machine Learning (ML): LLMs, the core of AI agents, are trained using ML techniques on massive text datasets. ML, encompassing various algorithms and approaches, enables LLMs to learn patterns and relationships in data, forming the basis for their language understanding and generation abilities.

Supervised Learning: The pre-training process of LLMs often involves supervised learning, where the models are trained on labeled data to predict specific outcomes. For example, LLMs might be trained on text and code pairs to learn code generation.

Unsupervised Learning:  Some aspects of LLM training may also utilize unsupervised learning techniques, where models learn patterns and structures from unlabeled data. This helps LLMs acquire general language understanding and representation abilities.

Deep Learning (DL):  LLMs are a prime example of DL, a subfield of ML that utilizes artificial neural networks with multiple layers to process information. DL allows LLMs to learn complex representations and achieve high performance on various language-related tasks.

By integrating LLMs as their "brain," AI agents leverage the power of ML and DL to achieve sophisticated cognitive abilities. These abilities, combined with carefully designed architectures incorporating memory, perception, and action modules, allow AI agents to interact with complex environments and perform a wide range of tasks.