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During the early years of AI, the primary focus was on rule-based systems. These systems were designed to mimic human intelligence by following a set of predefined rules. They were used in areas like medical diagnosis, where a set of symptoms could be matched to a potential disease. However, these systems were limited by their inability to learn or adapt to new situations.

The 1980s and 1990s saw the rise of expert systems, which were a more advanced form of rule-based systems. These systems used knowledge from human experts to make decisions. They were used in areas like financial planning and geological exploration. However, like rule-based systems, they were also limited by their inability to learn from experience.

With the advent of machine learning and deep learning, modern AI agents have the ability to learn from experience. They can adapt to new situations and improve their performance over time. These agents are used in a wide range of applications, from autonomous vehicles to personal assistants.

Deep learning is a subset of machine learning that uses neural networks with many layers (hence the 'deep' in deep learning) to model and understand complex patterns. It's behind many of the advancements we see in AI today, including image and speech recognition, recommendation systems, and natural language processing.

Reinforcement learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a goal. The agent learns from the consequences of its actions, rather than from being explicitly taught, making it capable of flexible, autonomous learning.

Natural Language Processing (NLP) is a branch of AI that focuses on the interaction between computers and humans through natural language. The ultimate objective of NLP is to read, decipher, understand, and make sense of the human language in a valuable way. It's the technology used in voice assistants like Siri and Alexa, and in language translation apps.