1 Introduction to agents and their world
This chapter covers
- Defining the concept of agents
- Differentiating the components of an agent
- Analyzing the rise of the agent era: Why agents?
- Peeling back the GPT interface
- Navigating the agent landscape
This book introduces the world of large language model-empowered agents and assistants—or what is more commonly referred to now as GPT Agents and Assistants, where GPT is an acronym for generative pre-trained transformer, which is the architecture that powers the current best large language models, or LLMs.
This chapter will explore what an agent is, why we need agents, the components that make up an agent, what agents have to do with software 2.0, and the platform and tools used to build agents. Starting in the next section, we define what an agent and assistant are.
1.1 Defining agents
The agent is certainly not a new concept in machine learning and artificial intelligence. In reinforcement learning, for instance, the word agent denotes an active decision-making and learning intelligence. In other areas, the word agent aligns more with an automated application or software that does something on your behalf.
You can consult any online dictionary to find the definition of an agent. The Merriam-Webster Dictionary defines it this way (https://www.merriam-webster.com/dictionary/agent):
- One that acts or exerts power
- Something that produces or can produce an effect
- A means or instrument by which a guiding intelligence achieves a result