The concept of agents includes both those that replace human tasks and those that operate as low-level system processes, complicating the definition further.

The definition of an agent may evolve as we explore the capabilities of AI, particularly in how they interact with users and perform tasks.

A simple definition of an agent is a clever prompt on top of a knowledge base or context, resembling a human agent's interaction.

AI agents are expected to handle most mundane tasks, significantly lowering barriers for developers and making complex tasks like coding more accessible to everyone.

The creation of agents is becoming more accessible, allowing individuals to build numerous agents to streamline workflows, similar to creating spreadsheets.

An AI agent is defined as a system that allows a language model to autonomously decide how many times to run processes until it finds a resolution, rather than following a predefined path.

Our multi-agent system starts with a supervisor agent that orchestrates tasks, understands user intentions, and delegates to specialized sub-agents, ensuring high accuracy and reliability.

It's difficult to determine where agents are truly needed. There's a sweet spot for using agents in tasks that are valuable and complex, but where the cost of error or monitoring is relatively low.

AI agents are often defined as systems that perform complex planning and interact with external systems, but this definition overlaps with many current language models.