As AI models improve, agents are becoming more prevalent and capable, prompting the need for formal definitions to clarify their use.

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

It's crucial for the AI field to agree on a definition of AGI, as this will help guide the development of reliable and safe systems in the future.

The confusion around agents stems from the expectation that AI will develop human-like intent and creativity, which may not be theoretically possible.

There's a lot of excitement around AI agents, but many people are unsure of their practical applications, often trying to apply them to problems that simpler solutions could address.

There is a significant gap in the market for frameworks that clearly define how to scale and build AI agents, highlighting an opportunity for innovation.

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.

Defining AGI is complex; if you ask ten OpenAI researchers, you'll get fourteen different definitions.

Some argue that for something to be a true agent, it must be close to artificial general intelligence (AGI), capable of learning and working independently over time.