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.

The distinction between agents and functions is blurred, as both can involve multiple functions with language models in the middle, making them appear similar from an external perspective.

As AI agents begin to work more with each other, the dynamics of their communication and reasoning may evolve significantly.

The emergence of reasoning models is expected to enable AI agents to perform actions on behalf of users, enhancing their reliability and effectiveness in real-world applications.

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.

The core AI agent acts as the brain of the system, handling logic, knowledge, and workflows, similar to how human agents are trained.

Using an agent is beneficial when you can't enumerate all possible situations, allowing the model to navigate unknown scenarios using its own intuition.

The model will show an interleaved chain of thought, reasoning through the sources it encounters and the tool calls made, such as searching or analyzing with Python.

The technology behind this AI agent is a multi-agent system that utilizes a core react-like loop and various tools to assist users in coding tasks.