When building your own AI tools, you can create a navigation tree and focus on unreliable parts, enhancing the overall reliability of the agent.

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.

Independently evaluating sub-agents helps identify weak links and improve overall accuracy in the system.

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.

Programming with an agent is really empowering; it's like an extension of myself that helps me go further.

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.

By prompting the model with different language, we can simulate counterfactuals and complex scenes, allowing for the generation of varied scenarios based on the same initial conditions and enabling a robot to interact with objects as if it were in the real world, even if the hardware doesn't support it.

Agents for consumers are currently overhyped. Specifying preferences for tasks can be as complex as doing the task yourself, and verifying the agent's actions can be very expensive.

All agents share a common element: reasoning and decision-making, which can be likened to a multi-step language model chain with a dynamic decision tree.