AI Agents

Perception: An agent has "sensors" to take in information from its environment. This can be anything from a camera in a self-driving car to an API that reads data from a database or a chatbot that processes a user's text input.

Action: An agent has "actuators" that allow it to perform actions in its environment. This could be moving a robotic arm, writing to a file, making an API call, or generating a response to a user.

Autonomy: This is a crucial element. Unlike a traditional program that follows a fixed set of instructions, an AI agent has a degree of freedom to decide which actions to take to reach its goal. It can reason, plan, and adjust its course of action based on new information.

Goal-Oriented: An agent is designed with a specific objective in mind. Its behavior is guided by this goal, and it strives to achieve the best possible outcome.

Is There a Standard Definition?

While there isn't a single, universally adopted standard like a formal ISO or IEEE definition, the concept of an AI agent is a core part of the field of artificial intelligence.

Many of the leading textbooks and researchers in AI, such as Stuart Russell and Peter Norvig in their influential book "Artificial Intelligence: A Modern Approach," define AI as the "study and design of intelligent agents."

The core elements of the definition—perception, action, and goal-directed autonomy—are widely accepted in academic and industry contexts. However, different types of agents are often categorized based on their level of complexity and how they achieve their goals:

Simple Reflex Agents: These are the most basic. They act based only on the current state of the environment, without any memory of the past. (e.g., a simple thermostat that turns the heat on when the temperature drops below a certain point).

Model-Based Agents: These agents maintain an internal "model" of the world, allowing them to track the state of the environment and how their actions affect it. This gives them more robust decision-making capabilities.

Goal-Based Agents: These are more advanced. They don't just react; they have a clear goal and can plan a sequence of actions to achieve it.

Utility-Based Agents: These are even more sophisticated. They have a "utility function" that allows them to weigh different possible outcomes and choose the action that maximizes their overall "utility" or benefit.

Learning Agents: These agents have the ability to learn and improve their performance over time, adapting to new situations and becoming more effective with experience.

In the modern context of generative AI, the term "AI agent" has evolved to describe a system that uses a large language model (LLM) as its "brain" to perform complex, multi-step tasks. These agents are distinguished by their ability to:

Decompose a goal into smaller, manageable sub-tasks.

Use external tools (like APIs, web browsers, or databases) to gather information and perform actions.

Reason and reflect on their progress, making corrections and updating their plan as needed.

Maintain memory of past interactions and information to provide context for future actions.

So, while the core academic definition remains consistent, the popular understanding of an "AI agent" today often refers to these more advanced, LLM-powered systems capable of tackling complex, real-world problems.

Added September 7, 2025

How To Design Better AI Apps, Y Combinator

https://www.youtube.com/watch?v=WJoZK9sMwvw

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