Agentic AIEASY

What is agentic AI, and how does it differ from traditional AI?

28 views

Q
Question

What is agentic AI, and how does it differ from traditional AI in terms of function and design principles?

A
Answer

Agentic AI refers to artificial intelligence systems that are designed to act autonomously, making decisions or taking actions based on their own goals or objectives. Unlike traditional AI, which often follows predefined rules or patterns, agentic AI has the capability to learn from its environment and adjust its behavior accordingly. A key difference between the two is that agentic AI is more focused on achieving specific goals in dynamic environments, whereas traditional AI typically operates within the confines of fixed datasets and static algorithms.

E
Explanation

Theoretical Background: Agentic AI is inspired by the concept of "agency," which refers to the capacity to act independently and make free choices. In the context of AI, this involves creating systems that can adapt to new situations and make decisions without direct human intervention. Traditional AI, on the other hand, is often rule-based or relies on static models that do not change once they have been trained.

Practical Applications: Agentic AI is particularly useful in environments that are unpredictable or constantly changing. Examples include autonomous vehicles, robotic process automation that adapts to changing workflows, and personal assistants that learn from user interactions to provide personalized recommendations. Traditional AI applications might include image recognition, data classification, and predictive analytics, where the environment is more stable and predictable.

Code Example: Consider a reinforcement learning scenario where an agentic AI learns to play a video game. The AI receives rewards based on its actions and adjusts its strategy to maximize those rewards over time. This is in contrast to traditional AI, which might be trained using a dataset of game states and corresponding actions but would not adapt beyond its initial training.

import gym
import numpy as np

# Example of a reinforcement learning environment
env = gym.make('CartPole-v1')

def simple_agent(observation):
    return 0 if observation[2] < 0 else 1

# Run the agent in the environment
observation = env.reset()
for _ in range(1000):
    env.render()
    action = simple_agent(observation)
    observation, reward, done, info = env.step(action)
    if done:
        observation = env.reset()

External References: Agent AI: Surveying the Horizons of Multimodal Interaction