According to AI, even Agentic AI isn’t self-learning beyond an agent-based model

Question to ChatGPT: Is agentic AI self-learning

ANSWER:

Agentic AI is generally designed to be self-directed and autonomous, with the ability to act independently within certain parameters. While it may include self-learning capabilities, not all implementations of agentic AI are fully self-learning by default. However, when agentic AI includes machine learning or adaptive algorithms, it can adjust its behavior based on feedback from its environment, thus engaging in a form of self-learning.

Key Characteristics of Self-Learning in Agentic AI:

1. Learning from Environment Feedback: An agentic AI system with self-learning capabilities can collect data from its actions and adjust its future behaviors to optimize outcomes.
2. Continuous Improvement: When self-learning is incorporated, agentic AI can continuously refine its strategies and responses to become more effective over time.
3. Autonomy with Adaptability: While all agentic AI is designed to be autonomous, self-learning enables it to make not just preprogrammed or rules-based decisions but also improve on those decisions based on new data and scenarios.

Examples of Self-Learning Agentic AI Applications:

• Robotics in dynamic environments, such as autonomous drones or industrial robots that adjust to new tasks.
• Financial Trading Bots that adapt strategies based on market trends.
• Supply Chain Management Systems that optimize routing and logistics based on real-time data.

In essence, Agentic AI can be self-learning if it is equipped with machine learning algorithms that allow it to learn from past experiences or environmental interactions. This gives it a high degree of flexibility, making it suitable for complex, changing environments.

TODAY’S TAKEAWAY

Never lead with technology now or in the future! Use an Agent-Based development and implementation model.

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