What is agentic loop?
A process where an AI system's actions influence its own goals or decision-making
agentic loop explained in plain English
An agentic loop occurs when an AI system's behavior affects its own objectives, creating a self-reinforcing cycle. This can happen when the AI's actions change its environment in a way that, in turn, changes its goals or priorities.
Analogy
Imagine a thermostat that not only controls the temperature but also adjusts its own target temperature based on the current temperature. If it gets too hot, it might decide to aim for an even lower temperature, which in turn affects its heating and cooling actions.
Example
A self-driving car that adjusts its route based on real-time traffic data, which in turn affects its navigation goals and decision-making.
How is agentic loop used?
Agentic loops are used in AI research to create more autonomous and adaptive systems. They can be applied in areas like robotics, game playing, and decision-making under uncertainty.
Common misconceptions about agentic loop
History
The concept of agentic loops has its roots in the field of artificial intelligence and cognitive science, where researchers have long explored the idea of autonomous agents that can adapt and learn from their environment.
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