What is an Observation-Action Loop?
A core pattern in agentic AI where an agent observes the environment, reasons, and acts repeatedly to accomplish tasks.
More about Observation-Action Loop:
The Observation-Action Loop is a fundamental pattern in agentic AI systems. The agent continually observes its environment, reasons about the next best action (using components like a reasoning engine), performs the action, then observes the results, repeating the cycle.
This loop underpins agentic workflows, supports autonomous agents, and is critical for self-improving or adaptive AI.
Frequently Asked Questions
Why is the observation-action loop important?
It enables agents to iteratively adapt, self-correct, and optimize their behavior in dynamic environments.
What are typical use cases for observation-action loops?
They are foundational in robotics, virtual assistants, automated customer support, and advanced agentic workflow applications.
From the blog
How to Train ChatGPT With Your Own Website Data
Training ChatGPT with your own data can provide the model with a better understanding of your unique context, allowing for more accurate and relevant responses.
Herman Schutte
Founder
Using AI to make learning personal and increase your online course sales
Incorporating AI into your courses allows you to create a personalized learning environment that adapts to each student's needs. This personal touch doesn't just improve the learning experience; it also makes your courses more attractive and can increase sales. Let's explore how AI can make online courses more personal and commercially successful.
Herman Schutte
Founder