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.
