Learning from the Loop
Unlike traditional AI models that are "frozen" after training, agentic systems learn through "Episodic Feedback." Every time an agent completes a task, it reflects on the outcome. If a human corrects the agent, that correction is stored in its **Episodic Memory**. The next time the agent faces a similar situation, it retrieves that memory and adjusts its behavior accordingly.
Continual Improvement
This "On-the-Job Learning" allows agents to adapt to the specific nuances of a company or a user. Over months of operation, the agent becomes increasingly "Wise"--recognizing patterns, anticipating needs, and autonomously refining its own internal prompts and strategies to achieve better outcomes.
Conclusion
Experience is the ultimate teacher. By giving agents the ability to learn from their own actions and human feedback, we are building intelligence that doesn't just process data, but truly grows.