Agentic AI learns from past processes by recording actions, results, and critique in memory. It reviews successes and mistakes, extracts patterns, and upgrades goals, plans, and heuristics. This continual reflection enables adaptation, skill transfer, improved decision making, and greater autonomy across future, similar or novel tasks in dynamic learning environments.