Agentic AI can adapt based on past outcomes, but this does not mean it “learns” in the same way humans or continuously trained models do. Most Agentic AI systems do not update their underlying model weights on the fly. Instead, they learn operationally by recording outcomes, feedback, and context, and using that information in future decision-making. This is often implemented as memory rather than training.
For example, if an agent attempts a sequence of actions that leads to an error, that outcome can be stored as a failure case. The next time the agent encounters a similar situation, it can retrieve that memory—often stored as embeddings in a vector database such as Milvus or Zilliz Cloud—and adjust its plan. This kind of learning is closer to experience replay or case-based reasoning than true model retraining.
From a system design perspective, developers decide what counts as a “mistake,” how feedback is captured, and how strongly it influences future behavior. This makes Agentic AI safer and more controllable, but also means improvement depends heavily on good logging, evaluation, and memory design. The agent becomes better over time not because its model changes, but because its decision context improves.