The agent keeps prior conclusions and updates them instead of recomputing from scratch.
The agent stops drifting as it accumulates judgement over time.
The same inputs produce the same decisions across executions.
Incorrect conclusions get contradicted once and don’t repeat.
User-specific context stays isolated while still shaping the agent’s decisions.
What one agent learns can be shared and applied by others without retraining.