Unverified50% confidenceFactExact time
The system splits the Agent architecture into a memory layer (recording structured experience: tasks, actions, results and verification evidence) and a decision layer (providing typed probabilistic decisions), with the boundary between the two layers being: memory answers "is this true", and decision answers "what should we do and how certain are we".
1
Sources
50%
Confidence
Long-term
Relevance
10/14/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedPlanning refers to an Agent breaking down a big goal into an executable sequence of subtasks, with common approaches including task decomposition, prioritization, and dynamic replanning; the memory mechanism allows an Agent to maintain contextual coherence across multiple turns of interaction, with short-term memory usually being the conversation history and long-term memory relying on external storage such as vector databases.77% similarUnverified随着底层模型能力趋于饱和，记忆基础设施正在成为评估Agent的第三个关键维度，此前两个维度是模型能力和工具能力76% similarUnverified可商用的Agent系统需具备五大基础运行机制：任务拆解、事件循环、状态记忆、工具调用、权限闸门76% similarVerifiedIn the era of large models, within an Agent architecture, the LLM acts as the brain responsible for reasoning and decision-making, tools act as the limbs responsible for interacting with the external world, and the memory module stores conversation history and intermediate states.75% similarVerifiedAn Agent in the LLM context typically has four core components: perception, planning, action, and memory75% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1030830API
curl https://kongchang.com/api/v1/knowledge/claims/1030830MCP
get_claim(id=1030830)