Persistent context that lets agents retain preferences, decisions, and prior work.
1 min read
/ quick answer
Agent Memory is the stored state an AI system can retrieve across sessions: user preferences, previous outputs, project facts, operational rules, and feedback loops. Persistent context that lets agents retain preferences, decisions, and prior work.
Persistent context that lets agents retain preferences, decisions, and prior work. Agent Memory is the stored state an AI system can retrieve across sessions: user preferences, previous outputs, project facts, operational rules, and feedback loops. In practice: A research agent remembers the company ICP, preferred competitor categories, and rejected sources before generating the next market scan. This dictionary node is part of the Onexial knowledge graph and links to related concepts, workflows and tools below.
Definition
Agent Memory is the stored state an AI system can retrieve across sessions: user preferences, previous outputs, project facts, operational rules, and feedback loops.
Example
A research agent remembers the company ICP, preferred competitor categories, and rejected sources before generating the next market scan.
Agent Memory is the stored state an AI system can retrieve across sessions: user preferences, previous outputs, project facts, operational rules, and feedback loops.
What is an example of Agent Memory?
A research agent remembers the company ICP, preferred competitor categories, and rejected sources before generating the next market scan.
Why does Agent Memory matter for AI and automation?
Persistent context that lets agents retain preferences, decisions, and prior work. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.