Salesforce Agentforce agents struggle with multi-turn workflows because session history is bounded by context windows and lacks structure. Neo4j Agent Memory Service (NAMS) provides a graph-backed external memory layer that persists conversations, entities, observations, and decisions across sessions. The integration uses Salesforce External Services, Flow, Agent Actions, and Prompt Templates to store and recall context without custom Apex code. A graph model is well-suited because enterprise context is relational — users, companies, accounts, decisions, and preferences form a connected network. The recommended starting point is a four-step pattern: create a memory conversation, recall context before routing, store user input, and store the assistant response after the business action completes.
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How graph-backed memory can help Agentforce agents carry context across longer enterprise workflowsWhy Agentforce Workflows Need MemoryMemory Should Be Structured, Not Just Longer Prompt TextThe Integration PatternGet Jakub Marchwicki’s stories in your inboxHow Memory Changes The AgentWhat The Salesforce Implementation UsesWhy A Graph Memory Layer HelpsWhat To Try FirstClosing ThoughtsResourcesSort: