How does Data Cloud serve as memory and context management for Agentforce agents?

LLMs have a finite context window — the total amount of text (input + output) they can process in a single call. For agents handling long conversations or needing broad customer history, the context window is a critical constraint. Data Cloud addresses this as an agent memory layer:

1. Unified Customer Profile as Context: Data Cloud's identity resolution unifies customer data from multiple sources into a single profile. Relevant profile attributes (purchase history, service history, preferences, lifetime value) can be retrieved and injected into the agent's context at conversation start, providing personalisation without retrieving full raw records.
2. Vector Database for Semantic Memory: Conversation history and interaction data stored in Data Cloud as vector embeddings enables semantic retrieval of relevant past interactions — "the last time this customer called about billing" — without loading the entire history into the context window.
3. Context Window Management: By retrieving only the most semantically relevant chunks (via RAG) rather than all available data, the agent stays within context limits while still being well-grounded.
4. Persistent Memory: Unlike in-session agent memory which resets between conversations, Data Cloud can store and retrieve information across sessions — enabling agents to remember customer preferences and past resolutions.