An Agentforce agent can have excellent instructions and still answer poorly if it does not have the right enterprise context. Grounding addresses that problem by providing relevant information at run time so the model can base its response on trusted business data rather than on general model knowledge alone. Salesforce Data 360 provides a deeper grounding path when organizations need to ingest, harmonize, index, retrieve, and govern data across structured and unstructured sources.<\/p>\n
This creates a natural bridge between the Salesforce Agentforce Specialist<\/a> and Salesforce Data 360 Consultant<\/a> domains. Agent builders need to understand what context the agent requires; data architects need to make that context retrievable, current, permission-aware, and meaningful.<\/p>\n Instructions tell the agent how to behave. Grounding gives it facts or context for the current task. An instruction can say, \u201cAnswer warranty questions using company policy.\u201d It cannot supply the current warranty policy unless that policy is included or retrieved as data.<\/p>\n Trying to solve missing data with larger prompts creates maintenance problems. Policies change, customer records change, products change, and account status changes. Grounding allows the runtime prompt to include information retrieved for the current request rather than embedding every fact in static configuration.<\/p>\n Grounding also narrows the evidence used for a response. If the agent should answer from approved knowledge and customer-specific context, retrieval can supply those sources while instructions define how to use them and what to do when the evidence is insufficient. This separation makes policy review easier because teams can audit both the behavioral instruction and the evidence source.<\/p>\n Salesforce provides Agentforce Data Library as a way to assign knowledge sources that can ground agent responses. Current documentation describes support for sources such as knowledge content, fields, file uploads, and web sources depending on configuration. The platform indexes the assigned content so the agent can retrieve relevant information.<\/p>\n This is useful when an organization needs a straightforward knowledge grounding experience without designing the full data pipeline itself. A service team might ground an agent in approved troubleshooting articles and product policies. The agent can retrieve the relevant passages when a user asks a question rather than relying only on the model\u2019s pretraining.<\/p>\n The library still requires content governance. Outdated, duplicated, contradictory, or poorly scoped documents produce poor retrieval. Grounding does not make bad knowledge trustworthy; it makes the agent better at finding the knowledge you supplied.<\/p>\n For advanced scenarios, Salesforce documents Retrieval Augmented Generation (RAG) in Data 360. The organization can bring data into Data 360, prepare it for search, create search indexes, configure retrievers, and use retrieved results to ground prompts and agents.<\/p>\n This path provides more control over ingestion, processing, data sources, and retrieval behavior. It can support structured customer information along with unstructured documents, long-text fields, conversation histories, and other enterprise content.<\/p>\n The architectural value is not simply \u201cmore data.\u201d It is the ability to decide which data is part of the grounding corpus, how it is prepared, which search behavior retrieves it, and how those results are exposed to the agent.<\/p>\n Current Data 360 documentation describes a preparation pipeline for unstructured retrieval that includes loading content, chunking it, vectorizing it, storing it in a vector data store, and creating a search index. A retriever then searches that index and returns relevant information for grounding.<\/p>\n Chunking matters because models rarely need an entire large document for one question. Good chunks preserve enough local context to answer a question without burying the relevant sentence in hundreds of unrelated paragraphs. Very small chunks may lose meaning; very large chunks reduce retrieval precision and consume more prompt context.<\/p>\n Index and retriever design also affect results. A search configuration optimized for exact product codes may behave differently from one optimized for natural-language policy questions. Evaluate retrieval with realistic user queries rather than assuming that content presence guarantees discoverability.<\/p>\n Agent grounding is not limited to documents. Structured customer, account, transaction, entitlement, product, or case data can provide the live context that turns a generic response into a useful business answer. The key is to retrieve only the records and fields appropriate to the current user and task.<\/p>\n For example, a return-policy document explains general eligibility, but the customer\u2019s order date and product category determine whether that policy applies. An agent may need both the policy text and structured order data. The answer becomes a combination of governed rules and current business state.<\/p>\n Do not flatten every record into a giant prompt. Retrieve the minimum context needed for the task. Smaller, relevant context is easier to reason over, cheaper to process, and less likely to expose unrelated sensitive data.<\/p>\n Data graphs can provide a fast related-data view.<\/strong> Salesforce Data 360 data graphs provide a flattened representation of related data that can be retrieved efficiently. For agent grounding, a data graph can assemble related customer context into a form that is easier to retrieve than traversing many relationships during every interaction.<\/p>\n This is useful when the answer depends on a connected view: a customer plus relevant orders, loyalty state, service history, or other modeled relationships. The graph should reflect a business question rather than becoming an indiscriminate dump of every related object.<\/p>\n As with any denormalized structure, freshness and ownership matter. Teams should know how quickly the graph reflects source changes and which system owns each attribute.<\/p>\n Freshness requirements should be defined per source. A policy document might be acceptable if it was updated last week, while an order-status answer may need data from seconds ago. One retrieval architecture should not assume that every source has the same acceptable age. Include timestamps or version context where the agent must distinguish current state from historical material.<\/p>\n Source authority matters too. If a knowledge base contains draft policies, community answers, and approved operating procedures, the retriever should not treat all of them as equally authoritative simply because they contain the same keywords. Metadata, filters, separate indexes, or other governance controls can help prioritize the sources the business has approved for agent use.<\/p>\n Identity resolution affects whether the context belongs to the right person.<\/strong> When Data 360 unifies information from multiple sources, identity resolution determines which source profiles belong to the same real-world entity. Grounding quality depends on that resolution. An agent can retrieve perfectly relevant data for the wrong merged profile and still produce a dangerously wrong answer.<\/p>\n Match rules and reconciliation therefore have an AI impact, not only a marketing or analytics impact. Organizations should understand the identifiers used to unify profiles, the confidence of those matches, and how conflicts between source values are resolved.<\/p>\n High-risk actions should not rely on fuzzy identity alone. Agent workflows may need explicit authentication or transaction-specific verification even when a unified profile exists.<\/p>\n Grounding should not become a side channel around the organization\u2019s access model. Sensitive data that a user should not see must not be surfaced simply because it exists in a search index. Design the retrieval layer with data spaces, permissions, source governance, and agent\/action security appropriate to the implementation.<\/p>\n Also classify the content. Public product documentation, internal operating procedures, customer financial information, and regulated personal data carry different disclosure risks. The same retrieval technique should not imply the same access policy.<\/p>\n For unstructured data, remove obsolete or superseded documents from active retrieval paths. Two policy versions that contradict each other can cause an agent to cite the wrong rule even when both documents were \u201ctrusted\u201d at some point.<\/p>\n Observability for retrieval should include the query, filters, source identifiers, retrieved passages or records, and enough ranking information to explain why a result was selected. Without that evidence, teams can see that the final answer was wrong but cannot tell whether the problem began in the corpus, the search configuration, or the generation step.<\/p>\n When an agent answers incorrectly, first ask whether the right evidence was retrieved. If the correct document or record never appeared in the grounding context, rewriting the response instructions may not solve the problem. Investigate ingestion, chunking, indexing, filters, search configuration, and the user\u2019s query.<\/p>\n If the correct evidence was retrieved but the response misused it, then the reasoning or instruction layer needs attention. Separating retrieval quality from generation quality makes debugging much faster.<\/p>\n Build evaluation sets that include straightforward queries, synonyms, product codes, ambiguous language, multi-document questions, and cases where no approved answer exists. A trustworthy agent must know when retrieval is weak enough to ask for clarification or escalate instead of inventing certainty.<\/p>\n Effective grounding requires a lifecycle: identify authoritative sources, ingest and prepare them, control access, measure retrieval, monitor freshness, remove obsolete content, and test how agent behavior changes as the corpus evolves. The best architecture assigns owners to both the agent and the data it depends on.<\/p>\nGrounding is different from writing better instructions<\/h2>\n
Agentforce Data Library is the simpler grounding path<\/h2>\n
Data 360 supports more controlled RAG architecture<\/h2>\n
The retrieval pipeline turns enterprise content into searchable context<\/h2>\n
Structured data can ground personalization and business state<\/h2>\n
Permissions and data governance still apply to AI retrieval<\/h2>\n
Evaluate retrieval separately from answer quality<\/h2>\n
Grounding is a data product, not a one-time connection<\/h2>\n