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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Salesforce positions Data 360 as a shared layer that assembles current, permission-aware evidence from distributed enterprise data and carries governed context into agent workflows. It is designed to connect existing data and systems to AI—not to make a model’s context window, or a chat transcript, trustworthy by itself.
What Data 360 contributes to enterprise AI
An AI agent can only give a reliable answer when it can find relevant evidence, interpret it correctly, and access it under the right permissions. Salesforce describes Data 360 as a distributed data and context foundation for that work, with an Enterprise AI Harness connecting context to governed action.
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The intended scope spans Salesforce applications, external data platforms such as Snowflake and Databricks, operational systems, documents, and knowledge bases. Instead of requiring every dataset to be copied into one repository, organizations can bring data in through ingestion or make it available through Zero Copy federation. Salesforce Architects describes the goal as making existing investments actionable through Salesforce, not replacing them wholesale.
That is an architectural proposition, not a guarantee of answer quality. A model does not create trusted context on its own: source accuracy, data meaning, indexing, current permissions, and operating controls all affect what an agent can retrieve and do.
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How distributed data becomes retrievable context
Salesforce’s architecture describes several cooperating layers: connectivity and federation; storage and compute; processing and intelligence; trust controls; unified query and retrieval; and context assembly. In practical terms, the layers connect sources, prepare them for use, find evidence relevant to a request, and pass that evidence to an agent or workflow.
Ingest or federate data
Ingestion brings data into the platform for processing and use there. Zero Copy federation lets participating data remain in its source environment while being accessed through a federated connection. Which route fits depends on data authority, freshness needs, source capabilities, governance, and workload. The architecture does not mean that all data is automatically available: source connections, models, policies, and implementation still have to be configured.
Prepare unstructured content for retrieval
Salesforce Help describes a retrieval flow in which content is loaded, divided into chunks, vectorized, and stored in a search index. A retriever searches that index for a particular use case and can supply relevant results to prompt templates, flows, or agents. Depending on the task, filters can narrow results by category, recency, or geography.
The Tool Desk
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Assemble evidence for the task
Retrieved material becomes useful context only when it is selected and presented in a form the agent can use. Salesforce describes context assembly as part of the shared foundation, while the Enterprise AI Harness connects that context to action. In an implementation, teams need to check whether the evidence actually answers the domain-specific question, whether it is fresh enough, and whether its source and meaning remain clear through the workflow.
How permissions and provenance fit into retrieval
Salesforce Help says retrievers follow configured data-governance policies and perform row-level access checks. Filters can constrain what is returned, and citation support can connect a generated response to relevant source content so a user can inspect the evidence. These mechanisms help preserve policy and make answers more checkable, but they depend on correct configuration and on the underlying policies being accurate and current.
Salesforce’s security architecture describes policy decision and enforcement components that apply access rules across APIs, the user interface, CRM enrichment, and retrieval-augmented generation (RAG). It also describes a shared-responsibility model: Salesforce secures and maintains the platform, while customers remain responsible for their data, configurations, and operating processes. An organization should therefore assess the full path from source access through retrieval to any resulting action, not just whether a chatbot displays a citation.
Salesforce presents identity, provenance, and governance as context travels across Salesforce, custom, partner, and external agent experiences. For an enterprise, the practical question is whether those details remain intact in the particular integrations and action paths it uses. Architecture-level intent should be validated against deployed flows and access policies.
Why working context, short-term memory, and long-term memory differ
Salesforce Engineering separates agent state into three forms. The distinction matters because active reasoning and a record of conversation are not automatically approved enterprise knowledge.
| State type | What it contains | How to treat it |
|---|---|---|
| Working context | The plan, evidence, tool state, and unresolved decisions for the current reasoning step. | Use it to support the current step; it is not, by itself, durable organizational memory. |
| Short-term memory | An active trace across turns, including events, tool calls, decisions, actions, and outcomes. | It can help an agent maintain continuity during a task or interaction. |
| Long-term memory | Approved facts, preferences, commitments, relationships, and prior work that remain useful beyond a session and belong to the enterprise. | Define approval and lifecycle controls before retaining it across sessions or agents. |
Salesforce Engineering puts the key distinction plainly: “A transcript records what happened; it does not automatically become enterprise memory.” A conversation may contain an unverified claim, a temporary instruction, or an outcome that should not be reused. Durable memory therefore needs a deliberate process for deciding what qualifies, who approves it, how it is corrected or retired, and which agents or channels may use it.
What Salesforce reports about production scale and latency
Salesforce reports the following Data 360 figures for Q2 FY27. They describe company-reported platform activity, not independently verified results.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problems| Reported measure | Salesforce-reported figure | Period and qualification |
|---|---|---|
| Connected records, including Zero Copy rows | 104.3 trillion total, including 81.5 trillion Zero Copy rows | Salesforce, Q2 FY27 |
| Records processed and query records | 12.7 quadrillion processed; 9.7 quadrillion query records | Salesforce, Q2 FY27 |
| Unstructured data and unified profiles | 22 terabytes of unstructured data; 25.3 billion unified profiles | Salesforce, Q2 FY27 |
| Activated records and action events | 2 trillion activated records; 41 billion action events | Salesforce, Q2 FY27 |
| Production Agentic RAG and semantic-plus-keyword search across structured and unstructured data | 126 ms p50; 352 ms p90 latency | Salesforce, Q2 FY27 article reporting; production workload |
| Separate high-concurrency query test | Approximately 40 ms median query latency at approximately 750 queries per second | Salesforce, Q2 FY27 article reporting; high-concurrency test |
The latency figures describe different workloads and should be treated as complementary vendor-reported measurements, not compared as though they came from one test. They are not an independent benchmark or a universal service guarantee. Actual performance depends on workload, data, query patterns, concurrency, policies, and implementation; teams should measure their own retrieval and query paths under realistic conditions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate whether the architecture fits your use case
“Trusted context” is not a single switch. Evaluate the design against the actual task, data sources, permissions, and agent experience you intend to deploy.
- Data authority and movement: Identify which systems remain authoritative and which data should be ingested versus accessed through Zero Copy federation.
- Coverage and meaning: Verify that the required structured records and unstructured formats can be represented and searched in ways that fit the task.
- Retrieval relevance: Test chunking, indexing, keyword or vector search, hybrid retrieval, filters, and any reranking against questions from your domain. Inspect whether returned evidence supports the answer rather than merely resembling the query.
- Access controls: Trace identity, row- and field-level permissions, consent, and policy decisions through every retrieval and action path.
- Freshness: Measure how quickly source changes become available to retrieval, and determine whether recency filters or streaming paths meet the use case.
- Memory lifecycle: Specify who may approve durable facts, how corrections and removals work, and how unverified conversation content is prevented from silently becoming enterprise memory.
- Portability and observability: Check whether identity, provenance, context, and outcomes carry across the Salesforce, custom, partner, and external agents you actually use.
- Performance under real workloads: Measure retrieval latency, concurrency, SQL queries, and graph queries separately with your data, policies, and expected load.
These checks distinguish what the architecture is designed to support from what a particular deployment demonstrates. Salesforce’s materials describe capabilities and intended design; they do not establish a neutral vendor comparison or an independently validated customer outcome.
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