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Data 360

2 posts about Data 360. Every summary links to the original.

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Salesforce
How Data 360 Builds Trusted Context: The Enduring Layer for Enterprise AI
Enterprise AI needs a way to assemble the smallest sufficient set of current, relevant, and authorized evidence for each agent turn instead of sending an entire data estate to a model. Salesforce presents Data 360 as a shared Trusted Context runtime spanning Salesforce, external platforms such as Databricks and Snowflake, operational systems, documents, knowledge bases, and live signals. Its progressive pipeline connects data through ingestion and Zero Copy, then harmonizes, unifies, models, retrieves, reconciles, governs, and compiles evidence into a token-fit Context Pack with citations and action contracts. The Agent Context Engine also returns typed interaction traces, promoting only approved facts into durable memory and validated outcomes into learnings. Q2 FY27 production usage is reported at 104.3 trillion connected records, including 81.5 trillion Zero Copy rows, while proposed benchmarking covers correctness, authorization, provenance, freshness, latency, cost, and memory continuity.

Salesforce
How AI Agents Get Trusted Customer Context with Data 360 Data Graphs
Salesforce’s AI Engineering team uses Data 360 data graphs to close the context gap that prevents agents from reliably personalizing customer interactions. The graphs join, aggregate, and apply business logic across fragmented identities, accounts, products, contracts, orders, cases, services, and entitlements, while partitioned data spaces provide a filtered customer-success view. The team designs smaller graphs or multi-graphs around agent access patterns, adds indices, supports semantic or keyword search, and curates context so agents can traverse extensible many-to-many relationships without receiving unnecessary data. Reusable deployment automation and validation practices helped deliver five major releases in six months, with integrations spanning Postman, Apex, Agentforce, and low-code or no-code workflows. Live monitoring later showed P50 performance below 200 milliseconds, compared with an earlier benchmark of about 400 milliseconds.

The best engineering reads, once a week.

Topic

2 posts about Data 360. Every summary links to the original.

Paid picks we use ourselves. Links carry a referral code — a subscription through one earns us a commission at no extra cost to you.

Newest first

Salesforce
How Data 360 Builds Trusted Context: The Enduring Layer for Enterprise AI
Enterprise AI needs a way to assemble the smallest sufficient set of current, relevant, and authorized evidence for each agent turn instead of sending an entire data estate to a model. Salesforce presents Data 360 as a shared Trusted Context runtime spanning Salesforce, external platforms such as Databricks and Snowflake, operational systems, documents, knowledge bases, and live signals. Its progressive pipeline connects data through ingestion and Zero Copy, then harmonizes, unifies, models, retrieves, reconciles, governs, and compiles evidence into a token-fit Context Pack with citations and action contracts. The Agent Context Engine also returns typed interaction traces, promoting only approved facts into durable memory and validated outcomes into learnings. Q2 FY27 production usage is reported at 104.3 trillion connected records, including 81.5 trillion Zero Copy rows, while proposed benchmarking covers correctness, authorization, provenance, freshness, latency, cost, and memory continuity.

Salesforce
How AI Agents Get Trusted Customer Context with Data 360 Data Graphs
Salesforce’s AI Engineering team uses Data 360 data graphs to close the context gap that prevents agents from reliably personalizing customer interactions. The graphs join, aggregate, and apply business logic across fragmented identities, accounts, products, contracts, orders, cases, services, and entitlements, while partitioned data spaces provide a filtered customer-success view. The team designs smaller graphs or multi-graphs around agent access patterns, adds indices, supports semantic or keyword search, and curates context so agents can traverse extensible many-to-many relationships without receiving unnecessary data. Reusable deployment automation and validation practices helped deliver five major releases in six months, with integrations spanning Postman, Apex, Agentforce, and low-code or no-code workflows. Live monitoring later showed P50 performance below 200 milliseconds, compared with an earlier benchmark of about 400 milliseconds.

The best engineering reads, once a week.