Salesforce Developers BlogYou will learn how to deploy Python-based Code Extension functions to override default chunking in Data 360 search indexes. The guide demonstrates how structure-aware splitting preserves table headers and speaker labels, resulting in more accurate Agentforce responses.
Default character or token splitters fracture complex enterprise documents like financial reports and call transcripts, causing Agentforce to retrieve contextless chunks. The article explains how Data 360 Code Extension functions allow developers to run custom Python scripts within the Salesforce trust boundary to implement tailored chunking logic.
For fragmented data tables, standard chunking strips column headers, leaving raw numbers meaningless to the model. A custom function detects table boundaries and repeats header rows across sub-chunks, enabling the agent to correctly map values to metrics like revenue and operating margins.
Multispeaker dialogues suffer when speaker turns merge into massive unstructured blocks. By applying a sliding window approach with defined turn counts and character limits, adjacent chunks overlap to preserve conversational context. The author validates these techniques by querying both native and custom vector indexes and comparing the resulting Agentforce answers against identical user prompts.
By Chandan AgarwalYou will learn how to structure, validate, and deploy Python scripts as managed Code Extensions within Data 360. The guide also covers connecting these scripts to batch data transforms and monitoring execution through the platform.
Northstar Outfitters faced inconsistent product data across commerce systems and needed a trusted catalog. Native visual transforms could not handle the required standardization, enrichment, and scoring logic, prompting the use of Data 360 Code Extension to run modular Python and PySpark scripts directly on Salesforce infrastructure.
The solution relies on isolated runtime environments where scripts read from and write to designated Data Lake Objects using explicit permissions defined in a configuration file. Developers validate logic locally against sampled data before packaging the script, dependencies, and tests into a deployable archive.
The guide details both Setup interface and Salesforce CLI workflows for uploading packages, selecting compute sizes, and linking the extension to a batch data transform. Execution, scheduling, and logging are handled by the platform, while operators can trigger runs and review history through REST API calls.
By Scott NybergYou will learn how to separate app instrumentation from campaign configuration so marketers can update experiences without app releases. The piece also explains how to maintain consistent identity resolution and dynamic native rendering across iOS, Android, React Native, and Flutter using server-side decisioning.
The team built a unified architecture to deliver real-time mobile personalization across iOS, Android, React Native, and Flutter. They solved platform fragmentation by establishing consistent schemas and event semantics while allowing each technology stack to use its native rendering capabilities. A bridge layer enables React Native and Flutter to access native APIs through iOS and Android, preserving a single SDK interface for developers.
Developers define stable content zones and register approved native components once. Marketers then configure templates, targeting, and component selection through the Salesforce user interface. The platform serves this metadata via a global CDN, and the mobile SDK applies the rules to dynamically render the correct native element without requiring new app builds.
Real-time identity resolution relies on Data 360 to merge anonymous web, email, and mobile interactions into a single profile during authentication. Decisioning runs entirely on the server, so the SDK only receives final experience instructions rather than raw customer data. Teams can validate targeting and layout using a QR code preview flow that injects live profile attributes into the simulator before launch.
By Raveendrnathan Loganathan and Tobias MuehlbauerReaders will understand how the Agent Context Engine retrieves, reconciles, and packages authorized evidence into token-efficient Context Packs for AI agents. The piece also explains how Data 360 separates runtime state from durable memory to maintain governance across models and channels.
Enterprises face a significant context problem when feeding AI agents because loading all enterprise data into prompts wastes tokens, increases latency, and risks exposing sensitive information. Salesforce addresses this with Data 360, which acts as a shared runtime foundation to compile current, relevant, and authorized evidence without duplicating the entire data estate across platforms.
The Agent Context Engine operates bidirectionally to assemble token-fit Context Packs for outbound agent turns while returning typed interaction traces for auditing on the inbound path. It manages four distinct memory states, separating temporary working context and short-term traces from approved long-term memory and distilled learnings to ensure only validated facts persist across sessions.
The platform supports a distributed enterprise model where data remains authoritative in external systems like Databricks and Snowflake while participating through ingestion or Zero Copy federation. Pro-code agents can integrate via SDKs and API adapters, while third-party assistants connect through standard protocols, all unified under the Enterprise AI Harness for consistent governance, security, and observability.
By Scott NybergReaders will learn how the engineering team partitions and indexes Data 360 data graphs to resolve complex customer identities while maintaining strict data isolation. You will also see the architectural tradeoffs used to achieve sub-two-hundred millisecond response times for Agentforce interactions.
The engineering team built Data 360 data graphs to solve the context gap for Agentforce by unifying fragmented customer data across accounts, entitlements, and products. Instead of running multiple queries at runtime, the system pre-aggregates relationships into cohesive data products that agents can call directly.
Resolving customer identity required a partitioned architecture that keeps the broader identity graph separate from customer success views. This ensures that prospect data and information belonging to different tenants remain strictly isolated while still supporting complex many-to-many relationships.
Performance was optimized by analyzing access patterns, designing smaller multi-graphs, and building targeted indices to avoid full table scans. The team shifted from static data models to flexible structures that support semantic search and unpredictable agent queries, ultimately achieving median response times under two hundred milliseconds without dedicated autoscaling infrastructure.
By Ross CollieIt explains how Agentforce Coworker fits into the AIforce stack, how its routing and security model work, and gives a concrete setup path via Data 360 and Salesforce Go.
Agentforce Coworker is presented as one of three pillars of AIforce, the interface layer announced at Dreamforce ’26 that sits on top of Agentforce, Customer 360 and Data 360. Its purpose is to reduce swivel-chairing between applications and to act as a router for a growing agentic workforce, so users do not have to know which agent handles which task. It offers three interaction modes: Find for natural-language queries across CRM, Slack, files, knowledge bases and 270+ connected sources; Catch Up for proactively surfacing changes that occurred while the user was away; and Plan and Act for outcome-level instructions that Coworker orchestrates across agents.
The article describes the trust model as reusing the existing Salesforce metadata-driven security: Profiles, Permission Sets and Sharing Rules still apply, so the agent operates under the same least-privilege constraints as users. Setup requires Data 360 to be enabled first, since it indexes CRM data and enforces governance, then assigning the Agentforce Coworker Admin Permission Set, enabling the feature through Salesforce Go, optionally granting additional data sources under Manage Data, and finally assigning the Agentforce Coworker User Permission Set to end users. The author cautions that orgs need clean data and properly configured permissions before enabling it, and notes the user-facing entry point is a new Ask button next to global search in Lightning Experience, with availability also in Microsoft Teams.