BrowseAll ecosystems
Everything in the atlas
15 of 2341
Domain
Signals: Last 6 months
Content: 2
More
Group by: Capability
EntryBySignalsLast activity
Agentic AI & AI AgentsAgentic AI & AI AgentsSAP's target picture for AI agents: Joule as the engagement layer — Joule Work, Joule Assistants, Joule Agents — with custom agents from customers, partners and SAP teams connecting through A2A for agent-to-agent collaboration and MCP for tool connectivity, on the SAP Business AI Platform. It says up front that parts are not generally available yet, in particular bidirectional communication with third-party agents through the Agent Gateway, so it reads as direction with the building blocks named rather than a design to copy today.published 2026-08-27
AI-native North Star architecture — Platform LayerAI-native North Star architecture — Platform LayerThe platform layer of SAP's North Star architecture, the chapter that says where agents run and what a developer builds them with. It names an AI Golden Path built on the SAP Cloud Application Programming Model, the ABAP Cloud development model with RAP and the SAP Fiori design system, and a managed agent runtime on SAP BTP that is designed to provide software development kits for building agents in any framework, an agent sandbox, container-hosted execution, a skills registry and continuous evaluations, which is the ground agent tooling covers. Agents are extended without changing their code through four patterns, the first of them tool extensibility through the Model Context Protocol, the concern MCP servers is named for; the process-layer chapter adds that a pro-code path with a standardised agent SDK and a prompt-driven path through the Joule Studio solution produce the same code artifact on the same runtime. It describes a design SAP intends rather than products that exist, and the SDK and the registry it names have no entry here yet.published 2026-05-13
AI-native North Star architecture — Platform Layer
Agent tooling changed in @cap-js/agents 0.9.1, 2026-08-14. Affects A reference.
CAP in the age of AI #1 - the MCP adapterCAP in the age of AI #1 - the MCP adapterThe episode that opens the series and the first pass over @cap-js/mcp, with four of the people building it on the call. Two of the adapter's tools are worked through: describe, the one an MCP client reaches for first to get the overview of what it may do, and query, which the panel establishes as read-only. It also writes the server entry into a local agent's own configuration file, which episode three comes back to as auto-wiring.published 2026-08-07
CAP in the age of AI #2 - more on the MCP adapterCAP in the age of AI #2 - more on the MCP adapterWhat the query tool costs in tokens, and the third tool @cap-js/mcp registers. Counted against a model running locally under llama.cpp, the same query is 26 tokens as CQL and 173 as CQN, and the tool definition sent with every system prompt drops from around 5,500 tokens to around 600 — the reason CQL became the default format. The call tool then exposes a service's unbound actions and functions, as one generic tool or, behind a flag, one tool per action with its own description and input schema; a question from the chat about extension points gets the answer that the adapter does the tools, which the panel calls a small subset of the protocol.published 2026-08-14
CAP in the age of AI #3 - from MCP to A2ACAP in the age of AI #3 - from MCP to A2AThe episode that finishes @cap-js/mcp with authorisation and then crosses to agents. @requires and @restrict are the same annotations CAP already has, and the plugin reads them to filter the tool list and the describe output per user and per request, so a model is not told about entities the caller cannot reach — where the OData metadata of the same service lists every exposed entity regardless. The second half annotates a service with @agent from @cap-js/agents, which serves it over A2A with an agent card and a chat preview, runs first against a mocked response and then against SAP AI Core bound with cds bind and a hybrid profile, and reuses the MCP adapter's tools rather than defining new ones.published 2026-08-21
CAP in the age of AI #5 - agents wrap-upCAP in the age of AI #5 - agents wrap-upThe agents topic closed out on what sits around the agent rather than inside it, now that the documentation has moved into capire. The chat preview's session is persisted in the database rather than kept in memory, traces go out through @cap-js/telemetry to a self-hosted MLflow under the OpenTelemetry conventions for generative AI, and the panel is explicit that @agent reuses the MCP adapter's code programmatically without serving an MCP endpoint. It ends in the xtravels sample, where a travel agent delegates to a hotel-service sub-agent and reaches a separately started flight service over MCP through the service binding CAP resolves anyway. Earlier in the hour the same agent answers from a model running on the presenter's laptop, reached as an OpenAI-compatible endpoint.published 2026-10-02
CAP in the age of AI #7 - vector embeddings wrap-upCAP in the age of AI #7 - vector embeddings wrap-upThe same embeddings model taken from SQLite to SAP HANA Cloud, then handed to an agent. Locally it is @cap-js/ai that adds the vector functions SQLite lacks and downloads an all-MiniLM L6v2 ONNX model on first start; on HANA natural language processing has to be switched on, the model name is a mandatory argument, the vectors it returns are 768 dimensions, and dimensions must match on both sides of a comparison. HANA itself can be given SAP AI Core as a remote source for other embedding models, which a CDS.ql expression has to inject because the postfix notation only takes what the CDS model holds, and the hour ends with a search function over cosine similarity exposed over @cap-js/mcp, so an agent asked about low output from solar panels calls semantic search instead of writing the query itself.published 2026-10-16
HANA AI Toolkit — Local MCP ServerHANA AI Toolkit — Local MCP ServerAn MCP server that turns the hana-ml Python library into governed tools an agent can call against a HANA Cloud instance, shipped in the hana-ai toolkit and exposed over stdio, SSE and streamable HTTP. Its one firm rule is attributable execution: an audit module writes the MCP-side identity — agent, model, session, tool, redacted arguments — into the HANA connection before any SQL runs, and fails the call if that write fails. A worked example of governance on an MCP tool surface, from SAP's data side rather than CAP's.published 2026-06-15
Explore your Hyperscaler data with SAP Business Data CloudExplore your Hyperscaler data with SAP Business Data CloudSAP's map of the ways data held on hyperscaler and partner platforms is joined with SAP data in SAP Business Data Cloud, with a sub-page each for AWS, Azure, Google Cloud, Databricks and Snowflake. It sets four modes side by side: BDC Connect shares data products in both directions over the Delta Sharing protocol from the object store layer, while in the SAP Datasphere layer federation reads remote tables in place, Replication Flows copy SAP data out to stores such as Amazon S3 or Google BigQuery, and Data Flows import files and transform them into Datasphere tables, the last three being the ground loading a warehouse covers. The Snowflake and BigQuery connectors are dated to 2026 releases, and a closing page lists what slows federation, from source and tenant in different regions to queries that do not filter and aggregate at the source.published 2026-05-08
Explore your Hyperscaler data with SAP Business Data CloudExplore your Hyperscaler data with SAP Business Data CloudSAP's map of the ways data held on hyperscaler and partner platforms is joined with SAP data in SAP Business Data Cloud, with a sub-page each for AWS, Azure, Google Cloud, Databricks and Snowflake. It sets four modes side by side: BDC Connect shares data products in both directions over the Delta Sharing protocol from the object store layer, while in the SAP Datasphere layer federation reads remote tables in place, Replication Flows copy SAP data out to stores such as Amazon S3 or Google BigQuery, and Data Flows import files and transform them into Datasphere tables, the last three being the ground loading a warehouse covers. The Snowflake and BigQuery connectors are dated to 2026 releases, and a closing page lists what slows federation, from source and tenant in different regions to queries that do not filter and aggregate at the source.published 2026-05-08
Transforming Enterprise Data Strategy with SAP Business Data CloudTransforming Enterprise Data Strategy with SAP Business Data CloudSAP's layered picture of SAP Business Data Cloud, from lake storage and compute up to a knowledge core and agents, with eight sub-pages that each work one part through. The data products page is the cataloguing and lineage part: datasets described by Open Resource Discovery metadata, listed in the BDC catalog and shared over Delta Sharing to SAP Databricks and SAP HANA Cloud, with SAP Datasphere as a consumer on the intelligent content page; other pages install intelligent content, bring SAP BW data in through the Data Product Generator, provision groups by SCIM from Cloud Identity Services into SAP Databricks, which is identity provisioning, and train predictive models in SAP Databricks that SAP AI Core then serves for batch and real-time scoring. The main page states direction more than mechanism, and the BW page marks SQL Share for derived and OData for analytical data products as to be available.published 2026-05-12
CAP in the age of AI #6 - vector embeddingsCAP in the age of AI #6 - vector embeddingsVector embeddings put to work in the incident management sample, answering whether this problem has been seen before. An embedding element of type Vector is calculated and stored from the vector_embedding function, so the database refreshes it whenever the text behind it changes, and cosine_similarity compares it with a freshly embedded string; because a title says little, a scheduled action first summarises each incident's conversation through a model on SAP AI Core and the embedding is taken from that summary. The similar-incidents list is a view joining incidents to themselves, with order and limit set in a before handler on the expanded query, and the vector element excluded from the service, since OData knows nothing about vectors — on SAP HANA Cloud the function is native, while SQLite needs packages added to the project.published 2026-10-09
CAP in the age of AI #7 - vector embeddings wrap-upCAP in the age of AI #7 - vector embeddings wrap-upThe same embeddings model taken from SQLite to SAP HANA Cloud, then handed to an agent. Locally it is @cap-js/ai that adds the vector functions SQLite lacks and downloads an all-MiniLM L6v2 ONNX model on first start; on HANA natural language processing has to be switched on, the model name is a mandatory argument, the vectors it returns are 768 dimensions, and dimensions must match on both sides of a comparison. HANA itself can be given SAP AI Core as a remote source for other embedding models, which a CDS.ql expression has to inject because the postfix notation only takes what the CDS model holds, and the hour ends with a search function over cosine similarity exposed over @cap-js/mcp, so an agent asked about low output from solar panels calls semantic search instead of writing the query itself.published 2026-10-16
Explore your Hyperscaler data with SAP Business Data CloudExplore your Hyperscaler data with SAP Business Data CloudSAP's map of the ways data held on hyperscaler and partner platforms is joined with SAP data in SAP Business Data Cloud, with a sub-page each for AWS, Azure, Google Cloud, Databricks and Snowflake. It sets four modes side by side: BDC Connect shares data products in both directions over the Delta Sharing protocol from the object store layer, while in the SAP Datasphere layer federation reads remote tables in place, Replication Flows copy SAP data out to stores such as Amazon S3 or Google BigQuery, and Data Flows import files and transform them into Datasphere tables, the last three being the ground loading a warehouse covers. The Snowflake and BigQuery connectors are dated to 2026 releases, and a closing page lists what slows federation, from source and tenant in different regions to queries that do not filter and aggregate at the source.published 2026-05-08
SAP Developer New, July 3rd 2026SAP Developer New, July 3rd 2026A "Did You Know" segment in this SAP Developer News video walks through schema evolution: CAP's ability to add a new field to an entity, or make a compatible change to an existing one, then redeploy the model with no data loss or manual migration. The rest of the July 2026 episode covers that month's Developer Challenge, a SAP HANA Cloud Q2 release announcement and a UI5 survey call, none explained in substance here.published 2026-07-02
Agentic AI & AI AgentsAgentic AI & AI AgentsSAP's target picture for AI agents: Joule as the engagement layer — Joule Work, Joule Assistants, Joule Agents — with custom agents from customers, partners and SAP teams connecting through A2A for agent-to-agent collaboration and MCP for tool connectivity, on the SAP Business AI Platform. It says up front that parts are not generally available yet, in particular bidirectional communication with third-party agents through the Agent Gateway, so it reads as direction with the building blocks named rather than a design to copy today.published 2026-08-27
AI-native North Star architecture — Platform LayerAI-native North Star architecture — Platform LayerThe platform layer of SAP's North Star architecture, the chapter that says where agents run and what a developer builds them with. It names an AI Golden Path built on the SAP Cloud Application Programming Model, the ABAP Cloud development model with RAP and the SAP Fiori design system, and a managed agent runtime on SAP BTP that is designed to provide software development kits for building agents in any framework, an agent sandbox, container-hosted execution, a skills registry and continuous evaluations, which is the ground agent tooling covers. Agents are extended without changing their code through four patterns, the first of them tool extensibility through the Model Context Protocol, the concern MCP servers is named for; the process-layer chapter adds that a pro-code path with a standardised agent SDK and a prompt-driven path through the Joule Studio solution produce the same code artifact on the same runtime. It describes a design SAP intends rather than products that exist, and the SDK and the registry it names have no entry here yet.published 2026-05-13
AI-native North Star architecture — Platform Layer
Agent tooling changed in @cap-js/agents 0.9.1, 2026-08-14. Affects A reference.
CAP in the age of AI #3 - from MCP to A2ACAP in the age of AI #3 - from MCP to A2AThe episode that finishes @cap-js/mcp with authorisation and then crosses to agents. @requires and @restrict are the same annotations CAP already has, and the plugin reads them to filter the tool list and the describe output per user and per request, so a model is not told about entities the caller cannot reach — where the OData metadata of the same service lists every exposed entity regardless. The second half annotates a service with @agent from @cap-js/agents, which serves it over A2A with an agent card and a chat preview, runs first against a mocked response and then against SAP AI Core bound with cds bind and a hybrid profile, and reuses the MCP adapter's tools rather than defining new ones.published 2026-08-21
CAP in the age of AI #4 - more on agentsCAP in the age of AI #4 - more on agentsAsking a human first, and telling the agent how to behave. Ordering a book through @cap-js/agents happens without a question being asked; the human-in-the-loop annotation on the action turns the same request into an approve-or-reject step, which the episode follows down to the A2A status update that stores the task in an input-required state in the database — A2A itself being JSON-RPC 2.0 over HTTP with server-sent events for the stream. The rest is instructions on disk: an agents.md in the service's own folder removes the question about how many copies to order, a skill under a skills folder is advertised by its description and read only when the agent decides the case applies, and either file makes the plugin build a deep agent instead of the plain one.published 2026-09-11
CAP in the age of AI #5 - agents wrap-upCAP in the age of AI #5 - agents wrap-upThe agents topic closed out on what sits around the agent rather than inside it, now that the documentation has moved into capire. The chat preview's session is persisted in the database rather than kept in memory, traces go out through @cap-js/telemetry to a self-hosted MLflow under the OpenTelemetry conventions for generative AI, and the panel is explicit that @agent reuses the MCP adapter's code programmatically without serving an MCP endpoint. It ends in the xtravels sample, where a travel agent delegates to a hotel-service sub-agent and reaches a separately started flight service over MCP through the service binding CAP resolves anyway. Earlier in the hour the same agent answers from a model running on the presenter's laptop, reached as an OpenAI-compatible endpoint.published 2026-10-02
SAP Document AISAP Document AISAP's reference architecture for SAP Document AI, first as SAP composes the service on BTP for document extraction: OCR, pretrained models, customer-defined schemas and LLM calls through SAP AI Core and its Prompt Registry in one pipeline, with tenant-isolated persistence in SAP HANA Cloud, PostgreSQL and an object store. Its scenario pages then lay out a solution in three layers: intake through Outlook and SharePoint inbound channels, the workspace UI, the Joule Work mobile app or the upload API; classification and extraction with a confidence score per field, automatic confirmation above a threshold and review of the rest in the workspace; and posting through outbound channels, directly to a REST API, through an SAP Integration Suite flow, or through a CAP service that maps the fields and calls SAP S/4HANA over OData. Enrichment beyond the service's own master data lookup is left to a CAP application or an Integration Suite flow triggered from an outbound channel, and direct API posting leaves error handling, retries and monitoring to the application.published 2026-06-03
CAP in the age of AI #3 - from MCP to A2ACAP in the age of AI #3 - from MCP to A2AThe episode that finishes @cap-js/mcp with authorisation and then crosses to agents. @requires and @restrict are the same annotations CAP already has, and the plugin reads them to filter the tool list and the describe output per user and per request, so a model is not told about entities the caller cannot reach — where the OData metadata of the same service lists every exposed entity regardless. The second half annotates a service with @agent from @cap-js/agents, which serves it over A2A with an agent card and a chat preview, runs first against a mocked response and then against SAP AI Core bound with cds bind and a hybrid profile, and reuses the MCP adapter's tools rather than defining new ones.published 2026-08-21
CAP in the age of AI #5 - agents wrap-upCAP in the age of AI #5 - agents wrap-upThe agents topic closed out on what sits around the agent rather than inside it, now that the documentation has moved into capire. The chat preview's session is persisted in the database rather than kept in memory, traces go out through @cap-js/telemetry to a self-hosted MLflow under the OpenTelemetry conventions for generative AI, and the panel is explicit that @agent reuses the MCP adapter's code programmatically without serving an MCP endpoint. It ends in the xtravels sample, where a travel agent delegates to a hotel-service sub-agent and reaches a separately started flight service over MCP through the service binding CAP resolves anyway. Earlier in the hour the same agent answers from a model running on the presenter's laptop, reached as an OpenAI-compatible endpoint.published 2026-10-02
CAP in the age of AI #6 - vector embeddingsCAP in the age of AI #6 - vector embeddingsVector embeddings put to work in the incident management sample, answering whether this problem has been seen before. An embedding element of type Vector is calculated and stored from the vector_embedding function, so the database refreshes it whenever the text behind it changes, and cosine_similarity compares it with a freshly embedded string; because a title says little, a scheduled action first summarises each incident's conversation through a model on SAP AI Core and the embedding is taken from that summary. The similar-incidents list is a view joining incidents to themselves, with order and limit set in a before handler on the expanded query, and the vector element excluded from the service, since OData knows nothing about vectors — on SAP HANA Cloud the function is native, while SQLite needs packages added to the project.published 2026-10-09
CAP in the age of AI #7 - vector embeddings wrap-upCAP in the age of AI #7 - vector embeddings wrap-upThe same embeddings model taken from SQLite to SAP HANA Cloud, then handed to an agent. Locally it is @cap-js/ai that adds the vector functions SQLite lacks and downloads an all-MiniLM L6v2 ONNX model on first start; on HANA natural language processing has to be switched on, the model name is a mandatory argument, the vectors it returns are 768 dimensions, and dimensions must match on both sides of a comparison. HANA itself can be given SAP AI Core as a remote source for other embedding models, which a CDS.ql expression has to inject because the postfix notation only takes what the CDS model holds, and the hour ends with a search function over cosine similarity exposed over @cap-js/mcp, so an agent asked about low output from solar panels calls semantic search instead of writing the query itself.published 2026-10-16
Transforming Enterprise Data Strategy with SAP Business Data CloudTransforming Enterprise Data Strategy with SAP Business Data CloudSAP's layered picture of SAP Business Data Cloud, from lake storage and compute up to a knowledge core and agents, with eight sub-pages that each work one part through. The data products page is the cataloguing and lineage part: datasets described by Open Resource Discovery metadata, listed in the BDC catalog and shared over Delta Sharing to SAP Databricks and SAP HANA Cloud, with SAP Datasphere as a consumer on the intelligent content page; other pages install intelligent content, bring SAP BW data in through the Data Product Generator, provision groups by SCIM from Cloud Identity Services into SAP Databricks, which is identity provisioning, and train predictive models in SAP Databricks that SAP AI Core then serves for batch and real-time scoring. The main page states direction more than mechanism, and the BW page marks SQL Share for derived and OData for analytical data products as to be available.published 2026-05-12
CAP in the age of AI #6 - vector embeddingsCAP in the age of AI #6 - vector embeddingsVector embeddings put to work in the incident management sample, answering whether this problem has been seen before. An embedding element of type Vector is calculated and stored from the vector_embedding function, so the database refreshes it whenever the text behind it changes, and cosine_similarity compares it with a freshly embedded string; because a title says little, a scheduled action first summarises each incident's conversation through a model on SAP AI Core and the embedding is taken from that summary. The similar-incidents list is a view joining incidents to themselves, with order and limit set in a before handler on the expanded query, and the vector element excluded from the service, since OData knows nothing about vectors — on SAP HANA Cloud the function is native, while SQLite needs packages added to the project.published 2026-10-09
CAP in the age of AI #7 - vector embeddings wrap-upCAP in the age of AI #7 - vector embeddings wrap-upThe same embeddings model taken from SQLite to SAP HANA Cloud, then handed to an agent. Locally it is @cap-js/ai that adds the vector functions SQLite lacks and downloads an all-MiniLM L6v2 ONNX model on first start; on HANA natural language processing has to be switched on, the model name is a mandatory argument, the vectors it returns are 768 dimensions, and dimensions must match on both sides of a comparison. HANA itself can be given SAP AI Core as a remote source for other embedding models, which a CDS.ql expression has to inject because the postfix notation only takes what the CDS model holds, and the hour ends with a search function over cosine similarity exposed over @cap-js/mcp, so an agent asked about low output from solar panels calls semantic search instead of writing the query itself.published 2026-10-16
Agentic AI & AI AgentsAgentic AI & AI AgentsSAP's target picture for AI agents: Joule as the engagement layer — Joule Work, Joule Assistants, Joule Agents — with custom agents from customers, partners and SAP teams connecting through A2A for agent-to-agent collaboration and MCP for tool connectivity, on the SAP Business AI Platform. It says up front that parts are not generally available yet, in particular bidirectional communication with third-party agents through the Agent Gateway, so it reads as direction with the building blocks named rather than a design to copy today.published 2026-08-27
HANA AI Toolkit — Local MCP ServerHANA AI Toolkit — Local MCP ServerAn MCP server that turns the hana-ml Python library into governed tools an agent can call against a HANA Cloud instance, shipped in the hana-ai toolkit and exposed over stdio, SSE and streamable HTTP. Its one firm rule is attributable execution: an audit module writes the MCP-side identity — agent, model, session, tool, redacted arguments — into the HANA connection before any SQL runs, and fails the call if that write fails. A worked example of governance on an MCP tool surface, from SAP's data side rather than CAP's.published 2026-06-15
Agentic AI & AI AgentsAgentic AI & AI AgentsSAP's target picture for AI agents: Joule as the engagement layer — Joule Work, Joule Assistants, Joule Agents — with custom agents from customers, partners and SAP teams connecting through A2A for agent-to-agent collaboration and MCP for tool connectivity, on the SAP Business AI Platform. It says up front that parts are not generally available yet, in particular bidirectional communication with third-party agents through the Agent Gateway, so it reads as direction with the building blocks named rather than a design to copy today.published 2026-08-27
Transforming Enterprise Data Strategy with SAP Business Data CloudTransforming Enterprise Data Strategy with SAP Business Data CloudSAP's layered picture of SAP Business Data Cloud, from lake storage and compute up to a knowledge core and agents, with eight sub-pages that each work one part through. The data products page is the cataloguing and lineage part: datasets described by Open Resource Discovery metadata, listed in the BDC catalog and shared over Delta Sharing to SAP Databricks and SAP HANA Cloud, with SAP Datasphere as a consumer on the intelligent content page; other pages install intelligent content, bring SAP BW data in through the Data Product Generator, provision groups by SCIM from Cloud Identity Services into SAP Databricks, which is identity provisioning, and train predictive models in SAP Databricks that SAP AI Core then serves for batch and real-time scoring. The main page states direction more than mechanism, and the BW page marks SQL Share for derived and OData for analytical data products as to be available.published 2026-05-12
CAP in the age of AI #5 - agents wrap-upCAP in the age of AI #5 - agents wrap-upThe agents topic closed out on what sits around the agent rather than inside it, now that the documentation has moved into capire. The chat preview's session is persisted in the database rather than kept in memory, traces go out through @cap-js/telemetry to a self-hosted MLflow under the OpenTelemetry conventions for generative AI, and the panel is explicit that @agent reuses the MCP adapter's code programmatically without serving an MCP endpoint. It ends in the xtravels sample, where a travel agent delegates to a hotel-service sub-agent and reaches a separately started flight service over MCP through the service binding CAP resolves anyway. Earlier in the hour the same agent answers from a model running on the presenter's laptop, reached as an OpenAI-compatible endpoint.published 2026-10-02
CAP in the age of AI #7 - vector embeddings wrap-upCAP in the age of AI #7 - vector embeddings wrap-upThe same embeddings model taken from SQLite to SAP HANA Cloud, then handed to an agent. Locally it is @cap-js/ai that adds the vector functions SQLite lacks and downloads an all-MiniLM L6v2 ONNX model on first start; on HANA natural language processing has to be switched on, the model name is a mandatory argument, the vectors it returns are 768 dimensions, and dimensions must match on both sides of a comparison. HANA itself can be given SAP AI Core as a remote source for other embedding models, which a CDS.ql expression has to inject because the postfix notation only takes what the CDS model holds, and the hour ends with a search function over cosine similarity exposed over @cap-js/mcp, so an agent asked about low output from solar panels calls semantic search instead of writing the query itself.published 2026-10-16
CAP in the age of AI #6 - vector embeddingsCAP in the age of AI #6 - vector embeddingsVector embeddings put to work in the incident management sample, answering whether this problem has been seen before. An embedding element of type Vector is calculated and stored from the vector_embedding function, so the database refreshes it whenever the text behind it changes, and cosine_similarity compares it with a freshly embedded string; because a title says little, a scheduled action first summarises each incident's conversation through a model on SAP AI Core and the embedding is taken from that summary. The similar-incidents list is a view joining incidents to themselves, with order and limit set in a before handler on the expanded query, and the vector element excluded from the service, since OData knows nothing about vectors — on SAP HANA Cloud the function is native, while SQLite needs packages added to the project.published 2026-10-09
Establish a central entry point with SAP Build Work ZoneEstablish a central entry point with SAP Build Work ZoneSAP's reference architecture for a business site in SAP Build Work Zone, standard edition or SAP Build Work Zone, advanced edition as one entry point to business applications, processes, information and communication, on the web or in the Joule Work mobile app. Three of its eight flow steps are how content gets in, the ground launchpad and site content covers: custom apps through the channel to the subaccount's SAP HTML5 Application Repository service for SAP BTP, other content through content providers that expose it over APIs from cloud or on-premise systems and content packages uploaded as files, and SAP Cloud Transport Management or manual export and import to move it between environments. Users and role assignments come from Cloud Identity Services, and the components inside the subscription, among them Notifications, SAP Mobile Services and UI Theme Designer, cannot be decoupled from the product, so a custom mobile app needs its own SAP Mobile Services subscription.published 2026-05-22