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Source capireSource SAP BTP AI Best PracticesSource SAP Help PortalSource SAP CommunitySource SAP websitePublished UndatedPublished 2018Clear all
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Develop a full stack Business application with SAP Cloud Application Programming ModelDevelop a full stack Business application with SAP Cloud Application Programming ModelBuilds a full-stack CAP application in SAP Web IDE Full Stack's now-retired 'SAP Cloud Platform Business Application' template: a data-model.cds entity with its own service definition, then Fiori annotations on the generated OData. Building the project compiles that model into Catalog.xml and csn.json, the EDMX and CSN that metadata publishing says every exposed service gets. The entity and service definitions it writes by hand are the basic shape data modelling covers, though the webIDE and MTA tooling staged around them is gone.former_member584731, published 2018-11-29
Introducing the Cloud Application Programming Model (CAP)Introducing the Cloud Application Programming Model (CAP)SAP's own general-availability announcement for CAP, laid out as a Golden Path: a data model and service defined in CDS gets a generated service provider that serves metadata publishing and CRUD requests, fills in primary keys and audit data, and writes trace and audit logs without application code. The same CDS model carries annotations that translate to OData vocabularies and generate a Fiori elements app skeleton, which is how Fiori annotations work in CAP.Daniel7, published 2018-06-05
Configure an MCP Server Created from an HTTP EndpointConfigure an MCP Server Created from an HTTP EndpointDocuments configuring API Management’s HTTP-source MCP server tools, resources, prompts and policies. Resources are selected from GET operations and have URI templates/content types; prompts define User/Assistant messages with optional arguments, alongside MCP policy configuration.
MCP Server — SAP Integration SuiteMCP Server — SAP Integration SuiteSAP's own page for the MCP Server artifact in SAP Integration Suite, and where the gateway that runs it is described: MCP clients and API clients reach the same backends through two gateways that share security, traffic management and multi-protocol connectivity. Sets out the three primitives a server may expose — tools derived from API operations with an input and output schema, resources addressed by URI for context rather than action, and prompts as reusable interaction patterns. Says availability depends on the Integration Suite service plan, and points at the three creation paths as separate pages.
Model Context Protocol (MCP)Model Context Protocol (MCP)Frames the Model Context Protocol as the answer to an M-times-N integration problem: one shared protocol turns M agents and N tools into M plus N connections, and one server can be called by any compliant agent. On SAP BTP its implementation section lists an SAP MCP Hub as a no-code way to expose enterprise tools, SAP Integration Suite's MCP Gateway as what it calls the governed path for exposing SAP and non-SAP APIs as MCP-compliant tools, custom SDK-based servers, SAP's MCP servers for development, and an MCP adapter for CDS as a CAP plugin marked coming soon. That is the ground MCP servers covers; for security it points at a separate page.
Model Context Protocol Adapter — CAP guideModel Context Protocol Adapter — CAP guideDocuments @cap-js/mcp and com.sap.cds:cds-adapter-mcp, service annotations, generated describe/query/call tools and local client setup. The guide records Java doc-comment configuration, unbound-action-based data changes, incomplete XSUAA PKCE support and the adapter’s governance boundaries.
The Full Picture: Federating, Replicating, and Caching Remote Data in CAP — Part 1The Full Picture: Federating, Replicating, and Caching Remote Data in CAP — Part 1Part one of two, and the concepts half: terminology, what CAP already does, and what a project still has to write. It fixes a vocabulary — federation is the umbrella for making remote data part of your model, delegation forwards each request to the remote at query time, replication is a scheduled copy into the local database, and caching is an option on either rather than a third strategy — which collides with CAP's own, since what this series calls replication is what the CAP-level Data Federation guide calls federation. From the ground the Service Integration guide covers, it lists what is left to hand-write: cross-service $expand, navigation filters across the boundary, write forwarding, timeouts and retries, then scheduling, delta detection, backoff, a guard for two app instances sharing one schedule, idempotent writes, and knowing whether the last run succeeded. That list is why the edge to cds-data-federation holds: the second half maps it onto @federation.delegate and @federation.replicate and says which package carries which part — annotations in cds-data-federation, the scheduled engine in cds-data-pipeline, response caching through cds-caching — so it is the design rationale for the annotations, not the API reference. The gap list and the plugins answering it come from the same person who maintains them; cap-replication-demo and SAP's xtravels sample are cited as the hand-written form of the same work. Part 2 is announced as a walkthrough of a demo application and is not covered here.
Develop a full stack Business application with SAP Cloud Application Programming ModelDevelop a full stack Business application with SAP Cloud Application Programming ModelBuilds a full-stack CAP application in SAP Web IDE Full Stack's now-retired 'SAP Cloud Platform Business Application' template: a data-model.cds entity with its own service definition, then Fiori annotations on the generated OData. Building the project compiles that model into Catalog.xml and csn.json, the EDMX and CSN that metadata publishing says every exposed service gets. The entity and service definitions it writes by hand are the basic shape data modelling covers, though the webIDE and MTA tooling staged around them is gone.former_member584731, published 2018-11-29
Content-based Agents with SAP Joule Studio Agent BuilderContent-based Agents with SAP Joule Studio Agent BuilderJoule Studio, classic edition, the capability in SAP Build the page calls Joule Studio, is where an organisation creates and manages its own Joule skills and Joule agents; its Agent Builder is for a custom agent that handles a multi-step task by combining instructions, model-backed planning and reasoning, and a configured set of tools, Joule skills, document resources, MCP servers, or other agents run as subagents. The page states an agent executes inside the calling user's own security context through SAP Cloud Identity Services, and can be tried against a test conversation before deployment. That is the tool-and-turn-taking ground agent tooling covers, here built through Joule Studio rather than in application code.
Custom Joule SkillsCustom Joule SkillsA custom Joule skill is a workflow written to extend what Joule does out of the box for one specific business need, built and deployed through Joule Studio in SAP Build; the page states this is the supported way to extend Joule's built-in capabilities. Through Joule Studio a skill reaches SAP and external APIs via the destinations already configured in the BTP subaccount and Build environment, with the workflow itself versioned, monitored and deployable from the same tool.
Pro-Code AI AgentsPro-Code AI AgentsPro-code agents are the most flexible way the page frames building an agentic workflow on SAP BTP: full autonomy, custom API integrations and algorithms chosen by the developer, set against declarative alternatives such as content-based agents and Joule Scenarios. It names LangGraph as the framework it recommends for production, enterprise-grade agentic workflows, alongside the ReAct pattern as the plan-act-observe loop such an agent runs. That is the ground agent tooling covers, building an agent's own tool-calling loop rather than exposing an application to somebody else's agent.
Content FilteringContent FilteringContent filtering screens a large language model's input and output for low-quality, irrelevant or unsafe content: the four categories it names being hateful, violent, sexual and vulgar content, checking a request once on the way in and once on the way back. The page states smaller models can do this filtering efficiently, reading perplexity scores to flag anomalies, with the aim that SAP AI Core produces content meeting a stated safety standard, which is the ground content filtering covers on the BTP door.
Data MaskingData MaskingData masking replaces identifiable or confidential text in a prompt with placeholders such as <Person_0> or <Organization_1> before it reaches a large language model, using pattern matching and machine learning to find what to replace. The page states SAP AI Core supports it as a feature and sets out five steps for applying it, defining the masking scope, choosing a technique, preserving the prompt's intent, checking compliance and risk, then testing and monitoring the result. That is the shape data masking takes on the BTP door.
Access to Generative AI ModelsAccess to Generative AI ModelsSets out the plain way to call a deployed generative AI model on SAP BTP: a request that carries the prompt, the model and the parameters that control generation, with the same code able to switch between models. It lists data masking, prompt templating and filtering as features that come with the access rather than as code the caller writes, which is the shape LLM integration takes on the BTP door; the page's implementation sections sit behind its steps and were not read.
Content FilteringContent FilteringContent filtering screens a large language model's input and output for low-quality, irrelevant or unsafe content: the four categories it names being hateful, violent, sexual and vulgar content, checking a request once on the way in and once on the way back. The page states smaller models can do this filtering efficiently, reading perplexity scores to flag anomalies, with the aim that SAP AI Core produces content meeting a stated safety standard, which is the ground content filtering covers on the BTP door.
Data MaskingData MaskingData masking replaces identifiable or confidential text in a prompt with placeholders such as <Person_0> or <Organization_1> before it reaches a large language model, using pattern matching and machine learning to find what to replace. The page states SAP AI Core supports it as a feature and sets out five steps for applying it, defining the masking scope, choosing a technique, preserving the prompt's intent, checking compliance and risk, then testing and monitoring the result. That is the shape data masking takes on the BTP door.
Document GroundingDocument GroundingDocument Grounding is a managed retrieval-augmented-generation service under the Generative AI Hub on BTP, which SAP's page on SAP AI Core places inside that service: it ingests documents from Amazon S3, SharePoint, SAP Document Management Service, SAP Work Zone or an SFTP server, and handles chunking, embedding and retrieval itself, refreshing content automatically when a source document changes. The page states this managed path is recommended for most enterprise scenarios because it covers the whole lifecycle end to end, and offers a separate Document Grounding Vector API for data that is already chunked and embedded, for cases that need finer control over chunking and metadata.
Prompt TemplatingPrompt TemplatingPrompt templating replaces a static prompt with a structured, reusable template whose placeholders are filled in at runtime, which the page says helps LLM responses stay consistent and lets the same instructions be adapted across tasks. On SAP AI Core this is implemented through the Prompt Registry, and the page states that storing prompts as plain strings in code is possible but not recommended. That is prompt management as a stored and managed object rather than a string embedded in the caller.
Prompt TemplatingPrompt TemplatingPrompt templating replaces a static prompt with a structured, reusable template whose placeholders are filled in at runtime, which the page says helps LLM responses stay consistent and lets the same instructions be adapted across tasks. On SAP AI Core this is implemented through the Prompt Registry, and the page states that storing prompts as plain strings in code is possible but not recommended. That is prompt management as a stored and managed object rather than a string embedded in the caller.
Document GroundingDocument GroundingDocument Grounding is a managed retrieval-augmented-generation service under the Generative AI Hub on BTP, which SAP's page on SAP AI Core places inside that service: it ingests documents from Amazon S3, SharePoint, SAP Document Management Service, SAP Work Zone or an SFTP server, and handles chunking, embedding and retrieval itself, refreshing content automatically when a source document changes. The page states this managed path is recommended for most enterprise scenarios because it covers the whole lifecycle end to end, and offers a separate Document Grounding Vector API for data that is already chunked and embedded, for cases that need finer control over chunking and metadata.
Vector RAG vs Graph RAGVector RAG vs Graph RAGA section introduction rather than the comparison itself: it states that the choice between Vector RAG and Graph RAG depends on the domain and the task, and that Vector RAG is the one to use when the subject area has a clear description and the model must be held to specific data structures and attribute values. The four sub-pages it introduces, two on vector-based and two on graph-based RAG, carry the comparison. It sits on the same retrieval question retrieval-augmented generation answers, without naming CDS, CAP or a store.
Agentic AI Security & GovernanceAgentic AI Security & GovernanceNames seven threat categories agentic AI introduces beyond ordinary application security: prompt injection, excessive agency, supply-chain and model-integrity compromise, memory poisoning, improper output handling, resource abuse and agent-to-agent communication poisoning. Its answer is a defence-in-depth structure of security controls, input validation, privilege management, supply-chain checks, memory segmentation, output validation, execution quotas, secure communication, paired with governance and oversight: audit logging, explainability, human-in-the-loop review, fairness assessment and continuous monitoring. States the reason plainly: an agent decides, chains actions across turns, keeps memory between them and can act on real systems, a different risk profile from a single request-response call. That is the authorisation and audit-trail question tool governance is named for.
Introducing the Cloud Application Programming Model (CAP)Introducing the Cloud Application Programming Model (CAP)SAP's own general-availability announcement for CAP, laid out as a Golden Path: a data model and service defined in CDS gets a generated service provider that serves metadata publishing and CRUD requests, fills in primary keys and audit data, and writes trace and audit logs without application code. The same CDS model carries annotations that translate to OData vocabularies and generate a Fiori elements app skeleton, which is how Fiori annotations work in CAP.Daniel7, published 2018-06-05
Configure an MCP Server Created from an HTTP EndpointConfigure an MCP Server Created from an HTTP EndpointDocuments configuring API Management’s HTTP-source MCP server tools, resources, prompts and policies. Resources are selected from GET operations and have URI templates/content types; prompts define User/Assistant messages with optional arguments, alongside MCP policy configuration.
SAP Cloud Application Programming Model (CAP) - start hereSAP Cloud Application Programming Model (CAP) - start hereThe orientation post for CAP, published in 2018 and revised in 2020 and again in 2021 with help credited to Iwona Hahn — which is the unusual part: a starting-point index that was maintained rather than left to rot, and says so at the top. Read as the shape of what a newcomer was pointed at, and as a marker of how much of that changed inside three years.published 2018-10-10
Set up VS Code on Chrome OS for local Application Programming Model developmentSet up VS Code on Chrome OS for local Application Programming Model developmentA CAP development environment on a Chromebook, written from preference rather than constraint — the author sets out why Chrome OS is his primary machine before showing the setup. Carries its own updates section noting what has changed since, which for a 2018 post about an operating system is most of it. The interest now is that local CAP development was portable this early.published 2018-10-16
Set up VS Code on Chrome OS for local Application Programming Model development
Local development has changed twice since this was published, most recently in @sap/cds-dk 7.9.1, 2024-05-02. Affects Runnable code.
Introducing the Cloud Application Programming Model (CAP)Introducing the Cloud Application Programming Model (CAP)SAP's own general-availability announcement for CAP, laid out as a Golden Path: a data model and service defined in CDS gets a generated service provider that serves metadata publishing and CRUD requests, fills in primary keys and audit data, and writes trace and audit logs without application code. The same CDS model carries annotations that translate to OData vocabularies and generate a Fiori elements app skeleton, which is how Fiori annotations work in CAP.Daniel7, published 2018-06-05
Under no capability
API Policy - FAQAPI Policy - FAQSAP’s June 2026 FAQ, version 1.3, explains the scope and use conditions of the SAP API Policy, including published and non-published APIs, ADT developer access, AI-driven usage and third-party MCP servers. It also addresses data extraction and the responsibilities of customers and partners. The FAQ is informational: it does not modify contracts, and applicable agreements and product documentation prevail if they conflict with it.