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@cap-js/ai@cap-js/aiOut-of-the-box value-help recommendations powered by SAP RPT-1, SAP AI Core as a standard CAP service, and an experimental local embedding model behind SQLite's vector functions.sap official, cap
Value-help recommendations and SAP AI Core as a CAP service
@mi8y/cap-agents-aicore-vectorstore@mi8y/cap-agents-aicore-vectorstoreA LangChain vector store backed by SAP AI Core Document Grounding: the collection holds the documents and makes the embeddings, and the CAP application stores no vectors of its own.independent, cap
LangChain vector store on SAP AI Core Document Grounding
@mi8y/cap-agents-cds-vectorstore@mi8y/cap-agents-cds-vectorstoreA LangChain vector store whose documents and embeddings are CDS entities, so retrieval runs as a CDS query against the database the CAP service is already bound to.independent, cap
LangChain vector store with CDS entities for embeddings
@sap/cds@sap/cdsThe CAP Node.js runtime, which compiles the CDS model, serves the services declared in it, answers generic requests before any handler runs, and loads every cds-plugin.sap official, cap
CAP Node.js runtime
@saptarishi/cds-plugin-llm@saptarishi/cds-plugin-llmA CAP service kind that puts eleven model providers behind one chat, stream, embed and batch interface, so which backend a service calls is a profile in package.json rather than a change to the handler.independent, cap
CAP service kind putting eleven model providers behind one API
@saptarishi/cds-plugin-vector-hana@saptarishi/cds-plugin-vector-hanaVector storage and similarity search on SAP HANA from CAP, packaged as a plugin so the embedding column and the query are modelled rather than hand-written.independent, cap
Vector storage and similarity search on SAP HANA from CAP
cap-llm-plugincap-llm-pluginGenerative AI in a CAP application with the sensitive fields anonymised through SAP HANA before the prompt leaves, so the model never sees the confidential half.independent, cap
Generative AI in CAP with fields anonymised through SAP HANA
Not where the records sit anywhere but [SAP HANA Cloud](artifact:btp-hana-cloud): the similarity search and the single retrieval method are both written against its vector engine, and the embeddings and the chat call go through [SAP AI Core](artifact:btp-ai-core), so a project without both has nothing here to call. Not where the dependency has to be an SAP-supported one either: the README states that the plugin is not an official SAP product and is published as a community contribution.
SAP AI CoreSAP AI CoreSAP's AI service on BTP: the runtime for AI assets, and through the Generative AI Hub inside it the access to foundation models, the orchestration service with its templating, masking, filtering and grounding modules, the Prompt Registry and Document Grounding.sap official, btp
AI runtime on BTP with the Generative AI Hub
SAP HANA CloudSAP HANA CloudThe managed database under most of this: CAP's default target, the store Datasphere and Analytics Cloud sit on, and a service with plans and components.sap official, btp
Managed SAP HANA database service on BTP
Provisioned per plan and per component, so capacity and cost are decisions taken before any model exists. And it is the database, not a warehouse — modelling, loading and cataloguing are the products above it, and reaching it directly bypasses every semantic they add.
sap-knowledge-pipelinesap-knowledge-pipelineA Python library that reads selected records out of an SAP OData service or an SAP HANA SELECT and renders them as chunked documents, each carrying the entity set and business key it came from.independent, btp
Python library turning OData or HANA records into chunked documents
An early alpha release whose README says to pin the version and test recipes against non-production data first. HANA synchronisation is snapshot-only and does not reconcile deleted rows, so a document for a row deleted in the source stays in the index. A recipe's field allow-list does not decide what the service account may read: the README states that field allow-listing does not replace SAP authorization, and that the package infers no SAP module semantics, authorization scope or safe fields from table names. Only Business Partner has a built-in recipe, every other dataset needs a `KnowledgeRecipe` written for it, and [Qdrant](external:qdrant) local mode is stated to be for development and smaller indexes.
zvdbzvdbA vector database implemented entirely in ABAP, which stores quantised vectors and retrieves the document chunks relevant to a prompt by brute-force dot product.independent, abap
Vector database implemented in ABAP for document chunk retrieval
cap-bp-ragcap-bp-ragAn event-driven RAG application: Cloud Integration embeds a Business Partner event taken off a broker, and the CAP service stores the vector in SAP HANA Cloud and answers questions from it.independent, cap
Event-driven RAG app with CAP, Cloud Integration and SAP HANA Cloud
Neither the package manifest nor the repository tree declares a licence.
cap-embedding-functioncap-embedding-functionA CAP service that computes a note's Vector(768) embedding in its own handlers, selecting SAP HANA Cloud's vector_embedding as a column from a Dummy entity.independent, cap
CAP service computing embeddings with HANA Cloud vector_embedding
cap-vector-hccap-vector-hcA sample that declares VECTOR_EMBEDDING in a CDS entity: an FAQ model whose embedding column HANA Cloud generates and stores, then answers questions by similarity search.independent, cap
VECTOR_EMBEDDING in a CDS entity for FAQ similarity search
Neither the package manifest nor the repository tree declares a licence.
SAP BTP AI Best Practice Demo - Vector Embedding (RAG) - CAPSAP BTP AI Best Practice Demo - Vector Embedding (RAG) - CAPA CAP backend-sample and fullstack-sample that parse an uploaded CSV of science data, embed each row through SAP AI Core and store the vectors in SAP HANA Cloud's vector store.sap official, cap
CAP samples embedding CSV rows into the SAP HANA Cloud vector store
Every CDS service in both samples is marked `@requires: 'any'` and left publicly accessible, which the README says simplifies the testing and deployment process, and it asks for proper authentication mechanisms to be applied in their place.
SAP BTP AI Best Practice Demo - Vector Query (RAG) - CAPSAP BTP AI Best Practice Demo - Vector Query (RAG) - CAPA CAP backend-sample and fullstack-sample that run a similarity query over the vectors in SAP HANA Cloud and pass the closest science-data rows to the model as context.sap official, cap
CAP samples running RAG similarity queries over SAP HANA Cloud vectors
Every CDS service in both samples is marked `@requires: 'any'` and left publicly accessible, which the README says simplifies the testing and deployment process, and it asks for proper authentication mechanisms to be applied in their place.
sap-btp-ai-best-practicessap-btp-ai-best-practicesSAP's collection of sample implementations for AI-powered applications on SAP BTP, one directory per topic under best-practices, in TypeScript, Python, Java and CAP.sap official, btp
Sample implementations for AI-powered applications on SAP BTP
sap-genai-hub-with-sap-hana-cloud-vector-enginesap-genai-hub-with-sap-hana-cloud-vector-engineA hands-on workshop that works through SAP HANA Cloud's vector engine, SAP Generative AI Hub and the Python LangChain framework in one Jupyter notebook.sap official, btp
Workshop on HANA Cloud vector engine, Generative AI Hub and LangChain
SAP-samples/btp-cap-genai-ragSAP-samples/btp-cap-genai-ragA CAP reference application on SAP BTP for GenAI email support, built on SAP AI Core's Generative AI Hub and retrieval-augmented generation over SAP HANA Cloud's Vector Engine.sap official, cap
CAP reference app for GenAI email support with RAG on HANA Cloud
SAP-samples/btp-cap-genai-semantic-searchSAP-samples/btp-cap-genai-semantic-searchA basic CAP sample that builds a semantic search engine on SAP BTP using Generative AI Hub and SAP HANA Cloud's Vector Engine.sap official, cap
CAP sample for semantic search with Generative AI Hub and HANA Cloud
SAP-samples/generative-ai-codejamSAP-samples/generative-ai-codejamA CodeJam that deploys and queries a large language model on SAP AI Core's Generative AI Hub, then builds a retrieval-augmented chatbot on top of it.sap official, btp
CodeJam deploying an LLM on Generative AI Hub and building RAG
teched2025-DA261teched2025-DA261An SAP TechEd 2025 hands-on workshop on SAP HANA Cloud's AI capabilities: outlier analysis, text embeddings with machine learning, and the knowledge graph engine.sap official, btp
TechEd 2025 workshop on SAP HANA Cloud's AI capabilities
🟣 Bring AI into Your ABAP Applications with ISLM: Build, Integrate, and Consume AI Natively🟣 Bring AI into Your ABAP Applications with ISLM: Build, Integrate, and Consume AI NativelyDevtoberfest 2026 session AI-11, from the product manager and product owner of Intelligent Scenario Lifecycle Management: ISLM as the place an AI use case is registered, deployed and operated inside an SAP S/4HANA system, and what has been added to it for generative AI. The demo builds a custom scenario end to end — an intelligent scenario of type generative AI, a connection to SAP AI Core, a GPT-5 mini model, an orchestration flow template that configures data masking, input and output filtering and translation into the user's logged-in language, then prompts with a grounding query over five companies' return-policy documents — and consumes it from an ABAP report through the ABAP AI SDK, which calls the orchestration API and returns the summary as JSON. Two newer pieces follow: SAP RPT, the relational pre-trained transformer for tabular prediction, called with context rows and query rows and answering with a confidence value and no training step; and system-triggered agents, where an application job in the ABAP system starts a billing-creation agent that reaches S/4HANA OData APIs as tools through the agent gateway with principal propagation. The closing availability matrix states the generative AI capabilities for on-premise and private cloud from the 2021 release, with SAP notes to implement on lower ones.SAP Developers, published 2026-09-24
A Use Case for HANA Cloud Knowledge Graph: AI‑Driven Tender AnalysisA Use Case for HANA Cloud Knowledge Graph: AI‑Driven Tender AnalysisMatching unstructured text against internal data, using both HANA engines at once — the Knowledge Graph for the relationships and the Vector Engine for similarity — with CAP holding the application and its data. Notes that the Knowledge Graph Engine is off by default and has to be enabled when the instance is created, which is the kind of thing that stops a weekend.giacomo_dantonio, published 2026-03-19
Building a Generative AI-powered Inventory Assistant with SAP CAP, RAG, and Vector DatabaseBuilding a Generative AI-powered Inventory Assistant with SAP CAP, RAG, and Vector DatabaseAn existing CAP stock application turned into a question-answering one, and the part worth reading is the schema: three tables, not one — the products, an archive of the text that was embedded, and a separate lookup table holding the vectors. Retrieval finds candidate products by similarity and then reads the real rows, so the answer is grounded in records rather than in the embedded text.furkan5, published 2025-11-07
Building agentic applications with SAP HANA CloudBuilding agentic applications with SAP HANA CloudA CAP application extended with semantic search and agent features on top of SAP HANA Cloud's vector engine and SAP AI Core, built in a hands-on workshop. The abstract names the parts, CAP, the vector engine and AI Core, and the two additions: semantic search, the retrieval half of retrieval-augmented generation, and LLM-supported agent capabilities inside the application, which is agent tooling. Which embedding model, which CAP runtime and how the agent calls its tools are not stated. A TechEd 2026 session (Thursday 29 October, Berlin), presented by Hui Li, Ritty Joseph, Shraddha Shetty and Tanuja Jadhav, written here from SAP's abstract; the recording is not yet published.
CAP in the age of AICAP in the age of AISeven live episodes of Hands-on SAP Dev in which DJ Adams works through CAP's AI features with the people who build them, each one a screen-share against a sample project. The first three are @cap-js/mcp: the @mcp annotation on a service, the describe, query and call tools, CQL as the query format, and what authorisation takes out of the tool list. From episode three the run moves to A2A and @cap-js/agents — human approval of an action, agents.md and skills, session persistence, telemetry and sub-agents — and the last two episodes are vector embeddings in CDS, local on SQLite and native on SAP HANA Cloud.published 2026-08-07
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
Conversational search for the Cloud Integration capabilityConversational search for the Cloud Integration capabilityCloud Integration content becomes searchable in natural language through three architectures SAP's abstract names: MCP servers, retrieval-augmented generation and knowledge graphs, set against the work of analysing and governing a growing SAP Integration Suite tenant. The abstract names the three and does not say which is built or how they compare. A TechEd 2026 session (Tuesday 27 October, Berlin), presented by Vadim Klimov, written here from SAP's abstract; the recording is not yet published.
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.
Generative AI on SAP BTPGenerative AI on SAP BTPThe reference shape for a generative AI application on BTP: a CAP backend, SAP HANA Cloud holding data and embeddings for similarity search through its vector engine, and the Generative AI Hub in SAP AI Core as the single access point to foundation models. It shows retrieval-augmented generation on that stack and names the CAP plugins and SDKs that connect the pieces, for both Cloud Foundry and Kyma.published 2025-09-02
Generative AI on SAP BTP
LLM integration changed in @cap-js/ai 1.0.0, 2026-04-28. Affects A reference.
HANA Cloud’s VECTOR EMBEDDING in CAP and Comparison with OpenAI EmbeddingHANA Cloud’s VECTOR EMBEDDING in CAP and Comparison with OpenAI EmbeddingTwo ways to produce the same embedding, run side by side: HANA Cloud's own VECTOR_EMBEDDING function against Azure OpenAI through the CAP LLM plugin. The CDS side is short — an element of type cds.Vector maps to REAL_VECTOR on deploy — so what remains is the comparison, which is the thing you cannot get from either product's documentation alone.lalitmohan, published 2025-04-09
Michael Jackson: AI Cognitive Pipeline on SAP BTP, SAP CAP, ElevenLabs, Claude, HANA DB, SolaceMichael Jackson: AI Cognitive Pipeline on SAP BTP, SAP CAP, ElevenLabs, Claude, HANA DB, SolaceA pipeline that listens to a song, identifies the historical events it references, retrieves knowledge about each and builds an emotional arc across the whole track. Not an enterprise use case, and useful precisely for that: the plumbing — CAP, HANA, Solace, Claude, ElevenLabs — is doing something the vendor demos never ask of it.Shahid, published 2026-05-18
Multi-modal RAG Proof of Concept: facilitating tech supportMulti-modal RAG Proof of Concept: facilitating tech supportA report on a customer proof of concept rather than an implementation guide — there is no code in it. The reason it is here is the architecture paragraph: CAP holds the backend and the use-case logic, the LLM plugins and the SAP Cloud SDK for AI sit inside it, and gpt-4o is one of two nodes in a small langgraph. It opens with a disclaimer that SAP's own Grounding service is due to gain multi-modal features, which dates the whole thing on purpose.maxlienhardt, published 2025-06-03
Out-of-the-Box Document Grounding in SAP AI Core: RAG with SAP Help PortalOut-of-the-Box Document Grounding in SAP AI Core: RAG with SAP Help PortalWalks through SAP AI Core's document grounding: a default 'help.sap.com' data repository that is already grounded, queried either directly through /retrieval/search after labelling a resource group for grounding, or through the orchestration service's grounding_module_config paired with llm_module_config and templating_module_config to inject the retrieved chunks into a prompt. Shows the orchestration service as a unified interface for swapping LLM providers by changing a configuration parameter rather than application code.Joni, published 2025-04-15
RAG with SAP HANA Cloud Vector Engine, GenAI Hub & CAPRAG with SAP HANA Cloud Vector Engine, GenAI Hub & CAPThe end-to-end version: a CAP service exposing the vector engine and the AI hub behind a couple of endpoints, answering where a CodeJam is on a given date. It has aged in a way the author has marked in the page itself — the section on the CAP LLM plugin now carries "(Outdated)" in its own heading — so read the embedding preparation and the retrieval shape, and check the plugin part against the package.KevinR, published 2024-05-21
Semantic search in CAP (Node.js): vector embeddings and cosine similaritySemantic search in CAP (Node.js): vector embeddings and cosine similarityFinding the best semantic match for a prompt inside an entity, using local vector embeddings and cosine similarity — and the finding the author leads with is that it works without a vector database at all. Reported as experiments rather than as a pattern, which is the honest register for it: the interesting claim is how little infrastructure the simple case needs.published 2025-12-09
Using HANA Cloud’s "VECTOR_EMBEDDING" Function in CAPUsing HANA Cloud’s "VECTOR_EMBEDDING" Function in CAPEmbeddings generated inside the database rather than fetched from AI Core or an external model — a Q4 2024 HANA Cloud function, used from CAP. The shift it describes is where the embedding step lives, which changes what has to be deployed and what has to be called.published 2025-03-02
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.
a2a-plugina2a-pluginExposes an annotated CDS service as an Agent-to-Agent protocol agent — @a2a opts the service in, @a2a.Skill declares what it offers, and an agent handler answers.independent, cap
Exposes an annotated CDS service as an Agent-to-Agent agent
No public repository is declared on the package, so the README is the documentation.
API ManagementAPI ManagementAPI proxies in front of backend services, with security and traffic management policies attached to them, and Developer Hub where consumers subscribe.sap official, btp
API proxies with security and traffic policies and Developer Hub
arc-mcp-hubarc-mcp-hubA hub on SAP BTP that puts several arc-1 instances, one per SAP system, behind one login and a route each, exchanging the caller's token through a destination so each system still sees the real user.independent, btp
Hub on SAP BTP putting several arc-1 instances behind one login
Archived The declared repository is archived.
Functional boundary With one SAP system the README says to point the client at that arc-1 directly. Version 1 exchanges tokens within a single subaccount, so backends spread across subaccounts are out; it keeps its session map in memory, so more than one instance needs sticky sessions or a shared store. It routes deterministically and runs no model, which is a different thing from an assistant that reasons across systems. The `/all` endpoint drops the per-connection isolation, and the maintainer's answer to that is a PROD backend set read-only at the SAP end rather than a tool surface that hides the writes.
bw-modeling-mcpbw-modeling-mcpReads, creates and modifies BW/4HANA modelling objects through the same internal APIs Eclipse BWMT and the BW/4HANA Cockpit use — and can run centrally on BTP with each caller reaching BW as themselves.independent, btp
MCP server for BW/4HANA modelling objects
Its README says it plainly and so does this entry: this is not a simulation, every tool call reaches a live BW system, and write operations produce real changes. BW modelling objects are the definition of a warehouse — the blast radius of a wrong one is every report built on it.
cds-agentscds-agentsTurns a CAP service into a tool layer for agent frameworks — a CAPToolkit bound to a named service, with a tool strategy controlling how much of it becomes callable.independent, cap
Toolkit turning a CAP service into tools for agent frameworks
Maherd18/sap-mcp-serverMaherd18/sap-mcp-serverAn MCP server for ABAP development over the ADT resources: eight tools, each checked against a JSON policy file before the call reaches the system, with activation and transport release held for a person to approve.independent, abap
Policy-checked MCP server for ABAP development over ADT
Development and sandbox systems only: the README warns against pointing it at production. Transport release is never executed, since the tool exists to show the policy stopping it, and separation of duties is checked against the `identity.approvers` list in the policy file rather than against SAP authorisations, so it holds as far as that file does and no further. The write path has been tested with ABAP classes and other object types may need adjustments, there is deliberately no tool that creates or deletes an object, and one server instance serves one SAP system.
pyrfc-mcppyrfc-mcpAn MCP server for calling an SAP system's RFC-enabled function modules from an agent, built so that discovering a function does not authorise calling it: a read or a write is denied unless the function name matches an allowlist the server is configured with.independent, abap
MCP server calling RFC-enabled function modules behind an allowlist
Real SAP calls need the proprietary [SAP NetWeaver RFC SDK](external:sap-nw-rfc-sdk) and the `pyrfc==3.3.1` release the README calls archived and yanked, on Python 3.11, so without those only the demo backend answers. Not over HTTP as it stands: credentials are read from the environment for stdio, and the README says not to expose the server over HTTP without implementing the MCP authorization specification and deployment-level rate limiting. Multi-tenant auth and RBAC beyond the one server-wide policy, rate limiting, metrics and tracing, and deployment packaging are listed as not built. The result limits protect the client and not the system, since the README states they cannot reduce work already performed inside SAP, and the MCP tool annotations that describe read and write risk are advisory metadata rather than an authorisation boundary, so the service account's `S_RFC` and table authorisations are still what decides.
SAP API Policy Evidence SkillSAP API Policy Evidence SkillAssesses whether a described SAP API usage aligns with the SAP API Policy, gathering live evidence from SAP's own sources and returning a sourced assessment with a stated confidence — and routing the legal questions away from itself.independent, btp
Skill assessing API usage against the SAP API Policy
It says so itself, in bold, and the atlas repeats it: not legal advice and not a compliance decision. Only SAP, your contract, or SAP support can answer bindingly, and the skill routes legal, commercial and roadmap questions there rather than guessing at them. Treat the output as evidence gathered, not as a verdict.
SuperClaude for SAPSuperClaude for SAPA Claude Code plugin that installs and wires its own ABAP MCP server, then puts a two-layer write guard between the agent and any system profile marked QA or production.independent, abap
Claude Code plugin with an ABAP MCP server and write guards
It is a plugin for one server — its own — so it is a stack rather than a component. And the guard is only as good as the profile labels: a production system registered as `DEV` is a production system with the safety off.
A Clearer View of Your SAP Integrations Under the New API PolicyA Clearer View of Your SAP Integrations Under the New API PolicyThe SAP API Policy Evidence Skill assesses one SAP interface or usage pattern at a time against SAP API Policy v.4.2026a, reading evidence from five MCP servers, among them servers for the Business Accelerator Hub and SAP Notes, mcp-sap-docs and a read-only ARC-1, and returns one of four labels with a confidence level, an evidence table, residual risk and questions for SAP. It gives no compliant or not compliant answer on purpose, because SAP's FAQ (Q49) declines to publish a binding decision matrix, and it sends legal, commercial and roadmap questions to SAP and lowers its confidence when a source is not connected. Four sample runs show the method, among them ODP-RFC extraction labelled likely not aligned under SAP Note 3255746 and the same API_SALES_ORDER_SRV assessed differently for an autonomous agent behind a custom gateway and for a Boomi middleware flow; the SAP Notes and Business Accelerator Hub servers read SAP sites through a browser login, which the post says to use at your own risk.published 2026-06-02
ABAP + MCP: Let AI Do Real Development in Your SAP SystemABAP + MCP: Let AI Do Real Development in Your SAP Systemabap-adt-mcp is an open-source MCP server that gives an AI agent full ABAP development capability over ADT: it searches, reads, edits, activates and unit-tests objects, and runs ATC checks, on a connected S/4HANA Cloud or on-premise system, acting as the signed-in developer. It runs on abap-adt-api, the same REST client Eclipse's own ADT tooling uses, and sits beside SAP's own ADT MCP Server, shipped with ADT for VS Code and Eclipse under a different server key, and marianfoo's abap-docs, a read-only server over the ABAP Keyword Documentation and Clean ABAP. Each destination carries a policy — read-only, allowed packages, denied tables and tools — the server checks before every call, and an optional audit file logs each tool call with its outcome and policy gate.Willian_Saez, published 2026-09-03
ABAP and Agentic AI: The Hidden Problem in Real ProjectsABAP and Agentic AI: The Hidden Problem in Real ProjectsAI in SAP development splits on three questions, how the model gets context, where writing happens and who controls the setup: UI5 and CAP work in local files where git, reviews, tests and pipelines already gate a change, while on-stack ABAP writes land directly in a shared development system. Six patterns follow, framed after a paper by Lars Hvam, from AI in the IDE to off-stack development with abapGit, and the post settles on a hybrid where the agent reads the system over ADT or MCP and writes through a gated git flow. Read access alone is put at maybe 80 percent of the work, and what agent access to your code still lacks is named as tool governance: read-only defaults, package restrictions, blocking free SQL, per-user identity and auditing.published 2026-04-22
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
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.
Agentic Engineering for SAP BTP ExtensionsAgentic Engineering for SAP BTP ExtensionsAn agent harness coordinates code generation across isolated Git worktrees, using a skills registry, SAP CAP MCP Server, Fiori MCP Server and UI5 Web Components MCP Server for development context. The diagram shows LiteLLM as a model proxy in front of SAP AI Core, while automated checks and review gate the generated changes. The worked scenario is CAP-based extensions on BTP, with specifications written before generation and reusable skills governed across teams.
ARC-1 on SAP BTP: Secure ABAP Agentic Development Beyond the LaptopARC-1 on SAP BTP: Secure ABAP Agentic Development Beyond the LaptopThe BTP deployment of ARC-1: one Cloud Foundry app per SAP system, reached by MCP clients through XSUAA and reaching the backend through the SAP Destination service, the SAP Connectivity service and Cloud Connector, with a BasicAuth destination for startup probing and a Principal Propagation destination for per-user requests, and SAP_PP_STRICT=true so a propagation problem fails instead of falling back to the shared user. Tool governance is the three gates it spells out, the server ceiling set by environment variables, the user's permission from XSUAA role collections, OIDC scopes or API-key profiles, and the SAP backend authorisation, all of which must allow an action, and the staged rollout from read-only to writes in $TMP, then selected packages, then transport writes. It recommends the MTA deployment over Docker on Cloud Foundry or a buildpack push, treats BTP ABAP Environment as a separate service-key scenario with no Cloud Connector, and is not a full setup guide: the commands are left to the ARC-1 deployment docs.published 2026-04-29
ARC-1 on SAP BTP: Secure ABAP Agentic Development Beyond the Laptop
arc-1 has been through 3 major lines since this was published: 0.8 (2026-05), 0.9 (2026-05), 1 (2026-07). Affects A recipe.
Building an Enterprise-Ready SAP AI Agent with Open SourceBuilding an Enterprise-Ready SAP AI Agent with Open SourceA self-hosted chat agent for SAP systems: LibreChat as UI and orchestrator with Entra ID sign-in and SharePoint connected, a local LLM through Ollama, and two MCP servers, mcp-sap-docs for public documentation and the ADT MCP server vibing-steampunk for the system itself. The ADT server runs in its read-only mode under a technical user with display authorisations only, so the agent can find a function module and pull its source, or locate a RAP business object with its behaviour definition and projection view, but cannot change anything. Two workflows show it, explaining what BAPI_DISPUTE_CREATE does in one system and planning a RAP action from a PDF specification, where the generated ABAP is copied into Eclipse by hand; the full setup is in the marianfoo/llm-client-sap-system-integration-sharepoint repository.published 2026-03-03
Building MCP Servers with SAP Integration Suite: What to Design, Not Just How to ConnectBuilding MCP Servers with SAP Integration Suite: What to Design, Not Just How to ConnectArgues that the tool design, not the connection, is the hard part of putting an API in front of an agent. Works one request — create a customer — through eight endpoint-shaped tools, where the agent carries ids between calls and rebuilds the business rules each run, then through a single intent-shaped create_customer that hides the sequence; and says what separates the two: a business operation for a name, parameters a caller can supply without a lookup, the rules in the server rather than the prompt, and a description written for an agent choosing a tool. The SAP Integration Suite half is short — three creation paths, an iFlow behind the API artifact when a tool has to cross systems — and it closes on a checklist to run before publishing.published 2026-07-07
Building trusted applications on SAP Business AI Platform by designBuilding trusted applications on SAP Business AI Platform by designBuilding cloud applications on SAP Business AI Platform with security and compliance built in: the identity, connectivity, governance and security services SAP groups under the platform, secure-by-design development, and authorisation and access controls for AI agents. The edges are to Authorization and to Tool governance, the concern of what an agent may call with which authorisations; the abstract names the service categories and no service by name. A TechEd 2026 session (Wednesday 28 October, Berlin), presented by Christian Cohrs and Regine Schimmer, written here from SAP's abstract; the recording is not yet published.
From APIs to Agent Tools: How MCP Gateway changes what Integration meansFrom APIs to Agent Tools: How MCP Gateway changes what Integration meansThe opening session of the Devtoberfest 2026 integration track, in which two product managers for SAP Integration Suite set out why the suite puts an MCP gateway between agents and backend systems, and then build one. The demo turns an HTTP endpoint and its OpenAPI specification into MCP tools on the integration cell runtime, does the same on top of an existing API-centric integration, and shows the policies tab where authentication through an external provider, quotas, spike arrest and IP filtering sit, plus the trace of the JSON-RPC calls in monitoring and the agent they are earmarked against in analytics. That is where the serve your application over MCP and tool governance edges hold; RFCs and BAPIs as sources are stated as supported, and MCP federation, custom domains and edge integration cell as roadmap.SAP Developers, published 2026-09-23
From SAP Integration Suite iFlow to MCP Tools: Exposing OData APIs to AI AgentsFrom SAP Integration Suite iFlow to MCP Tools: Exposing OData APIs to AI AgentsBuilds an MCP Server artifact inside SAP Integration Suite's Integration Cell runtime that fronts a Northwind OData V4 API through a Cloud Integration iFlow, turning each OpenAPI operation into a tool an agent can call. Walks through the OAuth2 client-credentials flow against XSUAA and the authorization policy scope — ESBMessaging.send in place of the default API.invoke — that gates access to the MCP server, then tests the server from Claude Code.published 2026-07-14
Govern AI-supported actions across enterprise systemsGovern AI-supported actions across enterprise systemsGoverning what an AI agent may do once it takes business actions across enterprise systems, which is the Tool governance concern, with SAP Integration Suite as the place it is enforced. The abstract lists the mechanisms, scoped APIs, event-driven workflows, runtime policy enforcement, exception handling and observability across SAP and third-party systems, and names no Integration Suite feature behind any of them. A TechEd 2026 session (Tuesday 27 October, Berlin; also IN200V, virtual), presented by Finny Babu and Piyush Gakhar, written here from SAP's abstract; the recording is not yet published.
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
Intelligent, governed, and agentic integrations with SAP Integration SuiteIntelligent, governed, and agentic integrations with SAP Integration SuiteSAP Integration Suite positioned as the control plane that connects and governs agentic workflows across SAP and third-party systems, set against the abstract's claim that exposing back ends directly to AI brings security, compliance and cost risks. That governing role is the Tool governance concern; the abstract promises recent innovations and names none. A TechEd 2026 session (Wednesday 28 October, Berlin), presented by Finny Babu and Piyush Gakhar, written here from SAP's abstract; the recording is not yet published.
Introducing ARC-1: A Secure ADT MCP Server for Enterprise SAP DevelopmentIntroducing ARC-1: A Secure ADT MCP Server for Enterprise SAP DevelopmentWhy ARC-1 exists beside earlier ADT MCP servers such as vibing-steampunk, fr0ster/mcp-abap-adt and dassian-adt: one centrally deployed instance per SAP system instead of a server on each developer laptop, with the MCP client to ARC-1 hop and the ARC-1 to SAP hop authenticated separately. It sets out the restrictive defaults (read-only, no free SQL or named table preview, writes confined to $TMP), the layering in which ARC-1's configuration, the user's role and SAP's own authorisation must all allow an action, Principal Propagation so SAP sees the real user, and audit events to the SAP Audit Log service; that is tool governance as much as agent access to your code. It leaves the BTP setup to the next part, and places ARC-1 beside SAP's ADT MCP Server, which SAP's configuration guide describes as a local HTTP server hosted by the IDE, and beside SAP API Policy 4.2026a, where ARC-1's stated position is to stay close to documented ADT behaviour and ask SAP before production use.published 2026-04-27
MCP Servers are the New Z-TablesMCP Servers are the New Z-TablesA single CAP service exposed as three overlapping MCP servers tools, queried by the same prompt nineteen times, returned the same tool every time — evidence, the author argues, that tool selection rests on the resemblance between a prompt and a tool's description rather than on any binding a where-used list could enumerate before retiring it. It contrasts that setup with @cap-js/mcp, which exposes a whole service as three generic tools instead of one per operation, and quotes the adapter's own beta documentation admitting no rate limiting, audit logging, input validation or approval workflow, and no use as a gateway to SAP Application APIs. From there it names four small Tool governance practices it has adopted: an owner recorded in the tool's own metadata, logging every invocation, deprecating inside the description, and exposing fewer tools.published 2026-08-21
Model context protocol (MCP) unlocked: Enable governed access to AI agentsModel context protocol (MCP) unlocked: Enable governed access to AI agentsThe MCP Gateway in SAP Integration Suite: the place policy, security and analytics are applied to a curated set of MCP tools before an AI agent reaches enterprise tools and APIs through it, tool governance done in the integration layer and agent access to your systems routed through one gateway. The abstract names the gateway and its three functions and does not say how tools are curated, which policies exist or how the agent authenticates. A TechEd 2026 session (Tuesday 27 October, Berlin), presented by Bruno Renzo, Emily Mui, Markus Winter and Sunny Kapoor, written here from SAP's abstract; the recording is not yet published.
OData entity as a governed query surface for agents, not a toolOData entity as a governed query surface for agents, not a toolAn OData entity, generated from the annotations a service already carries, as the surface an agent queries over SAP data: filterable, selectable and sortable fields, authorisation injected on the server side, and a bound query cost, instead of the entity as a tool. SAP's abstract sets this against the MCP gateway in SAP Integration Suite, where exposing an API is the easy part and getting an agent to pick the right one is not, which is the tool governance concern read through OData. A TechEd 2026 session (Wednesday 28 October, Berlin), presented by Aleksandr Ivanov and Evgenii Shirokikh, written here from SAP's abstract; the recording is not yet published.
Powering Enterprise-Ready Agentic AI with SAP Integration SuitePowering Enterprise-Ready Agentic AI with SAP Integration SuiteA Devtoberfest 2026 session (INT-31) from SAP Integration Suite product management on the MCP gateway. The demo creates an MCP server in a design package from an API already deployed on the tenant, exposes two of its 300-odd resources as tools, attaches authentication and quota policies on a policies tab, and publishes the deployed endpoint as an AI product in Developer Hub of API Management, where an agent subscribes and receives a token, URL, key and secret; the calls then show up in monitoring, in message processing logs and in the analytics dashboard, which is the MCP exposure and the tool governance the edges rest on. Three AI features inside Cloud Integration follow: a content migration agent that converts a PI/PO Java mapping into a Groovy script and generates a second integration flow for its lookup, memory profiling during a flow simulation, and root-cause analysis on a failed message processing log.SAP Developers, published 2026-10-07
Third-Party MCP Server Access to SAP Solutions on BTPThird-Party MCP Server Access to SAP Solutions on BTPDescribes external MCP servers and custom servers on BTP, assigning their operators responsibility for authentication, scoped identity, API limits, logging and lifecycle. The permitted use remains conditional on the SAP API Policy, while SAP Integration Suite and Joule Studio are described as SAP-provided paths. Hosting a server on BTP does not automatically supply its governance controls.Dhawal Joshi, Harut Ter-Minasyan, Mirko Günther
Under no capability
API CompositionAPI CompositionOne API over the business data in a landscape: a business data graph queried with OData V4 or GraphQL, instead of each source system's own API.sap official, btp
Business data graph queried with OData V4 or GraphQL
btp-agentbtp-agentOne LangGraph agent in a CAP service, with a UI5 chat screen in front of it and four odata-mcp-proxy MCP server modules behind it, deployed to Cloud Foundry as a single MTA.independent, cap
LangGraph agent and UI5 chat screen over four BTP MCP servers
Neither the package manifest nor the repository tree declares a licence.
Reports Assistant with SAP Document ManagementReports Assistant with SAP Document ManagementA CAP agent that turns a few notes into a status report and saves it in an SAP Document Management repository, where the instructions and the skills it follows are kept too.independent, cap
CAP agent writing reports into a shared SDM document workspace
SAP AI LaunchpadSAP AI LaunchpadThe SaaS front end over SAP AI Core on BTP: a Model Library, Grounding Management for document pipelines, Workspaces that connect AI runtimes, and ML Operations for the lifecycle of a scenario.sap official, btp
SaaS front end over SAP AI Core
SAP Integration SuiteSAP Integration SuiteSAP's integration service on BTP: Cloud Integration for orchestration, API Management in front of it, and an MCP gateway that serves a managed API to agents.sap official, btp
Integration service on BTP with Cloud Integration and API Management
scecscecDocker Compose stacks for the edge side of an SAP BTP scenario: a ThingsBoard IoT stack with components added for an SAP Cloud landscape, and a template that builds a Cloud Connector image from SAP's own download.independent, btp
Docker Compose IoT edge stack that connects to SAP BTP
