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Agentic CAP: Build MCP servers and enterprise AI agents
CAP applications become AI agents over the Model Context Protocol and the Agent2Agent protocol, which is the serve your application over MCPServe your application over MCPExposing selected application data and business operations to an AI agent through the Model Context Protocol. An application may also integrate remote services through service integration, connecting its exposed contract with agent access to your systems. Development assistance belongs to agent access to your code.SAP and community, narrow by design concern, and 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 is the LLM integrationLLM integrationCalling a language model from application code with the tenant, destination and credential handling already solved instead using an SDK call with a hand-rolled key.SAP and community, narrow by design through which AI is embedded in a CAP app, with recommendations from the SAP-RPT-1 model as the abstract's example. The abstract names the two protocols and the one model and does not say which packages carry them. A two-hour TechEd 2026 session in a hands-on room (Tuesday 27 October, Berlin), presented by Daniel HutzelDaniel Hutzelgave 3 talks, contributed to 32 entriessap official, Daniel SchlachterDaniel Schlachtercreated 9 pieces, contributed to 10 entriessap official, Marc Huber and Sami Lechner, written here from SAP's abstract; the recording is not yet published.
Where it sits
Explains
Serve your application over MCPCAPABAPBTPServe your application over MCPExposing selected application data and business operations to an AI agent through the Model Context Protocol. An application may also integrate remote services through [service integration](capability:service-integration), connecting its exposed contract with [agent access to your systems](capability:agent-systems-access). Development assistance belongs to [agent access to your code](capability:agent-code-access).LLM integrationCAPABAPBTPLLM integrationCalling a language model from application code with the tenant, destination and credential handling already solved instead using an SDK call with a hand-rolled key.SAP AI CoreCAPABAPBTPSAP 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.
Who is behind it
Daniel HutzelDaniel HutzelWrote this
In the atlas: contributes to 32, wrote 3@danjoaDaniel SchlachterDaniel SchlachterWrote this
In the atlas: contributes to 10, wrote 9@DanSchlachterauthors
In the atlas: contributes to 32, wrote 3@danjoaDaniel SchlachterDaniel SchlachterWrote this
In the atlas: contributes to 10, wrote 9@DanSchlachterauthors
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For mcp-servers
Applies in
For llm-integration
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Group by111 of 111 pieces
Piece, what it offersStill applies
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
SAP Meets the Agentic Web: CAP MCP - UI5 WebMCPSAP Meets the Agentic Web: CAP MCP - UI5 WebMCPA Devtoberfest 2026 session (CAP-42) that puts MCP and WebMCP side by side: MCP exposes a backend to an agent, WebMCP exposes the tools of a web page to whatever agent arrives at it, and only Chrome behind a flag and Cloudflare's Kite browser support the second one today. The CAP half starts with two agents scraping a Fiori elements app with Playwright and browser dev tools, then annotates the catalogue service with the MCP protocol through @cap-js/mcp, after which the agent calls the generated describe tool instead of parsing pages. The UI5 half registers a search-authors tool on the browser's model context API inside the same app, with the cross-origin isolation header the API requires, and lists it through the WebMCP Chrome extension — the agent run against it ends on an exhausted Gemini key rather than a result.published 2026-10-16
Unlocking Tabular Foundation Models: From Experimentation to ProductionUnlocking Tabular Foundation Models: From Experimentation to ProductionA Devtoberfest 2026 session (AI-41) on taking tabular prediction from a playground to a deployment. It sets tabular foundation models against a model trained per use case and against language models reading rows as text, explains in-context learning as examples plus the rows whose target column is missing, and tries SAP RPT 1.6 on a train-delay dataset in the RPT playground, including the column and row importance it returns with the prediction. The same prediction is then rebuilt on SAP AI Core, where the tabular orchestration service plays the part the orchestration service plays for language models: a data destination, a table artifact with its CSN schema and a scenario configuration registered once at design time, after which a predict call passes only the query rows, the scenario configuration and a context-selection strategy. The last demo runs those same calls in natural language through the aicore-skills plugin in a coding agent, and one of them only resolves once the agent is told which scenario configuration to use.SAP Developers, published 2026-10-15
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
Building Code-Based AI Agents on the BAIPBuilding Code-Based AI Agents on the BAIPA Devtoberfest 2026 session on writing the agent yourself rather than configuring one, which is the agent tooling edge: an agent is a model with tools, skills, memory and context around it, and the session reads the code of one built in CAP as a LangGraph state graph, with an agent node that calls the model, a tools node holding the MCP clients, and the edge between them looping until no tool is needed. It then shows the shorter route, a CDS service annotated for the agent protocol with its instructions and skills in an agent.md file. SAP AI Core carries both, with the CAP service in front of it as middleware so the credentials never reach the browser, and that is what cap-agent-ui5-webui is demonstrated against: added as a dependency, it gives the annotated service a UI5 chat interface that can be deployed rather than only previewed. A Python multi-agent application on Cloud Foundry, where an orchestrator delegates to per-stack specialists so no single model sees every tool, closes the session.published 2026-10-09
Building Agentic Workflows with n8n in Joule StudioBuilding Agentic Workflows with n8n in Joule StudioA Devtoberfest 2026 session, JS-31, on SAP-managed n8n inside Joule Studio, shown as a labs preview on a blocked-invoice scenario: a solution made of assets, an intent refined by the web agent into requirements and then a specification, and the generated workflow opened in the n8n editor that Joule Studio connects to its own n8n tenant, with the generated files committed through GitHub. The nodes carry the other edges — the human-in-the-loop node is built on SAP Task Center, and SAP AI Core is where model providers are configured centrally instead of per developer. It ends in Joule chat, where the approval task the workflow created is listed, opened and completed.SAP Developers, published 2026-10-08
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
How to implement business AI capabilities in ABAP CloudHow to implement business AI capabilities in ABAP CloudA Devtoberfest 2026 session (ABA-32) on embedding AI in an ABAP Cloud application and on making SAP Joule aware of what that application can do. The first demo implements two features of a RAP travel app with the ABAP AI SDK: a determination on an uploaded receipt that sends the image together with the allowed categories and transaction types to a model reached through an intelligent scenario, then writes the extracted description, category, currency and amount back to the expense entity; and a recommendation function, triggered by a side effect on the trip title, that proposes a trip type with a score the Fiori UI sorts the proposals by and leaves the business user to accept — which is LLM integration from ABAP code. The second demo builds a capability for SAP Joule in the Joule Studio code editor in VS Code, with a scenario that extracts a trip title from the user's intent and calls an OData service in the ABAP backend through a BTP destination, deploys it and asks the Joule test UI for the trip's details; the session states that agents reach ABAP only over governed remote APIs — OData, SQL and MCP — and that automatic grounding of custom OData services in the knowledge graph is upcoming rather than available.published 2026-10-05
What's in Daniel Hutzel's brain?What's in Daniel Hutzel's brain?A Devtoberfest 2026 session in the building-apps track, in which Daniel Hutzel and DJ Adams work through what the CAP team is building for agents. A bookshop gets an MCP endpoint beside its OData one from a single annotation, with the plugin writing the coding agent's configuration itself; in the xt-travels sample five services that know nothing about each other are served over MCP and an agent does the orchestration the hand-built UI and its value helps used to do. Hutzel then annotates a service as an agent so the ReAct loop and the choice of model move into the CAP service, and finally drops MCP for a skill that teaches the agent CQL instead, which is where the edges to Serve your application over MCP, Agent tooling and Agent guidance come from. The mechanism under all of it, a plugin file in the package root and event handlers that can be added to any CAP service, is the edge to Plugin authoring.published 2026-10-02
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
🟣 Enable Your AI application to Run on Multiple Models to Ensure Resilience and Compliance🟣 Enable Your AI application to Run on Multiple Models to Ensure Resilience and ComplianceDevtoberfest 2026 session AI-21. A cross product owner on the SAP AI Core team gives the reasons an AI application ends up on more than one model — a model down at the hyperscaler, a data centre that is not allowed to run it, a version being deprecated — and runs a joke app that answers from Gemini 2.5 Flash in EU10 and from Claude 4.5 Haiku in Asia-Pacific. The walk through AI Core's parts is where the edges hold: prompts kept as templates in the prompt registry, imported through the launchpad and the APIs or synced as YAML from a git repository (Prompt management); the evaluation service scoring one prompt across up to four models per run on built-in or model-as-judge metrics, and the prompt optimizer generating variants against one metric (Agent evals); and the orchestration service harmonising the providers behind one message format with a priority-sorted fallback list (LLM integration). The session also states where a metric has to be designed rather than picked: an exact-JSON match scores an email generator at zero, so the joke app's custom metric judges topic and family-appropriateness instead.SAP Developers, published 2026-10-01
🔴 Your Agent Wants to Call SAP. Now What?🔴 Your Agent Wants to Call SAP. Now What?Devtoberfest 2026 session INT-23, on what belongs between an agent and an SAP system. A solution advisor and an enterprise architect point Joule Desktop extensions at MCP servers built on API Management inside SAP Integration Suite, and ask it for business partners, orders and line items from an S/4HANA demo system; asked for the ten largest customers it answers from a sample of 200 orders rather than reading the whole table. They then open the API proxy behind it, where a spike arrest policy caps the backend at ten calls per second and the MCP server is configured to expose only the GET resources of the business-partner API so that an agent cannot create one, which is where the edges to Serve your application over MCP and Rate limiting hold. For totals across all sales orders they send the agent to SAP Business Data Cloud instead of the transactional system, since it aggregates and is read-only, and back through Integration Suite for writes such as journal entries.SAP Developers, published 2026-09-30
What an AI infra engineer sees when opening SAP's AI stack (1/3): the model gatewayWhat an AI infra engineer sees when opening SAP's AI stack (1/3): the model gatewayA first look at SAP AI Core's Generative AI Hub from someone who builds gateways outside SAP for a living: the orchestration service harmonizes provider calls onto one API (a model name string switches providers, and SAP Note 3437766 carries the current model list, token rates and deprecation dates), runs a fixed, non-reorderable module pipeline — templating, masking through SAP Data Privacy Integration, grounding, content filtering, then the model call — and retries down an ordered fallback list whose entries can each carry their own model and rules. It places @sap-ai-sdk/orchestration among the official JavaScript, Java and Python SDKs, and notes SAP's own LiteLLM provider as a way to put a different gateway in front of it.amishkush, published 2026-09-30
CAP: The Road Ahead - Simplicity in an AI EraCAP: The Road Ahead - Simplicity in an AI EraDevtoberfest 2026 session CAP10, from the head of SAP's Cloud Application Programming Model: forty minutes of live building rather than slides, on the argument that the cheapest code in an AI project is code nobody has to generate. Claude Code builds an invoice-matching workspace from cds init onwards, and each step is one the framework answers rather than the agent: a 42-line domain model that @sap/cds serves as OData and Fiori elements with a status flow, data privacy and change-tracking annotations that produce audit log entries in the console and would reach the SAP Audit Log service once deployed, an imported S/4HANA supplier data product, and semantic matching from a local embedding model after @cap-js/ai is added. The last three steps are annotations: @mcp beside @odata turns the service into an MCP server that a second Claude session queries through @cap-js/mcp, an agent annotation adds the A2A protocol with an agent card and an audit log entry for the agent task, and a skill file gives the agent the matching policy.SAP Developers, published 2026-09-25
Bring "developers.sap.com" Into Your Dev Environment: SAP Developer Context for AI Coding AgentsBring "developers.sap.com" Into Your Dev Environment: SAP Developer Context for AI Coding AgentsDevtoberfest 2026 session FA-11: a tour of the rebuilt developers.sap.com by the team that runs it, from the administration screens behind the home page to the agent interfaces in front of it. The site is a CAP application on SAP HANA Cloud and SAP BTP, open-sourced in full, and the half of the session that places it here is how its content reaches an agent: every tutorial is also served as markdown, a coding agent is detected and given that version instead of the HTML, a well-known agent card advertises what the site offers, and SAP Developers MCP answers questions about tutorials, news and a reader's own completion history in Claude Code and Joule Desktop, over OAuth or a personal access token. Behind the answers sit two things working together: a knowledge graph in the HANA database over 1400 tutorials and 11,000 concepts, and SAP AI Core for the Joule interactions and the embeddings, with the agent harness written for the site rather than taken off a shelf. Where neither MCP nor A2A is available, the same content is published as skills.SAP Developers, published 2026-09-25
Expose RFCs as MCP Tools in Integration SuiteExpose RFCs as MCP Tools in Integration SuiteWalks through creating an MCP server in SAP Integration Suite from an RFC source: an on-premise RFC destination labelled for Integration Cell inclusion, a search over the backend's function modules (minimum seven characters), and up to 50 selected modules each becoming a tool. Covers the resulting server's configuration (Source, Tools and Prompts, with no Resources section for this source) and the two scopes an RFC-based server's policy adds beyond an API-sourced one, API.invoke and ESBMessaging.send, plus the RFC receiver adapter used at runtime. Demonstrates the flow with a purchase-order scenario where an AI client calls BAPI_PO_CREATE1 and BAPI_PO_GETDETAIL1 as MCP tools.ronit199666, published 2026-09-24
🟣 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
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
Supercharge Joule Desktop: Connect Any SAP API in Minutes with MCP GatewaySupercharge Joule Desktop: Connect Any SAP API in Minutes with MCP GatewayDevtoberfest 2026 session INT-13, in which an SAP solution advisor connects a live S/4HANA system to the desktop experience of Joule Work through the MCP gateway in SAP Integration Suite. One OpenAPI specification covering business partners, sales orders, material stock, billing and purchase requisitions is uploaded as an MCP server artifact, deployed to integration cell with XSUAA inbound authentication, and registered as an MCP connector; the destination behind it is then switched from basic authentication to principal propagation, and the Cloud Connector monitor shows the same question arriving as the presenter's own user. That demonstration is the serve your application over MCP edge, and the second half is Joule Work itself: a Markdown skill that fixes the steps and format of a customer briefing, and a Space the skill rebuilds with each run.SAP Developers, published 2026-09-23
Create an MCP Server for Enterprise AI AgentsCreate an MCP Server for Enterprise AI AgentsWraps the preceding supply-risk graph from API Composition as an MCP server in Integration Suite, with API Management activated as a prerequisite. The walkthrough selects retrieval tools, deploys on Integration Cell, publishes a product in Developer Hub, creates an agent subscription and invokes the tools in MCP Inspector; its companion tutorial uses mock backends.Shyam, published 2026-09-23
SAP Just Became AI-Agent Ready — Here's a Step-by-Step MCP WalkthroughSAP Just Became AI-Agent Ready — Here's a Step-by-Step MCP WalkthroughBuilds an MCP Server on SAP Integration Suite's Integration Cell, in front of a Cloud Integration flow that passes an OData V4 backend through untouched, with three tools generated from an OpenAPI spec. Secures it with OAuth 2.0 on SAP BTP client_credentials against XSUAA, and walks through the scope mismatch — the service key issues ESBMessaging.send, not the server's default API.invoke — that returns 403 until corrected; the finished server is then registered as a tool in Claude Code.published 2026-09-04
SAP Integration Suite + MCP: A Practical Architecture for AI Agents and SAPSAP Integration Suite + MCP: A Practical Architecture for AI Agents and SAPLays out the runtime chain for exposing S/4HANA APIs to an AI agent through SAP Integration Suite: the agent calls a tool on the MCP Gateway, which is built from a governed API Artifact, which still runs on Cloud Integration for mapping, routing and error handling before reaching the backend. It states that the API-artifact approach requires the Integration Cell runtime rather than the normal CPI message-processing runtime, an OAuth-secured REST or OData artifact on OpenAPI 3.0.0-3.0.3, and an entitlement that is service-plan dependent (citing SAP Note 2903776) — so the architecture question is whether the landscape already has the right MCP entitlement, runtime and API foundation.aayushaggarwal, published 2026-09-02
From SAP Sales and Service Cloud V2 APIs to AI: Building an MCP Server with CAP Node.jsFrom SAP Sales and Service Cloud V2 APIs to AI: Building an MCP Server with CAP Node.jsA step-by-step CAP Node.js walkthrough that wraps the SAP Sales Cloud V2 Case API as a remote service, then exposes the resulting service over both OData V4 and MCP through a single @protocol: ['odata-v4', 'mcp'] annotation together with @cap-js/mcp's per_action_tool setting. Each CDS action — listCases, getCaseById, createCase, patchCase — becomes its own MCP tool served at /mcp/caseservices, discoverable through tools/list and invoked from a natural-language request.pvsbprasad, published 2026-08-31
SAP's API Policy on AI Agents: What It Prohibits, Allows, and Leaves OpenSAP's API Policy on AI Agents: What It Prohibits, Allows, and Leaves OpenReading SAP's API Policy v.4.2026a and its FAQ against the reference architectures, the piece narrows the widely repeated claim that SAP bans AI to what Section 2.2.2 actually restricts: an agent planning and chaining API calls outside an SAP-endorsed route, closed off further by Section 3's block on bypassing API Controls through custom code. It compares the endorsed and allowed paths — the MCP Gateway on SAP Integration Suite reaching S/4HANA and ECC through RFC-based back ends under a 15-tool, 200,000-token limit, SAP-provided MCP servers through Joule Studio, the A2A protocol, customer-operated MCP servers allowed under FAQ Question 56, and the ADT MCP Server, whose 18 tools work only on development artifacts and are documented as not intended for productive use. It leaves open whether Section 2.2.2 covers ABAP code that calls a model from inside the system, since enforcement is defined at the API-traffic level rather than by a list of tools.published 2026-08-31
UI5 AI Features library is now an npm PackageUI5 AI Features library is now an npm PackageThe release note for a demo turning into a dependency, and it is explicit about why that matters: the earlier version worked, but using it meant cloning the repo and copying both halves, which stops working the moment one of them changes. The post walks through what shipping the CAP plugin and the be.wl.ai control library in one npm version buys — a client and server contract that cannot drift — then lists the eleven controls by group, shows the two-line configuration that activates each half, and notes that this version is actually deployed on BTP behind both a standalone approuter and Work Zone. It closes on the demo repository, written with no library source in it as the test that the package stands alone.published 2026-08-31
UI5 AI Features library is now an npm Package
cds 8, line ended. Affects Runnable code.
Running AI agents against SAP ECC 6.0: lessons from building an in-system MCP serverRunning AI agents against SAP ECC 6.0: lessons from building an in-system MCP serverNicolasHernandez recounts building and running, over two years, an MCP server written in plain ABAP behind a single ICF node, for SAP ECC 6.0 and S/4HANA on-premise customers that the ADT MCP Server and SAP Joule for Developers, ABAP AI capabilities do not reach. Authorization runs as the calling user rather than a technical RFC user, a choice forced by a customer security team after an earlier version used a technical RFC user, and a later security review found a display-authority check applied uniformly across every write endpoint. The failure modes that cost the most time sat in RFC connectivity itself: RFC_READ_TABLE's empty-table exception collapsed into a false success, a where-used filter was applied only after the row limit, and BAPI calls treated an empty string and an absent parameter as different, silently wrong inputs.NicolasHernandez, published 2026-08-28
From Dashboard to Conversational AI in Minutes: Adding MCP to an Existing CAP ApplicationFrom Dashboard to Conversational AI in Minutes: Adding MCP to an Existing CAP ApplicationA walkthrough of adding @cap-js/mcp to an existing CAP service behind the SuccessFactors Localization Dashboard: two annotations (@odata, @mcp) expose the service over MCP beside its existing OData endpoint, and doc comments added to the 18 entities and their fields are surfaced through the describe tool so a querying model gets business context instead of bare field names. During local development cds watch auto-registers the endpoint in ~/.claude.json, and the author connects the same endpoint to Joule Work Desktop as an MCP connector so users can ask coverage questions in natural language.i533678, published 2026-08-24
Connecting SAP Integration Suite with SAP Document AI and SAP Business AIConnecting SAP Integration Suite with SAP Document AI and SAP Business AIWalks through a Cloud Integration flow in SAP Integration Suite that pulls PDF purchase orders from a Gmail mailbox, extracts header and line-item fields through SAP Document AI's job-submission and polling API, and validates the result with SAP AI Core's generative AI hub using GPT-5-mini as a quality check rather than the source of the extracted values. Groovy steps combine the Document AI confidence score and the model's score into one figure that routes low-scoring purchase orders to manual review, with no S/4HANA posting performed.subhojitSaha, published 2026-08-24
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
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
Connecting a Custom MCP Server to Joule Work Desktop: A Seamless and Secure Authentication FlowConnecting a Custom MCP Server to Joule Work Desktop: A Seamless and Secure Authentication FlowWalks through connecting a custom MCP server to Joule Work Desktop: a Dynamic Client Registration handshake (RFC 7591) followed by an Authorization Code + PKCE flow through the organisation's SSO via XSUAA or IAS, so authentication establishes the human identity that Joule then attaches to every subsequent tool call. That forwarded identity lets the BTP backend apply its existing authorization checks and row-level data filtering instead of a second permission model, and a role change in XSUAA revokes access on the next token refresh.published 2026-08-17
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
Add MCP capabilities to a CAP serviceAdd MCP capabilities to a CAP serviceA fifteen-minute walkthrough of turning a plain CAP service into an MCP server: annotate the service with @mcp, install @cap-js/mcp, and it is served alongside OData at /mcp/<path>. The part worth reading it for is what happens next — cds watch writes the endpoint into the local agent's own configuration file, so OpenCode or Claude Code can call the service without anyone editing JSON by hand. It is built on the tiny-sample facet, so there is nothing to prepare, and it shows the protocol traffic rather than only the result. Written while the adapter was in beta, and it says so.published 2026-08-13
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 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
Expose S/4HANA Cloud OData API as an MCP Server via CAP Node.jsExpose S/4HANA Cloud OData API as an MCP Server via CAP Node.jsA CAP Node.js service imports the S/4HANA Cloud Outbound Delivery OData v2 API's EDMX metadata with cds import, proxies its reads and a custom createDelivery action against it, and annotates the service @protocol: ['odata-v4', 'mcp'] with @cap-js/mcp's per_action_tool setting on, so the custom action gets its own MCP tool rather than one generic call_action. It tests the result with raw JSON-RPC tools/list and tools/call requests before deploying to SAP BTP, Cloud Foundry Runtime.Jacky Liu, published 2026-07-29
Expose S/4HANA Cloud OData API as an MCP Server via CAP Node.jsExpose S/4HANA Cloud OData API as an MCP Server via CAP Node.jsA step-by-step walkthrough that wraps SAP S/4HANA Cloud's Outbound Delivery OData v2 API in a CAP Node.js service and exposes it through @cap-js/mcp, so an MCP client can list the generated tools, query the delivery entities and call a custom createDelivery action over JSON-RPC. The remote S/4HANA connection is configured two ways in package.json: direct Basic Authentication credentials for local hybrid testing, and a named BTP destination for the app once deployed to Cloud Foundry.Jacky_Liu1, published 2026-07-29
RFC/BAPIs Meet AI Agents: Why SAP Integration Suite MCP Server MattersRFC/BAPIs Meet AI Agents: Why SAP Integration Suite MCP Server MattersExplains that SAP Integration Suite's MCP Server artifact can expose RFC-enabled BAPIs, alongside API artifacts and HTTP endpoints, as MCP tools — letting BAPIs on SAP ECC or on-premise S/4HANA participate in agentic workflows without rewriting business logic or waiting for a full ERP transformation. Frames RFC support as a bridge for organisations still modernising rather than a recommendation to build new RFC-based integrations, and notes the exposed BAPIs inherit the server's existing authentication, rate limiting and IP filtering. A demo built against an SAP ECC backend shows an MCP client discovering and invoking several read-only BAPIs from natural-language prompts, with no custom wrapper code.arunmaarirajha_kv, published 2026-07-28
API Composition: Seamless Enterprise Data Access for Developers and AI AgentsAPI Composition: Seamless Enterprise Data Access for Developers and AI AgentsAPI Composition (formerly Graph, a capability of API Management) curates business data graphs from existing SAP and non-SAP API providers, so a query can navigate and expand relationships across systems through one composed API rather than hand-written integration code. Palak walks through combining SAP S/4HANA warehouse-stock data with Everstream Analytics risk data into one API, then exposing that composed API as an MCP server so an AI agent calls it with the same semantic context — the scenario behind API Management's MCP servers coverage.published 2026-07-27
MCP Servers on SAP Integration Suite : Build Your First MCP Server from an APIMCP Servers on SAP Integration Suite : Build Your First MCP Server from an APIWalks through building an MCP server on SAP Integration Suite from an existing managed API — SAP S/4HANA's Purchase Order API — rather than from an HTTP endpoint or an RFC, which the same creation wizard supports but a later post in the series is left to cover. Selected API operations become MCP Tools with generated input and output schemas, the server inherits its Authentication policy from the source API, and a Process Integration Runtime service instance supplies the OAuth credentials an external client needs to invoke it. Ends by connecting the deployed server to Cline in VS Code and having it chain tool calls to answer a natural-language purchase-order query.published 2026-07-15
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
Turn SAP APIs into MCP Tools for AI Agents using SAP Integration Suite Free TrialTurn SAP APIs into MCP Tools for AI Agents using SAP Integration Suite Free TrialWalks through building an MCP server on SAP Integration Suite's free trial from an existing API artifact — the Business Partner API against a public S/4HANA Cloud sandbox — rather than from an HTTP endpoint with an OpenAPI specification or from RFC-enabled function modules, the creation wizard's other two source types, left unexplained here. Covers the API artifact's Content Modifier policy that injects the Business Accelerator Hub API key before the backend call, and the "Trust Upstream MCP Authorization" setting that lets the MCP server delegate authorization checks to that source API instead of duplicating them. Ends by fetching an OAuth token from a Process Integration Runtime service key and pointing an MCP client at the deployed server over streamable HTTP.published 2026-07-13
Autonomous Diagnostics on Kyma Unplugged: Goose + LiteLLM + KRO + SAP AI Core in ProductionAutonomous Diagnostics on Kyma Unplugged: Goose + LiteLLM + KRO + SAP AI Core in ProductionAn in-cluster autonomous diagnostic pipeline on Kyma Unplugged (open-source Kyma, noted to also run on SAP BTP, Kyma runtime): a Goose agent with kubectl access calls SAP AI Core through a LiteLLM proxy, and most of the post is about the quirks of that route — drop_params silently swallowing unrecognised parameters and beta headers, streaming that has to be off when tools are attached, and prompt caching that sits on the Orchestration V2 API rather than the foundation-model path the LiteLLM provider hits. A second Node.js proxy reaches that API, and the author reports caching unconfirmed at production scale, which leaves the roughly $0.004 per static run a target rather than a measured figure. Spend metrics reach SAP Cloud Logging over OTLP through Kyma's Telemetry MetricPipeline while the health analysis itself lands in Kubernetes custom resources read in Busola, and the stack is declared as KRO Resource Group Definitions applied with one Terraform command.quovadis, published 2026-07-09
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
The Easiest Way to Expose a CAP Node.js Project as an MCP ServerThe Easiest Way to Expose a CAP Node.js Project as an MCP ServerFrom cds init to a running MCP server in a handful of steps, ending at the MCP Inspector so the result is something you can see rather than something you have to trust. The claim it makes for the plugin is the absence of work: no REST wiring by hand, the CAP application exposed as a server because the package is installed. A walkthrough rather than an argument — the reasoning about what should be exposed lives elsewhere.Jacky Liu, published 2026-07-03
Natural Language Queries for SAP CAP via MCP: The LLM Translates, CDS ExecutesNatural Language Queries for SAP CAP via MCP: The LLM Translates, CDS ExecutesThe division of labour is the argument, and it is in the subtitle: the model turns English into a CDS query and CDS runs it, so nothing generates SQL and nothing hallucinates a join. Schema annotations do the resolving. Long, with the package, the repository and the MCP registry entry all linked.Shahid, published 2026-06-25
Banking Sentinel: A Multi-Agent Credit-Risk Copilot on SAP BTP, Built on a Mixed AI StackBanking Sentinel: A Multi-Agent Credit-Risk Copilot on SAP BTP, Built on a Mixed AI StackThe risk it is chasing is a graph problem stated in banking terms: a borrower who looks safe alone and is the linchpin of a group ten times the size. Multi-agent, mixed AI stack, on BTP and CAP.Shahid, published 2026-06-24
Banking Sentinel: A Multi-Agent Credit-Risk Copilot on SAP BTP, Built on a Mixed AI Stack
Agent tooling changed in @cap-js/agents 0.9.1, 2026-08-14. Affects A recipe, Runnable code.
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
Beyond Basic AI Error Handling in SAP CPI - A Rules-First Architecture with HANA Cloud LearningBeyond Basic AI Error Handling in SAP CPI - A Rules-First Architecture with HANA Cloud LearningBuilds a rules-first error-diagnosis pipeline on Cloud Integration in SAP Integration Suite: a Groovy classifier resolves known failures — auth, timeout, malformed payload — deterministically, and only genuinely novel errors reach an LLM through the AI Adapter, after a sanitizer script replaces every payload leaf value with a typed placeholder before it leaves the tenant. SAP HANA Cloud, reached over JDBC, holds a fingerprinted dedup cache and an accumulating table of past diagnoses. It also documents the AI Adapter configuration — Advanced mode, a Bearer-prefixed Secure Parameter, and a connection/processing URL split — needed to reach a non-SAP OpenAI-compatible endpoint, found by the author to be undocumented.Krishna_Karthik, published 2026-06-11
Building an LLM Agent in CAP Java with Spring AIBuilding an LLM Agent in CAP Java with Spring AIThe Java counterpart to the author's own Node.js post, and written as one: connecting to AI Core with the SAP AI SDK, then calling an agent and a tool through Spring AI. Opens with a section on libraries and workflows — which framework does what, and what is left to you — before any code.published 2026-06-08
Building an LLM Agent in CAP Java with Spring AI
Agent tooling changed in @cap-js/agents 0.9.1, 2026-08-14. Affects Runnable code.
Calling Claude from ABAP: How I Wired SAP AI Core into a Real Business ScenarioCalling Claude from ABAP: How I Wired SAP AI Core into a Real Business ScenarioA proof of concept wiring SAP AI Core's Claude deployment directly into ABAP with no CAP layer between them: two SM59 HTTP destinations, a hand-written OAuth 2.0 client-credentials exchange, and a regex to unwrap Claude's system/messages response envelope. Most of the effort goes into a system prompt that constrains the model to return one fixed JSON schema of filter fields, which ABAP deserialises with /UI2/CL_JSON and exposes through a RAP custom action so a Fiori search field can drive an OData V4 filter.PranayGaroo, published 2026-06-04
ABAP development tools for Visual Studio Code is now available on the VS Code marketplace!ABAP development tools for Visual Studio Code is now available on the VS Code marketplace!Announces the marketplace release of ABAP development tools for Visual Studio Code, an extension that works against any ABAP system already reachable from ADT for Eclipse and is focused on RAP UI service development, so the Fiori/SAPUI5 frontend and the ABAP backend can be built in the same tool. It ships a built-in ADT MCP Server, disabled by default and switched on from the extension settings, and points readers to a documentation template for capturing ABAP-specific rules in a project's AGENTS.md file for chat agents such as Microsoft Copilot.thomasalexander_ritter, published 2026-06-01
Building an LLM Agent using CAPBuilding an LLM Agent using CAPThe argument is that CAP is already most of what an agent framework wants — services, handlers, a typed model — so the agent is built inside it rather than beside it. AI Core through sap-ai-sdk, then Langchain for the agent and its tools. Long, with a TLDR at the top for people who want the shape without the walk.published 2026-05-27
Building an LLM Agent using CAP
Agent tooling changed in @cap-js/agents 0.9.1, 2026-08-14. Affects Runnable code.
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.
Agentic AI on BTP: a Multi‑Agent App with Pydantic AIAgentic AI on BTP: a Multi‑Agent App with Pydantic AISplits a single CAP/TypeScript agent that had grown to juggle five BTP MCP servers behind one bloated tool list into btp-multiagent-app, a Python and Pydantic AI rewrite where the Cloud Foundry and BTP Core MCP servers each get a dedicated specialist agent, with an audit log specialist scaffolded but left disabled, behind an orchestrator whose only tools are delegation calls to those specialists, which is the agent-tooling pattern the piece works through. Each specialist authenticates to its MCP server over Streamable HTTP with OAuth 2.1, and a shared module centralises the SAP AI Core GenAI Hub model setup behind two credential layouts, one for local runs and one for the Cloud Foundry service binding, the LLM integration side of the rewrite. Tool lists per agent drop from well over a hundred to a dozen or two, and routing failures become separable from tool-misuse failures by reading the trace.published 2026-05-03
Agentic AI on BTP: a Single Agent with LangGraphAgentic AI on BTP: a Single Agent with LangGraphA CAP TypeScript agent puts a LangGraph StateGraph in front of AzureOpenAiChatClient from @sap-ai-sdk/langchain, reaching SAP AI Core's Generative AI Hub, and lets its tool-calling loop draw on five deployed MCP servers — OData MCP Proxy, CI MCP Server, AI Core MCP Server, BTP MCP Server and BTP CF MCP Server — through @langchain/mcp-adapters' MultiServerMCPClient. Because agent, servers and APIs share one XSUAA tenant, the chat service forwards the signed-in user's own JWT instead of a service account, so each downstream server enforces that user's own authorizations. Past roughly a hundred combined tools the single prompt slows down and stops separating concerns, which is why the author's next post splits it into one specialist agent per MCP server behind an orchestrator.published 2026-05-03
Enhancing Enterprise Applications: AI-powered User Experience with SAP CAP AI PluginEnhancing Enterprise Applications: AI-powered User Experience with SAP CAP AI PluginStarts from the blank form nobody wants to fill in, and asks what an enterprise application should do about it. The AI is in service of that rather than the subject of it.published 2026-04-29
What Happens to Your Data When You Enable SAP's AI Features?What Happens to Your Data When You Enable SAP's AI Features?Traces where a prompt goes when Joule, the Generative AI Hub or the orchestration service calls a third-party model through SAP AI Core: the prompt and the business context around it travel to a model such as GPT-4o, Claude or Gemini for inference, and masking through SAP Data Privacy Integration is an optional orchestration module rather than a default, so with no masking block in the @sap-ai-sdk/orchestration client config nothing is masked. On the masking options, which the author says he confirmed with SAP AI Core product management, anonymization discards the original value and pseudonymization keeps it reversible; DPI recognizes person names, email addresses and phone numbers, plus custom entities defined by regex, which leaves commercially sensitive non-PII such as a revenue forecast or a project codename to pass through unless someone writes a pattern for it, and whether that data is covered at all is one of three answers the post marks as still pending. The "no training" assurance for third-party providers is read as contractual rather than technical, and models hosted and managed by SAP, Mistral among them, are named as a path other than a US-hyperscaler-hosted one.jaripie, published 2026-04-23
Integrating a Custom AI Model into SAP CAP Using AI LaunchpadIntegrating a Custom AI Model into SAP CAP Using AI LaunchpadYour own model rather than a hosted one: deployed to AI Core, made callable through AI Launchpad, then reached from CAP. The case a lot of companies ask about and few posts cover.egeAksoyek, published 2026-03-20
Integrating a Custom AI Model into SAP CAP Using AI Launchpad
LLM integration changed in @cap-js/ai 1.0.0, 2026-04-28. Affects Runnable code.
BTP Audit Log MCP Server: Security Monitoring with AI AssistantsBTP Audit Log MCP Server: Security Monitoring with AI AssistantsWouter Lemaire builds an MCP server over SAP BTP's Audit Log Retrieval API using odata-mcp-proxy, with a single entity set and zero custom code, so an AI assistant can answer natural-language questions like "Were there any configuration changes yesterday?" by calling AuditLogRecords_list against a BTP destination authenticated with the Audit Log service's service key. It covers the destination setup, the entity-set configuration that maps the PascalCase entity name to a lowercase auditlogrecords path, and handling the API's 500-record pagination limit.published 2026-03-13
BTP Cloud Foundry MCP Server: Managing CF Environments with AIBTP Cloud Foundry MCP Server: Managing CF Environments with AIWalks through a server built on odata-mcp-proxy that exposes the Cloud Foundry V3 API's 28 entity sets — apps, orgs, spaces, services, networking, quotas — as MCP tools from a JSON config with no application code. It shows the config shape that bridges the API's snake_case paths to PascalCase tool names via a urlPath field, and the OAuth2 client-credentials destination setup an assistant needs to reach a CF landscape.published 2026-03-13
SAP Integration Suite: Intelligent Integration with AI AdapterSAP Integration Suite: Intelligent Integration with AI AdapterThe AI Adapter adds generative AI as a receiver step inside a Cloud Integration flow, connecting through either AICore-GenAIHub — a single API onto SAP AI Core's Generative AI Hub and its foundation models — or Custom-OpenAI-Rest, a plain REST call out to a provider such as OpenAI. SAP Integration Suite starts the call from the iFlow and feeds the model's response into the next step, which is the mechanics behind the sentiment-analysis, summarisation and routing use cases the piece lists without walking through any one of them.meena_choudhary, published 2026-01-28
cds-toon-ai-protocol-adapter: a pragmatic way to cut AI token costs using SAP CAPcds-toon-ai-protocol-adapter: a pragmatic way to cut AI token costs using SAP CAPNames a cost most CAP-plus-LLM writing ignores: every JSON payload streamed to a model is billed by its verbosity, in latency as well as money. The plugin brings TOON — Token-Oriented Object Notation — to a CAP service so the wire format changes without the service code doing so, in a REST variant and an OData one, with token counts reported on both sides.published 2026-01-25
UI5 MCP Server Got Extended for UI Integration Cards GenerationUI5 MCP Server Got Extended for UI Integration Cards GenerationVersion 0.2.1 of the UI5 MCP Server adds three tools for UI Integration Cards: get_integration_cards_guidelines feeds an agent current best practices and API versions, create_integration_card scaffolds a new card project, and run_manifest_validation checks the generated manifest against the schema. The post frames these as closing the same gaps as the server's existing tools — an LLM's outdated or wrong card API knowledge — this time for scaffolding and validating a card rather than a full app.daniel_vladinov, published 2026-01-22
Enhancing Business Processes with SAP BTP AI in SAP Integration SuiteEnhancing Business Processes with SAP BTP AI in SAP Integration SuiteWalks through a post-exit iflow in SAP Integration Suite that calls an LLM through SAP AI Core's Generative AI Hub to turn a customer's free-text return comment into one of a fixed set of SAP rejection-reason codes, so an inbound ORDERS05 IDoc carries a decision (E1EDP01-ABGRU) the ERP can act on without pausing for manual review. The worked example is a Commerce Cloud returns flow into S/4HANA; the piece argues the same bounded-classification pattern fits other narrative fields — delivery-exception remarks, expense-claim justifications — where the cost of an LLM call is repaid by removing a human review step, and points to a colleague's post on the Generative AI Hub orchestration workflow for the guardrailing (prompt templating, data masking) it only names.arunmaarirajha_kv, published 2025-12-15
Prompt-Driven Development: Building Full-Stack SAP CAP Apps from User StoriesPrompt-Driven Development: Building Full-Stack SAP CAP Apps from User StoriesA user story in, a scaffolded full-stack CAP application out, through SAP Build Code and Joule. The question it is actually asking is how much of the foundational code is worth generating, and it answers by generating all of it.AnuradhaDhingan, published 2025-12-11
Streaming LLM Output with CAP and WebSocketsStreaming LLM Output with CAP and WebSocketsTokens arriving from the AI SDK's orchestration module, forwarded to the browser as they come rather than after the model finishes. Covers all four pieces — enabling WebSocket support in CAP, a service with actions and events, the streaming itself, and a frontend on the native WebSocket API — which is what makes it the clearest case for why a CAP service would want a socket at all.RobinP, published 2025-11-18
Streaming LLM Output with CAP and WebSockets
LLM integration changed in @cap-js/ai 1.0.0, 2026-04-28. Affects A recipe, Runnable code.
How to Reduce Prompt Token Costs Using Toon = Save MoneyHow to Reduce Prompt Token Costs Using Toon = Save MoneyToken spend treated as a running cost rather than a detail, and TOON as the lever. Companion reading to the CAP adapter for the same format that is indexed here.Yogananda, published 2025-11-12
How to Reduce Prompt Token Costs Using Toon = Save Money
LLM integration changed in @cap-js/ai 1.0.0, 2026-04-28. Affects A recipe, Runnable code.
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.
How to use Cloud SDK for AI in an existing CAP NodeJS Project in CommonJSHow to use Cloud SDK for AI in an existing CAP NodeJS Project in CommonJSA dependency that is ESM-only inside a project that is CommonJS, and the author's judgement that converting the project was too risky. What follows is the esbuild workaround, which is the honest answer when the ecosystem has moved and your codebase has not.Jacky_Liu1, published 2025-08-01
Improving Exception Handling using Generative AI in SAP Cloud Integration (SAP CI/CPI)Improving Exception Handling using Generative AI in SAP Cloud Integration (SAP CI/CPI)Wires an Exception Subprocess in Cloud Integration to send a failed iFlow's caught exception text to Google Gemini's generateContent endpoint over the HTTP adapter, then parses the response in Groovy for a user-friendly error message, the likely causes, the steps to try and an HTTP status code to return to the sender. A Content Modifier holds the API key and the prompt as externalised properties, which the post says is what lets another model be put in their place, and a closing Groovy script writes the extracted fields to custom headers and log attachments and rebuilds them as an XML error response. The exception-subprocess wiring is given step by step and so is the Gemini call with its request format, which is why the piece sits on integration flows and on LLM integration.AdarshRao, published 2025-07-20
Lets Talk to the Database - CAP & SQLite Powered by Google Gemini-2.5-ProLets Talk to the Database - CAP & SQLite Powered by Google Gemini-2.5-ProNatural language against a CAP service on SQLite, with a non-SAP model doing the translation. Written as a learning exercise rather than a pattern, and the choice of Gemini over anything in the AI hub is the interesting part of it.ShivamShuklaSAP, published 2025-04-16
Elevating Data Privacy in SAP CAP based GenAI Applications with CAP LLM PluginElevating Data Privacy in SAP CAP based GenAI Applications with CAP LLM PluginThe question underneath the enthusiasm: what leaves your landscape when a prompt goes to a model. The plugin's answer is to anonymise the sensitive fields through HANA before the prompt is sent, and this is the post that explains why that is the design rather than an option.AkashAmarendra, published 2024-02-02
An Innovative Language Exchange App developed with SAP Build Apps, CAP, Kyma, and OpenAIAn Innovative Language Exchange App developed with SAP Build Apps, CAP, Kyma, and OpenAIA dormant internal app at SAP Japan revived with AI, built to solve a problem the team actually had — colleagues who could not talk to each other. Build Apps on the front, CAP on Kyma behind it, OpenAI in the middle.javieryw, published 2024-01-31
An Innovative Language Exchange App developed with SAP Build Apps, CAP, Kyma, and OpenAI
LLM integration changed in @cap-js/ai 1.0.0, 2026-04-28. Affects Runnable code.
GenAI Mail Insights - Leveraging the generative AI hub in SAP AI Core to improve customer supportGenAI Mail Insights - Leveraging the generative AI hub in SAP AI Core to improve customer supportA sample application rather than an explainer, and the author says so in the opening — enough talking, here is something that runs. Incoming support mail summarised and categorised through the generative AI hub in AI Core.martinfrick, published 2023-11-15
Automate your Job Postings Using Generative AI (ChatGPT) & SAP Build Process Automation AppsAutomate your Job Postings Using Generative AI (ChatGPT) & SAP Build Process Automation AppsA process that drafts its own text: ChatGPT writing job postings, SAP Build Process Automation carrying them through approval, and CAP in between. Presented as a generic pattern the author expects to be adapted rather than copied.shivamshukla12, published 2023-08-10
Creating a meal generator application with SAP Build, Cloud Application Programming and openAICreating a meal generator application with SAP Build, Cloud Application Programming and openAIIngredients in, meal suggestions out, assembled from BTP services plus OpenAI and Google APIs. A small application built for the pleasure of it, and useful here as the plainest example of CAP holding the middle of a generative flow.former_member793421, published 2023-07-17
Empowering Your Data Analytics with GPT-3 and SAP CAP IntegrationEmpowering Your Data Analytics with GPT-3 and SAP CAP IntegrationFree text in, SQL out, answer back in a sentence — the model writes the query, CAP runs it, and the model turns the rows into prose. Written in the GPT-3 window, so the model access has dated, but the three-step shape has not, and it is a different bet from retrieval: nothing is embedded, the schema is the context.Former Member, published 2023-03-10
A ChatGPT-like chat app built with modern SAP technologies (CAP, SAPUI5) – Part 3/3A ChatGPT-like chat app built with modern SAP technologies (CAP, SAPUI5) – Part 3/3The SAPUI5 frontend of the sample, and mostly an argument for writing UI5 in TypeScript: ES imports in place of sap.ui.define, a class with real private members in place of the underscore convention, and the service types generated from the CDS model copied into the UI package, so one IChats interface types the OData object the controller reads. The second half is structure — a formatter class and a singleton LayoutManager called from the XML view through core:require, a confirmation helper taking a callback, a dialog class resolving a promise, a generic createEntity<T> on a service class — all of it to keep every controller under 100 lines. What it says about arranging a UI5 app holds whatever model sits behind the chat; the tooling around it is of its date, and the post calls UI5's TypeScript support experimental.published 2023-02-27
A ChatGPT-like chat app built with modern SAP technologies (CAP, SAPUI5) – Part 3/3
cds2types. Affects Runnable code.
A ChatGPT-like chat app built with modern SAP technologies (CAP, SAPUI5) – Part 2/3A ChatGPT-like chat app built with modern SAP technologies (CAP, SAPUI5) – Part 2/3The backend: CAP with TypeScript, the entities holding chats and personalities, and the service that talks to the API. This is where the streamed completion and the repositories behind the handler come from, which is the part of the sample still worth lifting even though the model access has since been superseded by AI Core.published 2023-02-24
A ChatGPT-like chat app built with modern SAP technologies (CAP, SAPUI5) – Part 2/3
cap-ui5-gpt-chat: no published releases, repository quiet for 41 months. Affects Runnable code.
A ChatGPT-like chat app built with modern SAP technologies (CAP, SAPUI5) – Part 1/3A ChatGPT-like chat app built with modern SAP technologies (CAP, SAPUI5) – Part 1/3The concepts before the code: what the GPT model and its API actually offer, and what the finished chat application looks like — personalities included. Written in the weeks when the ground was moving under it, and the post carries its own change log to prove it: GPT-3.5 added on 1 March, GPT-4 on 15 March, and an admission that a series planned as two posts became three.published 2023-02-22
A ChatGPT-like chat app built with modern SAP technologies (CAP, SAPUI5) – Part 1/3
cap-ui5-gpt-chat: no published releases, repository quiet for 41 months. Affects Runnable code.
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
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.
CAP Node.js Expert Session #6 - The MCP AdapterCAP Node.js Expert Session #6 - The MCP AdapterDaniel Schlachter and DJ Adams add @cap-js/mcp to a bookshop service, inspect its describe, query and action tools, and query the catalogue through an AI agent. The session distinguishes this runtime adapter from the development-time MCP server and demonstrates automatic registration of the running service with a local agent. The recording describes the adapter as beta in July 2026, with read-only queries and writes through unbound actions.
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.
Examine the details of the Model Context Protocol on startupExamine the details of the Model Context Protocol on startupWhat the MCP adapter actually does when a CAP server boots, watched rather than described — the companion to the tutorial that switches it on.
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.
ABAP AI SDK powered by ISLM - How to implement your own AI use cases in ABAPABAP AI SDK powered by ISLM - How to implement your own AI use cases in ABAPA Devtoberfest 2026 ABAP-track session (ABA-44) on the ABAP AI SDK: an intelligent scenario development object holds the model and its version, so the ABAP code asks for an API, sends a prompt and reads the completion with its token counts and finish reason, and swapping the model is a change to that object rather than to the code. Four demos go through the ABAP line by line — a bare completion, a system prompt with the temperature set to 0.2, tool calls where the model asks for order details and the result goes back into the message container, and a JSON response schema that returns category, severity and goodwill as fields a program can act on — and a fifth replaces the API with the SDK's own mocks so unit tests run with no network call, which is what the LLM integration edge on the ABAP door rests on. The closing availability matrix separates base scenarios, available across SAP S/4HANA on-premise, private edition, public cloud and the SAP BTP ABAP environment, from the orchestration service and SAP RPT, shown for on-premise and private cloud with transport-based correction instructions back to release 2022 and not yet for the public cloud editions.SAP Developers
AI-enabled administration: Automating log analysis with generative AI hubAI-enabled administration: Automating log analysis with generative AI hubA log analysis tool for incident work, built in SAP Business Application Studio with the generative AI hub feature, which SAP AI Core ships as its LLM integration by its own documentation: it automates root-cause analysis over system errors and masks sensitive data. SAP's abstract states the tool's jobs and not how the hub is called, what the masking covers or which model answers. A TechEd 2026 session (Wednesday 28 October, Berlin), with no speaker listed in the catalogue at the time of writing, written here from SAP's abstract; the recording is not yet published.
Stop guessing, start counting: How generative AI fixed an inventory messStop guessing, start counting: How generative AI fixed an inventory messAn inventory-counting application a business built on SAP Business AI Platform with SAP HANA Cloud and generative AI, to replace recount lists kept by gut feel across SAP ERP Central Component, SAP S/4HANA and Microsoft Excel; the talk promises the app, its architecture and what went wrong. The generative AI part is LLM integration from application code; the abstract names HANA Cloud and generative AI as the two building blocks and says nothing about how either is used. A TechEd 2026 session (Thursday 29 October, Berlin), presented by Ranjeet Kumar, written here from SAP's abstract; the recording is not yet published.
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.
Enriching AI models with SAP AI Core in SAP Business Data CloudEnriching AI models with SAP AI Core in SAP Business Data CloudThe integration between SAP Business Data Cloud and SAP AI Core, through which data products with their semantics feed AI models for benchmarking, custom AI development and agentic analytics and applications. The abstract names the two products and the three uses and states nothing about how the data reaches AI Core or what the integration consists of. A TechEd 2026 session (Tuesday 27 October, Berlin), presented by San Tran and Sandy Tran, written here from SAP's abstract; the recording is not yet published.
Build AI agents and applications natively on SAP HANA CloudBuild AI agents and applications natively on SAP HANA CloudSAP HANA Cloud as the base for agentic applications on SAP Business AI Platform: the session goes through CAP, the SAP HANA Cloud vector engine and SAP AI Core, with architecture patterns and lessons learned from building AI-supported applications. SAP's abstract names the three parts of that stack and the vector engine as the database's feature in it, and describes none of the patterns. A TechEd 2026 session (Wednesday 28 October, Berlin; also DA113V, virtual), presented by Christoph Morgen and Shraddha Shetty, written here from SAP's abstract; the recording is not yet published.
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.
Agentic memory: Knowledge graph engine and recall in SAP HANA CloudAgentic memory: Knowledge graph engine and recall in SAP HANA CloudThe knowledge graph engine in SAP HANA Cloud as an agent's memory: what the engine, the graph side of graph, spatial and document data, does in an agentic workflow, how an agent keeps what it learns there, the state agent tooling holds between turns, and the SAP AI Core integration through which the database calls LLMs and tabular foundation models such as SAP-RPT-1. The abstract names the engine, the memory role and the AI Core link and does not say how the memory is written or read. DA263 on Wednesday 28 October is a two-hour hands-on on the same engine with a different cast. A TechEd 2026 session (Tuesday 27 October, Berlin), presented by Markus Fath and Shraddha Shetty, written here from SAP's abstract; the recording is not yet published.
Knowledge graph and agent memory: Build an agent that remembersKnowledge graph and agent memory: Build an agent that remembersThe knowledge graph engine in SAP HANA Cloud as an agent's memory: what the engine, the graph side of graph, spatial and document data, does in an agentic workflow, how an agent keeps what it learns there, the state agent tooling holds between turns, and the SAP AI Core integration through which the database calls LLMs and tabular foundation models such as SAP-RPT-1. The abstract names the engine, the memory role and the AI Core link and does not say how the memory is written or read. DA205 on Tuesday 27 October is the 45-minute talk on the same engine, with a near-identical abstract and a different cast. A two-hour hands-on TechEd 2026 session (Wednesday 28 October, Berlin), presented by Christoph Morgen, Jan Zwickel, Markus Fath and Thomas Hammer, written here from SAP's abstract; the recording is not yet published.
SAP AI Core deep dive: Models, agents, and business context at scaleSAP AI Core deep dive: Models, agents, and business context at scaleSAP AI Core read as the management and orchestration layer of an AI architecture, beyond the model runtime: SAP's abstract lists extending Joule Agents, the model library in Joule Studio for contextual reasoning, connecting data products from SAP Business Data Cloud, and embedding AI in SAP S/4HANA. It names the connections and not the APIs behind them. A TechEd 2026 session (Tuesday 27 October, Berlin; also RA107V, virtual), presented by Franziska Dobrigkeit and Hadi Hares, written here from SAP's abstract; the recording is not yet published.
Joule and SAP Knowledge Graph: Context-aware AI across SAP solutionsJoule and SAP Knowledge Graph: Context-aware AI across SAP solutionsThe catalogue carries RA107's abstract under this title, and what it describes is SAP AI Core as the management and orchestration layer of an AI architecture: extending Joule Agents in SAP Joule, the model library in Joule Studio for contextual reasoning, connecting data products from SAP Business Data Cloud, and embedding AI in SAP S/4HANA. The SAP Knowledge Graph the title names does not appear in that text, so the edges rest on the abstract as published, and whether the session follows the title or the abstract is not known until it is recorded. A TechEd 2026 session (Wednesday 28 October, Berlin; also RA110V, virtual), presented by Jan Portisch and Pavithra G K, written here from SAP's abstract; the recording is not yet published.
Build business differentiation with SAP AI CoreBuild business differentiation with SAP AI CoreSAP AI Core as the orchestration layer for enterprise AI, worked through with SAP's engineers: extend Joule Agents with models from its library, ground them in SAP Business Data Cloud products, embed them in SAP S/4HANA flows, then govern, scale and monitor them from prototype to production. The abstract says what the layer does for an agent and not which services carry each step. A TechEd 2026 session (Tuesday 27 October, Berlin), presented by David Knaack, Franziska Dobrigkeit, Hadi Hares and Jonas Israel, written here from SAP's abstract; the recording is not yet published.
SAP-RPT and TabPFN: Get started with tabular foundation modelsSAP-RPT and TabPFN: Get started with tabular foundation modelsTabular foundation models, SAP-RPT and TabPFN, in a hands-on with SAP and Prior Labs: the model families first, then predictions run through SAP AI Core with a workflow built on tabular orchestration and connected to the participant's own data for real-time predictions. A predictive models session with the models as the subject; the abstract does not name the SDK or API the workflow is built with. A TechEd 2026 hands-on session (Thursday 29 October, Berlin), presented by Eliott Kalfon, Janick Zaehringer-Frasch, Johannes Hoehne, Katharina Schell, Nilay Mehta and Sherene Tan, written here from SAP's abstract; no recording is announced.
Behind the Autonomous Enterprise: SAP Knowledge Graph and SAP Domain ModelsBehind the Autonomous Enterprise: SAP Knowledge Graph and SAP Domain ModelsThe strategy behind SAP Business AI's intelligence layer: SAP Knowledge Graph for enterprise context, SAP Domain Models for SAP-specific expertise and tabular AI for predictions, unified on SAP AI Core with SAP Document AI. Knowledge Graph and Domain Models are not atlas entries, so the edges rest on the two products the abstract names as the infrastructure and on Predictive models for the tabular AI; nothing is stated about what any of them does in the layer. A TechEd 2026 session (Tuesday 27 October, Berlin; also ST103V, virtual), presented by Jan Portisch and Richard Grandpierre, written here from SAP's abstract; the recording is not yet published.
SAP Joule for Consultants: Unified knowledge for requirements and designsSAP Joule for Consultants: Unified knowledge for requirements and designsFit-to-standard analysis as a consultant on a cloud transformation project: connecting an organisation's data to SAP Joule for Consultants through SAP AI Core, getting answers from SAP knowledge and that content, then building an expert, loading project files and running guided prompts. The edges are to SAP Joule, of which Joule for Consultants is a part, and to SAP AI Core, named as the connection path; how the data is grounded is left to the session. A TechEd 2026 hands-on session (Wednesday 28 October, Berlin), presented by Anantharaman Ravi, Natalie Han, Sachin Kaura and Yi Hsuan Lin, written here from SAP's abstract; the recording is not yet published.
Ship your own sandboxed AI operations agent to Kyma in two hoursShip your own sandboxed AI operations agent to Kyma in two hoursAn operations agent built and then broken on purpose: the abstract has participants deploy a reasoning agent to SAP BTP, Kyma runtime, connect it to SAP AI Core, give it observability over the runtime and cause a failure for it to diagnose, the operations automation concern with an agent doing the diagnosis. Patterns for cost control and monitoring are promised at the end; the agent framework and how the sandbox is drawn are not named. A TechEd 2026 session (Tuesday 27 October, Berlin, two hours), presented by Christian Lechner, Piotr Tesny, Thorsten Sandfuchs and Vipin VijayKumar, written here from SAP's abstract; the recording is not yet published.
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
What's in Daniel Hutzel's brain?What's in Daniel Hutzel's brain?A Devtoberfest 2026 session in the building-apps track, in which Daniel Hutzel and DJ Adams work through what the CAP team is building for agents. A bookshop gets an MCP endpoint beside its OData one from a single annotation, with the plugin writing the coding agent's configuration itself; in the xt-travels sample five services that know nothing about each other are served over MCP and an agent does the orchestration the hand-built UI and its value helps used to do. Hutzel then annotates a service as an agent so the ReAct loop and the choice of model move into the CAP service, and finally drops MCP for a skill that teaches the agent CQL instead, which is where the edges to Serve your application over MCP, Agent tooling and Agent guidance come from. The mechanism under all of it, a plugin file in the package root and event handlers that can be added to any CAP service, is the edge to Plugin authoring.published 2026-10-02
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 #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 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 Node.js Expert Session #6 - The MCP AdapterCAP Node.js Expert Session #6 - The MCP AdapterDaniel Schlachter and DJ Adams add @cap-js/mcp to a bookshop service, inspect its describe, query and action tools, and query the catalogue through an AI agent. The session distinguishes this runtime adapter from the development-time MCP server and demonstrates automatic registration of the running service with a local agent. The recording describes the adapter as beta in July 2026, with read-only queries and writes through unbound actions.
Everything new: Try out the latest features in CAPEverything new: Try out the latest features in CAPCAP (@sap/cds) gains the features this hands-on session works through: declarative constraints, which is the input validation concern, status flows as status transition flows, a cds REPL, extensibility through @sap/cds-oyster, and HCQL, which SAP's abstract calls CAP's native query-over-HTTP protocol, after an opening stretch of AI-driven app development. The abstract names the features and stops there; what each one does is not described. A TechEd 2026 hands-on session (Wednesday 28 October, Berlin), presented by Daniel Hutzel, Daniel Schlachter, Marc Huber and Tim Back, written here from SAP's abstract; the recording is not yet published.
The fastest way to a deployable CAP app on SAP BTP Cloud FoundryThe fastest way to a deployable CAP app on SAP BTP Cloud FoundryThe gap between an application that runs locally and one that runs deployed — binding, bundling, the database connection, authorization — treated as the problem rather than as setup. Since updated for cds up, which is the shortest answer the core now offers, and the post carries that update inline rather than leaving the original standing.published 2025-02-13
Reusable components for CAP with cds-pluginReusable components for CAP with cds-pluginThe post that taught the mechanism, and it teaches it by picking a requirement small enough to be absurd: every string a service returns should end in a random emoji. Working through that on the bookshop example leaves nothing to argue about except the plumbing, which is the point — what a cds-plugin is, where it hooks in, and why packaging it beats copying it into the next project. Vinícius Barrionuevo credits this post as the thing that gave him the idea for sap-cap-sdm-plugin, and it is hard to read the plugin ecosystem in this graph without it.published 2023-04-30
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Serve your application over MCPcapability, core and community
LLM integrationcapability, core and community
SAP AI Coreartifact
SAP TechEd — session cataloguepublishing source
SAP TechEd — session cataloguebuild/teched
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talk
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sap.com
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- explains → applies_to on capability:llm-integration, capability:mcp-serversDoor scope derived from the artifacts this piece also explains, so a door page lists it only where it holds (docs/concepts/content-on-doors.md).
- Entered the atlasDiscovered via SAP TechEd — session catalogue · build/teched.
For your agent
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