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Build AI agents and applications natively on SAP HANA Cloud
SAP HANA CloudSAP HANA CloudThe managed database under most of this: CAP's default target, the store Datasphere and Analytics Cloud sit on, and a service with plans and components.sap official, btp 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 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, 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.
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SAP HANA CloudCAPBTPSAP HANA CloudThe managed database under most of this: CAP's default target, the store Datasphere and Analytics Cloud sit on, and a service with plans and components.SAP 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.
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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
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
SAP BDC in Business AI Platform: Contextualize and ReasonSAP BDC in Business AI Platform: Contextualize and ReasonA Devtoberfest 2026 session (DAT-31) that places SAP Business Data Cloud as the contextualise-and-reason pillar of the SAP Business AI Platform, between the build pillar of Joule Studio and Integration Suite and the govern pillar of AI Agent Hub. It covers data products described with open resource discovery and core schema notation metadata, a data product creation agent that assembles a derived data product for overdue billing documents from the billing document and customer products and shows its lineage and fields; the data products a line-of-business application brings being readable by Joule without a BDC subscription; and the SAP-managed knowledge graph that maps business entities and their relationships for agents, held as graph data in the SAP HANA Cloud knowledge graph engine as RDF triples queried with SPARQL. The closing demo has SAP Joule answer head-count and purchase-order questions, with the generated SQL passed through a data product query that adds the restriction policy defined for that user in cloud identity services, which is why the session stands for cataloguing and lineage as much as for the products it names.SAP Developers, published 2026-10-06
🟣 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
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
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
🟣 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
Building your knowledge graph on SAP HANA CloudBuilding your knowledge graph on SAP HANA CloudDevtoberfest 2026 session DAT-11, in which two enterprise architects explain what a knowledge graph is and then build one on SAP HANA Cloud. The first half sets the join across three relational tables against an ontology of classes and predicates, the subject-predicate-object triple written as a turtle file, and a SPARQL query over it, and argues that the chain of relationships is what makes a recommendation traceable back to its reasons. The demo, taken from a Discovery Center mission, curates the ontology and the RDF files for an accounts receivable scenario in VS Code, ingests the triples into HANA Cloud with SQL and walks the resulting graph outward from a customer; that is the graph, spatial and document data edge. It closes by separating three things that share the name: the HANA Cloud knowledge graph engine, SAP Knowledge Graph as the data, and the knowledge graph in SAP Business Data Cloud.SAP Developers, published 2026-09-22
Combining AI Agents with the power of SAP HANA embedded ML (part 1)Combining AI Agents with the power of SAP HANA embedded ML (part 1)Devtoberfest 2026 session DAT-12, the first of two parts, in which Vitaliy Rudnytskiy does a data scientist's work against the embedded machine learning of SAP HANA Cloud by talking to an agent. The session first takes an agent apart into its LLM, its tools, its memory and the loop between them, then stacks the Predictive Analysis Library under the hana-ml Python wrapper and that under the HANA AI toolkit, whose tools an agent calls from plain English in a notebook to preview a sales refunds table, split it into train and predict tables, run a time-series check and train an AutoML forecast, keeping what it decided in Markdown memory files. That is the agent tooling edge; the same toolkit is then started as an MCP server over stdio and over streamable HTTP and reached from a coding agent and from Joule Work's desktop, which is where the live demo partly fails and the failure is shown.published 2026-09-22
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
Building an SAP BTP Agent Architecture from Scratch: Python + CAP + HANABuilding an SAP BTP Agent Architecture from Scratch: Python + CAP + HANAA first-person build log of a Flask orchestrator on Cloud Foundry that reduces a question to keywords, searches a Node.js service's Documents entity, and passes what it finds to Claude for a grounded answer. Getting there means learning that HANA's fuzzy .search() matches one word but not a full sentence, fixed by splitting the query into terms and merging the per-term hits, and that provisioning SAP HANA Cloud hits a database-level free-tier quota rather than an HDI-container one, with a stopped instance needing to be resumed first. It also covers @sap/cds-dk needing to sit in dependencies rather than devDependencies, since Cloud Foundry's buildpack skips the latter, before cds deploy --to hana can run as part of the push.neilaspin, published 2026-07-28
Automate Recurring Data Updates in SAP HANA CloudAutomate Recurring Data Updates in SAP HANA CloudA walkthrough for marking inventory rows stale after 30 days using SAP HANA Cloud's own scheduler: a SQLScript procedure, a CREATE SCHEDULER JOB statement with its 7-field cron pattern, the M_SCHEDULER_JOBS monitoring view, and the matching DROP statements to remove both. It then names where that approach stops and lists the alternatives for scheduling outside the database: SAP Integration Suite's Timer Event, the SAP Job Scheduling service, SAP Automation Pilot for infrastructure commands, and a custom approach for triggering scheduled REST calls from an application.Joni, published 2026-06-11
Accelerating Your Analytics with Delta Table Replication into SAP HANA CloudAccelerating Your Analytics with Delta Table Replication into SAP HANA CloudStarting with QRC 04/2025, SAP HANA Cloud can replicate a Delta Lake table from an external Delta Sharing source directly into a native HANA table, rather than only querying it live through a virtual table. The post walks through creating a remote subscription, running a chunk-based initial load, and setting up change data capture on a schedule or on demand, each with the SQL statements and monitoring views involved, plus the simpler virtual-table-toggling and HANA Cloud Central UI alternatives.SeungjoonLee, published 2025-12-22
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
Medallion Reference Architecture for Big Data Processing in SAP HANA CloudMedallion Reference Architecture for Big Data Processing in SAP HANA CloudSAP's medallion layout for SAP HANA Cloud: structured, semi-structured and unstructured data lands raw in HANA Cloud, data lake files as the bronze layer, is cleansed into a common structure in silver, held in data lake files or the HANA database, and is curated and read-optimised in gold, from where it can also be exposed as a Delta Share. The staged path from raw to consumable is the ground loading a warehouse covers, with Spark jobs and SAP Datasphere data flows named as ways to move data between layers and SAP Analytics Cloud among the consumers of gold. It leaves the ingestion method open, to be chosen by source, freshness and performance, and names no pipeline or schedule beyond that.published 2025-04-03
SAP HANA Cloud as an Esri GeodatabaseSAP HANA Cloud as an Esri GeodatabaseSAP's design for running an Esri ArcGIS Enterprise geodatabase inside SAP HANA Cloud, so that geometries and SAP business data sit in one database and are joined in a HANA calculation view, which is the spatial part of graph, spatial and document data. It traces three personas: an analyst in ArcGIS Pro building query layers over that view, a developer whose CAP application models spatial types such as ST_Point in CDS, and an analyst using a Fiori app on top of such an application. The deployment advice stops at placement: ArcGIS Server and HANA Cloud go in the same region and availability zone, because Esri's certified architectures allow under 5 milliseconds of network latency between them.published 2025-03-25
SAP HANA Data Lake Files (HDLF) Basic FunctionalitiesSAP HANA Data Lake Files (HDLF) Basic FunctionalitiesA Devtoberfest 2026 data-track session on SAP HANA data lake files, the managed object store inside SAP HANA Cloud that is provisioned from HANA Cloud Central, priced per API call and per stored gigabyte, and reached with the same REST API whichever hyperscaler it runs on. Two product managers set up an instance with its IP allow list, client certificates and trusts, then upload, list, read and delete files over the WebHDFS REST API and the HDLFS command-line utility — the Object storage half — and cover file versions with snapshots, access policies per file, and chunked upload with a server-side merge. They then link the instance to a HANA database and query a CSV file as a virtual table: SQL on files now runs through a file adapter in the database, while the older path through the data lake relational engine is deprecated.SAP Developers
Talking to your database: The new data exploration tools in SAP HANA CloudTalking to your database: The new data exploration tools in SAP HANA CloudSAP HANA Cloud's new data exploration tools take a business question in plain language, find the right database objects and run the query, with no data model and no SQL written beforehand, shown live on a standard business data set. SAP's abstract says the session covers how the tools work, what they need and where they hit their limits, and states none of the three. A TechEd 2026 session (Thursday 29 October, Berlin), presented by Anna Hartmann, 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.
SAP HANA Cloud as a self-managed database: Advisors and automationSAP HANA Cloud as a self-managed database: Advisors and automationSAP HANA Cloud advisors generate recommendations for database administration, aimed at system performance and efficiency, and SAP Automation Pilot runs the manual and administrative tasks around the database as automated processes, which is the operations automation concern on the database side. SAP's abstract names the two services and claims reduced operational overhead; which advisors exist and which tasks the automation covers are not said. A TechEd 2026 session (Wednesday 28 October, Berlin), presented by Javier Martin and Roman Belosludtsev, written here from SAP's abstract; the recording is not yet published.
Modernize your database landscape with SAP HANA CloudModernize your database landscape with SAP HANA CloudWhy to move a database estate to SAP HANA Cloud, and the migration tools and services for doing so, which is the platform migration concern. SAP's abstract is one sentence and names none of the tools. A TechEd 2026 session (Thursday 29 October, Berlin), presented by Niklas Siemer and Roman Belosludtsev, written here from SAP's abstract; the recording is not yet published.
SAP HANA Cloud advisors and automationSAP HANA Cloud advisors and automationSAP HANA Cloud's advisors and the tasks they hand off: the elastic compute node, the partition, index and native storage extension advisors that produce recommendations for performance and efficiency, and SAP Automation Pilot as the service that runs the resulting tasks, which is operations automation on the database. The abstract lists the features and stops there, so what each advisor recommends and how a recommendation becomes an Automation Pilot command is not stated. A TechEd 2026 session (Tuesday 27 October, Berlin), presented by Hansika Colomboge Dona, Hui Li, Javier Martin and Roman Belosludtsev, 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.
Unlock predictive and generative AI with your data in SAP HANA CloudUnlock predictive and generative AI with your data in SAP HANA CloudThe AI features SAP lists for SAP HANA Cloud: the function libraries for tabular prediction and forecasting, where predictive models run inside the database, the vector engine, the knowledge graph engine, which is graph, spatial and document data put to generative use, and SQL to LLM calls. The abstract names the engines and the use cases they are meant for and gives no detail of any one of them. A TechEd 2026 session (Tuesday 27 October, Berlin; also DA204V, virtual), presented by Christoph Morgen and Javier Martin, 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.
Get hands on with tabular AI and forecasting models in SAP HANA CloudGet hands on with tabular AI and forecasting models in SAP HANA CloudPrediction and forecasting as predictive models inside SAP HANA Cloud, worked through hands-on: tabular AI models such as SAP-RPT-1 for on-the-fly predictions, time series foundation models for ad hoc forecasts, and the AutoML in the predictive analysis library. The abstract names the model kinds and the library and does not say how a model is called from SQL or from an application. A TechEd 2026 session (Wednesday 28 October, Berlin), presented by Christoph Morgen, Markus Fath, Ritty Joseph and Tanuja Jadhav, 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.
Flexible analytics at speed: Calculation views in SAP Business Data CloudFlexible analytics at speed: Calculation views in SAP Business Data CloudCalculation views in SAP HANA Cloud, the reusable analytic modelling blocks built in its graphical modelling environment, with three newer parts the abstract names: AI-assisted modelling, refactoring, and advanced materialisation, and the way the views sit inside SAP Business Data Cloud. What a materialisation is chosen on, and what the AI assistance generates, is not stated. A TechEd 2026 session (Thursday 29 October, Berlin), presented by Hansika Colomboge Dona, Jan Zwickel, Niklas Siemer and Thomas Hammer, written here from SAP's abstract; the recording is not yet published.
Road map for SAP HANA Cloud: What’s next for the AI databaseRoad map for SAP HANA Cloud: What’s next for the AI databaseSAP's road map for SAP HANA Cloud, which the abstract places as a core component of SAP Business AI Platform within SAP Business Data Cloud. The abstract promises a firsthand look at what is next for the database and names no feature, release or date, so the two edges rest on the products it names and nothing more. A TechEd 2026 session (Wednesday 28 October, Berlin), presented by Niklas Siemer and Thomas Hammer, written here from SAP's abstract; the recording is not yet published.
SAP HANA Cloud: The data foundation for contextualizing and reasoningSAP HANA Cloud: The data foundation for contextualizing and reasoningWhere SAP is taking SAP HANA Cloud, described in the abstract as the anchor of SAP's data strategy and a unified layer to store, contextualise and reason across a business, and what that means for a data platform decision. The abstract is a direction statement and names no feature, release or date, so the one edge rests on the product itself. A TechEd 2026 session (Tuesday 27 October, Berlin; also ST106V, virtual), presented by Matthew Zenus and Stefan Baeuerle, written here from SAP's abstract; the recording is not yet published.
SAP HANA Cloud as the AI database for SAP Business Data CloudSAP HANA Cloud as the AI database for SAP Business Data CloudSAP HANA Cloud as the AI database at the centre of SAP Business Data Cloud: the strategy behind its AI-first direction, and a Carl Zeiss speaker on how it runs in production today. The abstract names the two products and the customer and no feature, workload or figure, so the edges rest on the products as its subject. A TechEd 2026 session (Thursday 29 October, Berlin), presented by Kush Kashyap and Thomas Hammer, written here from SAP's abstract; the recording is not yet published.
Build your custom knowledge layer with SAP HANA CloudBuild your custom knowledge layer with SAP HANA CloudSAP HANA Cloud's multi-model engines, the knowledge graph engine among them, as the base for a custom knowledge layer over the enterprise's data, with SAP Business Data Cloud as the product that includes the database. Graph, spatial and document data put to an AI use; the abstract names the graph engine and not the query language or the vector side. A TechEd 2026 session (Wednesday 28 October, Berlin; also ST108V, virtual), presented by Niklas Siemer and Sebastian Schmidt, 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.
Agentic CAP: Build MCP servers and enterprise AI agentsAgentic CAP: Build MCP servers and enterprise AI agentsCAP applications become AI agents over the Model Context Protocol and the Agent2Agent protocol, which is the serve your application over MCP concern, and SAP AI Core is the LLM integration 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 Hutzel, Daniel Schlachter, Marc Huber and Sami Lechner, written here from SAP's abstract; the recording is not yet published.
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.
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.
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.
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