PersonSAPDeveloper Advocate
DJ Adams
Capabilities
Contributes to
Account provisioningBTPAccount provisioningSetting up cloud accounts, their organisational structure, resource entitlements, subscriptions and access assignments so the setup can be repeated from code. [Deployment](capability:deployment) puts an application onto a runtime that this account structure makes available.Agent guidanceBTPCAPUI5ABAPSAP CommerceAgent guidancePackaged knowledge you hand a coding agent so it gets a framework right unprompted — skills, rule files, instruction packs. Distinct from agent tooling, which is about building agentic features into your application, and from [agent access to your code](capability:agent-code-access), where the agent reads the real project instead of being handed a file about it.
Entries
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EntrySignals
Contributes to
btp-service-metadatabtp-service-metadataA machine-readable catalogue of the services available in an SAP BTP pay-as-you-go account, published as JSON files curated per persona, starting with the developer persona and an inventory list.sap official, btp
Machine-readable catalogue of SAP BTP pay-as-you-go services
btp-setup-automatorbtp-setup-automatorA containerised script that sets up an SAP BTP account from JSON configuration files: entitlements, subscriptions, service instances, administrators and role collections, instead of clicking through the cockpit.sap official, btp
The declared repository is archived.
Containerised script setting up an SAP BTP account from JSON config
SAP Developers MCPSAP Developers MCPThe hosted MCP server of developers.sap.com, over which an agent searches SAP's tutorials, reads missions and queries the site's knowledge graph of tutorials and concepts.sap official, umbrella
Hosted MCP server of developers.sap.com tutorials and missions
SAP-samples/cloud-btp-cli-api-codejamSAP-samples/cloud-btp-cli-api-codejamThe material for the "Hands-on with the btp CLI and APIs" SAP CodeJam, from installing the command line to creating and deleting account resources through the Core Services API.sap official, btp
CodeJam material on the btp CLI and the Core Services API
SAP-samples/sap-devs-cliSAP-samples/sap-devs-cliA CLI that injects current SAP developer knowledge packs into an AI coding tool's context and wires up SAP's own MCP servers for it, shipped as a single binary through Homebrew, Scoop or GitHub Releases.sap official, btp
CLI adding SAP developer knowledge and MCP servers to AI coding tools
SAP-samples/teched2024-XP280SAP-samples/teched2024-XP280An SAP TechEd 2024 session that creates an SAP BTP subaccount and sets up SAP Build Code and SAP Build Process Automation with the Terraform Provider for SAP BTP.sap official, btp
TechEd 2024 session on Terraform setup of SAP Build services
SAP/architecture-centerSAP/architecture-centerThe repository the SAP Architecture Center site is built from: SAP's reference architectures, each drawn as editable draw.io diagrams, with Mermaid for flows.sap official, btp
Source repository of SAP's reference architectures
Writing and topics
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
CAP in the age of AI #6 - vector embeddingsCAP in the age of AI #6 - vector embeddingsVector embeddings put to work in the incident management sample, answering whether this problem has been seen before. An embedding element of type Vector is calculated and stored from the vector_embedding function, so the database refreshes it whenever the text behind it changes, and cosine_similarity compares it with a freshly embedded string; because a title says little, a scheduled action first summarises each incident's conversation through a model on SAP AI Core and the embedding is taken from that summary. The similar-incidents list is a view joining incidents to themselves, with order and limit set in a before handler on the expanded query, and the vector element excluded from the service, since OData knows nothing about vectors — on SAP HANA Cloud the function is native, while SQLite needs packages added to the project.published 2026-10-09
CAP in the age of AI #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
Define, generate, deploy: AI-driven spec-first TerraformDefine, generate, deploy: AI-driven spec-first TerraformAccount provisioning written as a spec first: the abstract has participants write a structured description of a BTP account, its subaccounts, entitlements, governance, best practices and more, and an AI turns it into Terraform scripts that build it. The abstract does not name the Terraform provider the scripts use, the spec format or the AI behind the generation. A hands-on TechEd 2026 session (Wednesday 28 October, Berlin), presented by Christian Lechner, DJ Adams, Piotr Tesny and Vipin VijayKumar, written here from SAP's abstract; the recording is not yet published.
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