Building an Analytics Agent with Joule Studio

What you will learn

• Explain what an analytics agent adds over a grounded Q&A chatbot, and why the decision loop — not retrieval — is the agent
• Describe the three components of a Joule Studio agent (instructions, tools, knowledge sources) and how tools reach Datasphere or HANA Cloud through an MCP-capable BTP destination
• Design one analytics agent end to end for a named business question: tool schemas, tool-call order, and a written refusal rule for unsupported answers
• State when to split an agent into a multi-agent design versus keep one agent with a short tool list
• Separate, correctly, how generative-AI-hub token/CU metering and Joule agent AI-Unit metering apply, and avoid carrying a lapsed promotion into a cost estimate
• Build a production-readiness checklist for a Joule agent: tool ownership, evaluation set, call logging, fallback behaviour, review cadence

Module overview

Who this is for. You have built a first Joule agent in “Building Joule Agents with Joule Studio” and you know what MCP is from “MCP & A2A for SAP Systems”. This module does one thing: it takes those pieces and builds a single, complete analytics agent — an agent whose job is to answer an ambiguous business question ("why did gross margin slip in EMEA last quarter?") by calling real tools, reconciling their answers, and explaining the number in words a finance director can act on. “An MCP Server over Datasphere and HANA — Hands-On” covers how to build the MCP server this agent calls; “RAG over SAP Documents with the Generative AI Hub and Document Grounding” covers grounding it in documents; this module covers the agent itself — its instructions, its tools, its guardrails, and what a run costs.

Source: SAP — Joule Studio.

Prerequisites

• Building Joule Agents with Joule Studio (agent fundamentals)
• MCP & A2A for SAP Systems (protocol concepts)
• Working knowledge of a Datasphere or HANA Cloud data model
• Recommended alongside the MCP server this agent's tools call and document grounding for the second tool

Outcomes

• Produce a written agent brief (decision, tools, instructions, guardrails) for a real analytics question from your current engagement.
• Ship a tool schema and instruction set that make an agent refuse an unsupported answer instead of guessing.
• Justify, in one paragraph, a single-agent versus multi-agent design decision.
• Build a metering line in a cost estimate that correctly separates AI Units from generative-AI-hub capacity units.