Summary
Improvements
- Auto-Explain configuration from the Atoti Python SDK
- Visualize This configuration from the Atoti Python SDK
- Root-cause analysis without an LLM
- More efficient tool selection in Visualize This
Fixes
Improvements
Auto-Explain configuration from the Atoti Python SDK
Auto-Explain can now be configured directly from the Atoti Python SDK.Cube.auto_explain exposes the tuning
constants: drill-down depth, contribution and variation thresholds, and entropy and member limits. Hierarchies can
also be included in or excluded from the analysis, both overall and for individual measures.See Set up Auto-Explain in Python.Visualize This configuration from the Atoti Python SDK
The chat assistant can now be configured directly from the Atoti Python SDK.Session.chat exposes the chat
configuration, including the ability to read and customize the system prompt.See Set up Visualize This in Python.Root-cause analysis without an LLM
Auto-Explain’s root-cause members, contribution percentages, and contribution tables are now always available without any AI provider configured. An LLM is only needed to generate the optional AI summary.See Set up Auto-Explain in Python.More efficient tool selection in Visualize This
Visualize This now discovers the tools it needs on demand instead of loading the full tool set at the start of a conversation. This reduces token consumption and improves how the assistant selects the right tool for a request.Fixes
Chat filters now include their members
Filters added by the assistant during a conversation now reliably resolve their members in the cube. Previously, some filters could be added without members, which prevented them from taking effect.Summary
New features
Improvements
- Observability for Auto-Explain and Visualize This
- Richer AI cube descriptions
- Spring AI upgraded to 1.1.6
- Atoti starts without an AI license
- AI configuration from the Atoti Python SDK
New features
Customizable AI disclaimer
LLM-generated content can be inaccurate. To help organizations meet compliance and end-user transparency requirements, Atoti Intelligence now returns a disclaimer with every AI response, displayed in the UI alongside the answer. A default disclaimer is used when none is configured, and the text can be tailored per deployment.See Customize the AI disclaimer.MCP credentials page
A new page bundled withstarter-ai-mcp-server lets users sign in to Atoti and mint an OAuth token to connect their MCP
client (Claude Desktop, Postman, etc.) to the Atoti MCP server. The page streamlines onboarding by replacing manual
token generation steps with a guided flow.See MCP credentials page.Auto-Explain available from Visualize This
Auto-Explain is now exposed as a Spring AI tool that Visualize This can invoke during a chat conversation. Users can ask the assistant to explain a variation or contribution from within the same chat, without switching to a separate Auto-Explain view.See How to set up Visualize This.Improvements
Observability for Auto-Explain and Visualize This
Both Auto-Explain and Visualize This now emit OpenTelemetry spans and metrics covering the full request lifecycle — request handling, recursive analysis, summary generation, conversation lifecycle, prompt execution and tool invocations. This makes it possible to monitor performance, debug failures and understand cost in production.See Monitoring.Richer AI cube descriptions
Cube descriptions sent to the LLM in Visualize This now include XMLA properties and nest dimension, hierarchy and level descriptions under their parent. The model receives a structured, more accurate picture of the cube, which improves the quality of its responses to discovery and analytical questions.See How to configure Visualize This.Spring AI upgraded to 1.1.6
Atoti Intelligence now depends on Spring AI 1.1.6 (up from 1.1.2), picking up upstream bug fixes and chat-model improvements.See Compatibility.Atoti starts without an AI license
When an application is started with a license that does not include the AI feature, Atoti now logs an informational message and continues startup instead of throwing. Non-AI features remain available, making the AI starters safe to include in applications regardless of license content.AI configuration from the Atoti Python SDK
SessionConfig in the Atoti Python SDK now handles AI properties, so AI connection and chat options can be configured
directly from Python alongside the rest of the session configuration.