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For a detailed list of all changes, see the Changelog. For details about versioning, see our Versioning Policy.
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2026-08-13

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Summary

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Improvements

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Fixes

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Improvements

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Opt out of dynamic tool discovery

Version 6.1.23 made Visualize This discover its tools on demand rather than sending the whole tool set to the model at the start of a conversation. Dynamic tool discovery remains the default, but it can now be turned off when a deployment prefers the previous behaviour, for example to compare answer quality or to work around a model that handles the search tool poorly.Set spring.ai.chat.client.tool-search-advisor.enabled=false in Java, or AiConfig.dynamic_tool_discovery in the Atoti Python SDK. See Configure Visualize This.

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Fixes

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Clearer reporting when the license does not enable AI

A license that does not enable the AI components is now reported at startup. When an Atoti AI module is on the classpath but the license misses the ai-essentials component, a warning explains that the /activeviam/ai REST service is not registered. This previously surfaced only in Atoti UI, as “Unable to find the AI service on any of the servers”, with nothing in the server logs to explain it. The ai-extension component is reported the same way when the MCP server starter is present.

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MCP server stays off without the Atoti Intelligence SDK tier

A license without the ai-extension component now keeps the MCP server off even when the application sets spring.ai.mcp.server.enabled=true. The tier check is applied at the highest property precedence, so it can no longer be overridden from application.yml, an environment variable or a command-line argument. A license enabling ai-extension without ai-essentials no longer starts the MCP server either, as the Atoti Intelligence SDK offer builds on Atoti Intelligence Essentials.

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Reverse proxies no longer buffer the chat event stream

AG-UI streaming responses now carry X-Accel-Buffering: no and Cache-Control: no-cache, no-transform so that reverse proxies do not buffer or compress the event stream. A buffering proxy delivered the tool-call events to the browser only at the end of the run, so client tools always hit the server-side timeout. Client tool timeouts, stale tool results and retried chat prompts are now logged.See Monitoring.
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2026-07-09

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Summary

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Improvements

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Fixes

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Improvements

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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.

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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.

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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.

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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.

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Fixes

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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.
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2026-05-29

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Summary

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New features

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Improvements

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New features

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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 Set up a custom disclaimer in Java or in Python.

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MCP credentials page

A new page bundled with starter-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.

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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 in Java or in Python.

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Improvements

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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.

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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.

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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.

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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.

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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.