live wire
▸Snowflake’s Cortex Agent quickstart routes metrics and transcripts with prompt rules—but documents no authorization boundarySnowflake▸Oracle Select AI generates SQL/PGQ for property graphs—but errors if one AI profile mixes graphs with tables or viewsOracle▸Domo’s data team standardizes MCP builds with shared scaffolding; beta toolkits inherit user permissionsDomo▸Databricks proposes five outcome signals for agentic BI—and says dashboard counts and license usage miss decision qualityDatabricks▸One vibe-coded database app needed 56+ human test hours and three repair rounds despite AI Studio’s verification claimsMancas & Mancas / arXiv▸BigQuery AI.IF’s default cost mode trains a local model for most rows—and drops row-error thresholdsGoogle Cloud▸AWS’s multi-tenant data-agent pattern gives the broker Lambda no data permissions; Cognito roles carry Lake Formation scopeAWS Big Data Blog▸Amazon OpenSearch’s optimized engine adds nested agent traces, but AWS reserves it for log analytics—not vector searchAWS▸Two interpretation-based provenance semantics cannot express SQL bag difference, new theorem showsKolaitis / arXiv▸Databricks Feature Views reuse one definition for training and live scoring, but online lookup still returns latest available stateDatabricks▸Databricks Genie’s Slack and Teams previews default channel answers to public, even when viewers lack source-data accessDatabricks▸Snowflake’s two-tool GraphRAG agent beat a seven-tool graph setup 70% to 60% on 22 ontology questionsSnowflake Engineering▸Semantix opens an Apache-2.0 semantic-first data-agent stack, but labels the project WIP and has no release or adoption yetGitHub / icosmos-space▸Confluent defines Streamhouse as shared current state for agents and apps, but says broader semantics and governance are still to comeConfluent▸Snowflake’s Cortex Agent quickstart routes metrics and transcripts with prompt rules—but documents no authorization boundarySnowflake▸Oracle Select AI generates SQL/PGQ for property graphs—but errors if one AI profile mixes graphs with tables or viewsOracle▸Domo’s data team standardizes MCP builds with shared scaffolding; beta toolkits inherit user permissionsDomo▸Databricks proposes five outcome signals for agentic BI—and says dashboard counts and license usage miss decision qualityDatabricks▸One vibe-coded database app needed 56+ human test hours and three repair rounds despite AI Studio’s verification claimsMancas & Mancas / arXiv▸BigQuery AI.IF’s default cost mode trains a local model for most rows—and drops row-error thresholdsGoogle Cloud▸AWS’s multi-tenant data-agent pattern gives the broker Lambda no data permissions; Cognito roles carry Lake Formation scopeAWS Big Data Blog▸Amazon OpenSearch’s optimized engine adds nested agent traces, but AWS reserves it for log analytics—not vector searchAWS▸Two interpretation-based provenance semantics cannot express SQL bag difference, new theorem showsKolaitis / arXiv▸Databricks Feature Views reuse one definition for training and live scoring, but online lookup still returns latest available stateDatabricks▸Databricks Genie’s Slack and Teams previews default channel answers to public, even when viewers lack source-data accessDatabricks▸Snowflake’s two-tool GraphRAG agent beat a seven-tool graph setup 70% to 60% on 22 ontology questionsSnowflake Engineering▸Semantix opens an Apache-2.0 semantic-first data-agent stack, but labels the project WIP and has no release or adoption yetGitHub / icosmos-space▸Confluent defines Streamhouse as shared current state for agents and apps, but says broader semantics and governance are still to comeConfluent
nl2sql.ai
newsEnterprise AI

IBM Planning Analytics removes the all-or-nothing choice for custom agents

Workspace 3.1.11 lets teams add their own agent while keeping IBM-managed analysis features active, splitting customization from model operations.

Customer agent and IBM-managed analytics working in parallel.
AI-generated illustration
By The News Desk· Sep 25, 2026the quick take — two AI hosts go live when you do

IBM has changed the deployment choice behind its Planning Analytics Agent. In Planning Analytics Workspace 3.1.11 and 2.1.24, customers can connect a custom agent while continuing to use IBM-managed AI for the product’s built-in analytical features.

That sounds like a small configuration change. It is a meaningful operating boundary.

From one AI mode to two cooperating layers

IBM says the Planning Analytics Agent previously ran in one of two modes: fully IBM-managed or fully customer-managed. A team that wanted to bring in a custom agent—IBM gives a watsonx Orchestrate agent as its example—also had to provide the large language model used by every Planning Analytics AI feature.

The new hybrid model separates those decisions. A customer can build an agent around its own workflows, terminology or external data sources and connect it to Planning Analytics Workspace. At the same time, IBM-managed AI can continue powering the native exploration reports, outlier detection, impact analysis and other built-in capabilities.

For platform teams, the practical gain is not merely “more agents.” It is the ability to customize the business workflow without taking on model provisioning for the entire planning product. The release does not eliminate integration work: teams still need to decide what the custom agent may access and where requests should be routed. But it removes the earlier coupling between adding a custom agent and operating the models behind IBM’s native features.

MCP widens the integration path

IBM recommends watsonx Orchestrate as the starting point for custom agents, but it also says the Planning Analytics Agent’s MCP tools can be used with agent platforms that support remote MCP servers and OAuth. That gives teams a standards-based route beyond IBM’s own agent builder while retaining an authenticated connection path.

The first availability is in Planning Analytics Workspace. IBM says support for the hybrid model in Planning Analytics for Excel is coming soon, so teams should not assume the two client surfaces have feature parity yet.

Reports also get a presentation upgrade

The same release improves the agent’s exploration analysis report, a time-stamped PDF generated from a data view. IBM says the report now surfaces a wider range of patterns, uses clearer narrative language, and adds a table of contents, page breaks and layout improvements.

Those changes improve distribution, not validation. A polished PDF can make agent output easier to review and share, but the release post does not publish an accuracy evaluation for the new narratives. Teams should treat the report as an analytical starting point and keep their own review process for material planning decisions.

The larger release signal is architectural: IBM is no longer forcing customers to choose between a customized agent and the vendor-managed intelligence already embedded in Planning Analytics. That is a more realistic enterprise model, where teams often want ownership of the workflow without owning every model underneath it.

sources

Filed by The News Desk. Corrections: desk@nl2sql.ai · Our standards →

comments · 0

    Comments are moderated before they appear. Your email is used once to confirm it is you — never shown, never sold. Corrections and questions get an answer from the desk when we have one.

    more from the desk