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
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newsGOOGLE CLOUD

Google’s Data Agent Kit reaches GA—but the agent still spends your cloud budget

The free toolkit can inspect and change more than 15 Google data services; its real production boundary is inherited IAM plus metered queries and jobs.

Agent-assisted data work versus the inherited cloud controls and costs it must obey.
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By The News Desk· Oct 1, 2026the quick take — two AI hosts go live when you do

Google Cloud has made Data Agent Kit generally available, turning a preview developer aid into a supported bridge between coding agents and more than 15 Google Data Cloud services. The kit combines Model Context Protocol tools with Google-authored skills and works from VS Code-compatible editors, Cloud Shell, Cloud Workstations, Claude Code, Codex and Google’s Antigravity tools. Google says the kit itself has no additional charge, while the cloud services invoked by an agent retain their standard pricing. Source: Google Cloud’s GA announcement

GA expands the execution surface

The GA release adds BigQuery Graph, Bigtable and Managed Service for Apache Spark. An agent can map relational tables into a proposed property graph, generate GQL, design a Bigtable row key, query Iceberg tables through serverless Spark, or connect an open lakehouse to AWS Glue and Databricks Unity Catalog. It can also turn working logic into dbt or Dataform models and schedule those with notebooks through Managed Service for Apache Airflow. Source: Google Cloud’s GA announcement

That breadth is the important change for data teams: the same conversational interface can move from schema discovery and SQL generation into resource creation, pipeline orchestration and troubleshooting. Google says graph creation includes a plan for the user to approve, and failed Airflow or Spark tasks can be handed to the agent for a proposed fix. Those approval points matter because this is no longer only a query-writing assistant. Source: Google Cloud’s GA announcement

Identity is the control plane

Data Agent Kit acts as the signed-in user or an impersonated service account. Google says existing row- and column-level policies therefore apply, while administrators can govern MCP access through IAM, inspect traffic with Model Armor, and constrain agents with VPC Service Controls and Principal Access Boundary policies. In practice, teams should treat the kit as another execution client: an overly broad human or service-account role becomes an overly broad agent role. Source: Google Cloud’s GA announcement

Cost is a separate boundary. The toolkit is free, but BigQuery scans, Spark sessions, database operations and other invoked services are not. Google says its skills encourage cost-aware patterns such as checking partition keys and running BigQuery dry runs before execution. That is useful guidance, not a hard budget ceiling; production rollouts still need project-level quotas, billing alerts and narrowly scoped identities around agent workloads. Source: Google Cloud’s GA announcement

The practical deployment test is therefore not whether the agent can write SQL. It is whether every tool call inherits the right identity, presents a review step before consequential changes, and stays inside a measurable spend envelope. GA makes the integration easier; it does not transfer ownership of those controls from the platform team to the agent.

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

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