live wire
▸IBM makes watsonx Orchestrate AgentOps, custom LLM judging and Bedrock-agent discovery generally availableIBM▸SchemaGate 0.1.45 fixes broken Oracle ADB wallet connections and an OCI stack pinned 28 releases behindSchemaGate▸PDI’s Amazon Quick procurement agent grounds spend answers in vendor, category and contract contextAWS Business Intelligence Blog▸BigQuery’s ML.METRICS example returns 0.84 accuracy but 0.30 macro-F1 on the same 100-row classification queryGoogle Cloud BigQuery docs▸SchemaGate 0.1.44 auto-selects sentence embeddings, lifting bundled-schema retrieval from 90/98 to 93/98SchemaGate▸SchemaGate 0.1.43 adds read-only SQL execution with per-principal table checks—and documents unauthenticated client assertionsSchemaGate▸Databox adds reusable AI Analyst Skills with personal/company scope, auto-matching and marketplace installsDatabox▸Fabric previews an AI builder for data-agent instructions, source guidance and example queriesMicrosoft Fabric▸Databricks trains data-agent retriever to stop early or spend bounded extra search steps, reporting 5.8-second latencyDatabricks▸ThoughtSpot adds SpotterCode coding agent to its Visual Embed PlaygroundThoughtSpot▸LongMemEval-S audit: 67–73% of restore-fixable 80k-budget errors came from evicted evidence under three policiesarXiv▸SchemaGate 0.1.42 adds dimension-aware retrieval and fixes complex multi-table SQL promptsSchemaGate▸Snowflake agent toolsets can silently drop inherited tools when callers lack accessSnowflake Documentation▸Looker’s VS Code extension reaches GA with MCP-assisted LookML generation, editing and validationGoogle Cloud Looker release docs▸IBM makes watsonx Orchestrate AgentOps, custom LLM judging and Bedrock-agent discovery generally availableIBM▸SchemaGate 0.1.45 fixes broken Oracle ADB wallet connections and an OCI stack pinned 28 releases behindSchemaGate▸PDI’s Amazon Quick procurement agent grounds spend answers in vendor, category and contract contextAWS Business Intelligence Blog▸BigQuery’s ML.METRICS example returns 0.84 accuracy but 0.30 macro-F1 on the same 100-row classification queryGoogle Cloud BigQuery docs▸SchemaGate 0.1.44 auto-selects sentence embeddings, lifting bundled-schema retrieval from 90/98 to 93/98SchemaGate▸SchemaGate 0.1.43 adds read-only SQL execution with per-principal table checks—and documents unauthenticated client assertionsSchemaGate▸Databox adds reusable AI Analyst Skills with personal/company scope, auto-matching and marketplace installsDatabox▸Fabric previews an AI builder for data-agent instructions, source guidance and example queriesMicrosoft Fabric▸Databricks trains data-agent retriever to stop early or spend bounded extra search steps, reporting 5.8-second latencyDatabricks▸ThoughtSpot adds SpotterCode coding agent to its Visual Embed PlaygroundThoughtSpot▸LongMemEval-S audit: 67–73% of restore-fixable 80k-budget errors came from evicted evidence under three policiesarXiv▸SchemaGate 0.1.42 adds dimension-aware retrieval and fixes complex multi-table SQL promptsSchemaGate▸Snowflake agent toolsets can silently drop inherited tools when callers lack accessSnowflake Documentation▸Looker’s VS Code extension reaches GA with MCP-assisted LookML generation, editing and validationGoogle Cloud Looker release docs
nl2sql.ai
releaseNEW ENTRANT

Datatide launches self-hosted conversational BI with an unusually explicit security boundary

The new MIT project combines natural-language analysis, scheduled reports, Skills and MCP—but plainly says its guard is lexical and its permissions stop at the dataset.

Datatide’s capabilities contrasted with its explicit security limits.
AI-generated illustration
By The Tools Desk· Sep 6, 2026the quick take — two AI hosts go live when you do

Datatide arrived on GitHub on September 6 as an MIT-licensed, self-hosted conversational-BI project. Its initial 0.1.0 package describes a TypeScript service built on the Pi coding-agent SDK, DuckDB and the Model Context Protocol SDK, with Node 22.19 or newer required. The repository had no tagged release, stars or forks when checked, so this is an entrant to watch—not adoption evidence. (repository metadata; package manifest)

More than a chat-to-SQL demo

The implemented surface is broad for a first public version. Datatide accepts CSV, Parquet and Excel uploads, and mounts PostgreSQL read-only through DuckDB. Users can ask multi-step questions across authorized datasets, generate ECharts visualizations, export answer tables, and create scheduled Markdown reports. Administrators can grant dataset access, inspect token usage and audit logs, mount external SKILL.md procedures, and configure stdio or Streamable HTTP MCP servers. (README)

That combination matters because it packages three objects that small NL2SQL repositories often leave separate: the data connection, the reusable analysis method, and the recurring report. Datatide’s default agent tools expose dataset metadata, guarded SQL execution, chart creation, skill loading and an MCP gateway. The project also documents a REST surface for dataset grants, session ownership, report history and administrative audit access. (README)

The useful part is the warning label

Datatide does not present those features as a finished enterprise control plane. Its own security section says authorization is dataset-level, with no row- or column-level policy. It also says the SQL guard is lexical rather than a full abstract-syntax-tree parser, even though it restricts execution to one SELECT or WITH statement and blocks DDL, DML, PRAGMA, ATTACH, file-reading functions, unauthorized objects, excessive rows and long runtimes. PostgreSQL is attached read-only, while external MCP services and configured models can still create their own network behavior. (README)

That disclosure gives evaluators a concrete acceptance plan. Before connecting sensitive data, test nested writes and dialect edge cases against the lexical guard; verify that two users with different dataset grants receive different object lists; and treat any requirement for tenant, row or column isolation as unmet until the project implements and tests it. Those are not hypothetical enterprise extras—they are the boundary the maintainer explicitly documents today. (README)

Datatide is therefore notable less for claiming a novel model than for shipping a coherent, inspectable workflow and naming where trust stops. The next evidence to watch is a tagged release, independent users, stronger SQL parsing, finer-grained authorization and published live-evaluation results. (repository metadata; README)

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