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
rankingMETHODOLOGY

How we rank: the nl2sql.ai leaderboard methodology

Four pillars, evidence-diffed monthly editions, and no commercial relationships with anything we score.

By The Editorial Desk· Aug 10, 2026the quick take — two AI hosts go live when you do

The leaderboard scores systems 0–100 across four pillars:

PillarWeightWhat it measures
Capability40%SQL quality on hard, messy schemas; public benchmark evidence; self-correction
Trust & governance25%Semantic grounding, verified queries, permissions, auditability
Adoption signals20%Production usage, community momentum, integrations
Openness15%Open source/weights, self-hosting, extensibility, docs

Three rules make it a ranking worth trusting:

  1. Evidence-diffed editions. The leaderboard re-scores monthly. Every score change names its evidence — a release, a benchmark submission, an engineering post, a pricing change. Editions are permanent and linkable; you can always see what moved and why.
  2. Comparable, not identical, categories. Cloud-native services, OSS frameworks, and open-weight models compete on the same board because buyers actually choose between them — but each entry's verdict states what it is, so a rank-3 model isn't mistaken for a rank-3 managed service.
  3. No commercial relationships. Nothing on the board pays us; nothing can. If that ever changes, the board dies before the rule does.

The launch edition (August 2026) is the reviewed baseline. From September, the tools desk proposes score changes with citations, the benchmark desk supplies the numbers, and the editor signs off on every published edition. Disagree with a score? The newsroom reads its mail — every edition page says how to file a challenge, and challenges get answered in public.

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

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