From 309700bef0f853f5d440064959ab9bc7975920c2 Mon Sep 17 00:00:00 2001 From: Rafael Riki Ogawa Osiro Date: Tue, 24 Mar 2026 12:11:46 -0300 Subject: [PATCH 1/3] Add Agent Q regulatory document automation demo video (BCBS 239) --- .typos.toml | 1 + .../ai-and-agents/deep-dive/agent-q-in-action.md | 6 ++++++ 2 files changed, 7 insertions(+) diff --git a/.typos.toml b/.typos.toml index 66767c5e76..04fec1d68e 100644 --- a/.typos.toml +++ b/.typos.toml @@ -6,6 +6,7 @@ MyApp = "MyApp" OpenAPIv3 = "OpenAPIv3" AKS = "AKS" IST = "IST" +BCBS = "BCBS" [files] extend-exclude = [ diff --git a/docs/settings/integrations/ai-and-agents/deep-dive/agent-q-in-action.md b/docs/settings/integrations/ai-and-agents/deep-dive/agent-q-in-action.md index 95528b56a8..1ef6f8d353 100644 --- a/docs/settings/integrations/ai-and-agents/deep-dive/agent-q-in-action.md +++ b/docs/settings/integrations/ai-and-agents/deep-dive/agent-q-in-action.md @@ -56,6 +56,12 @@ Create and manage data quality checks through natural conversation: The AI translates your intent into the appropriate rule type and parameters automatically. +#### Automating Controls from Regulatory Documents + +Agent Q can parse regulatory publications such as [BCBS 239](https://www.bis.org/publ/bcbs239.htm){:target="_blank"} (Principles for effective risk data aggregation and risk reporting), analyze their applicability to a specific datastore, and automatically create tagged quality checks — preserving full traceability back to the original requirement. + +
+ ### Anomaly Investigation Investigate quality issues conversationally: From a0b336f3e6c2497f033a72dc370cca364922876d Mon Sep 17 00:00:00 2001 From: Rafael Riki Ogawa Osiro Date: Tue, 24 Mar 2026 12:26:08 -0300 Subject: [PATCH 2/3] Improve Capabilities video description with detailed context --- .../integrations/ai-and-agents/deep-dive/agent-q-in-action.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/settings/integrations/ai-and-agents/deep-dive/agent-q-in-action.md b/docs/settings/integrations/ai-and-agents/deep-dive/agent-q-in-action.md index 1ef6f8d353..bc49c00e6d 100644 --- a/docs/settings/integrations/ai-and-agents/deep-dive/agent-q-in-action.md +++ b/docs/settings/integrations/ai-and-agents/deep-dive/agent-q-in-action.md @@ -25,7 +25,7 @@ To generate a token, navigate to **Settings** > **Tokens** and click **Generate ## Capabilities -This video demonstrates the power of one-shot prompting using the Qualytics MCP server: +This video demonstrates the power of one-shot prompting using the Qualytics MCP server — a single natural-language prompt instructs Agent Q to join data across two different datastores (Databricks and BigQuery), aggregate customer spending on a monthly basis, and author a quality check on the result, all without specifying technical details like join keys, rule types, or field mappings.
From c5d2d18b0d5526d568f4cc4154d392f40843ae37 Mon Sep 17 00:00:00 2001 From: Rafael Riki Ogawa Osiro Date: Thu, 26 Mar 2026 15:41:48 -0300 Subject: [PATCH 3/3] docs(agent-q-in-action: fix terminology: use "the model" instead of "Agent Q" for MCP video description --- .../integrations/ai-and-agents/deep-dive/agent-q-in-action.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/settings/integrations/ai-and-agents/deep-dive/agent-q-in-action.md b/docs/settings/integrations/ai-and-agents/deep-dive/agent-q-in-action.md index bc49c00e6d..a1a1182339 100644 --- a/docs/settings/integrations/ai-and-agents/deep-dive/agent-q-in-action.md +++ b/docs/settings/integrations/ai-and-agents/deep-dive/agent-q-in-action.md @@ -25,7 +25,7 @@ To generate a token, navigate to **Settings** > **Tokens** and click **Generate ## Capabilities -This video demonstrates the power of one-shot prompting using the Qualytics MCP server — a single natural-language prompt instructs Agent Q to join data across two different datastores (Databricks and BigQuery), aggregate customer spending on a monthly basis, and author a quality check on the result, all without specifying technical details like join keys, rule types, or field mappings. +This video demonstrates the power of one-shot prompting using the Qualytics MCP server — a single natural-language prompt instructs the model to join data across two different datastores (Databricks and BigQuery), aggregate customer spending on a monthly basis, and author a quality check on the result, all without specifying technical details like join keys, rule types, or field mappings.