Agami Founders - Agami Insights (Page 2)

Query Business Central data with AI: eight dimension columns on the G/L Entries page in the app, two on the ledger table in the warehouse. Six of them were FlowFields, not columns.

Data Agent

Six of Eight Business Central Dimension Columns Are Computed on Read

Business Central shows eight dimension columns on the G/L Entries page. Six are computed on read, so only two survive replication. The semantic model resolves the rest through the dimension set.

Query Sage Intacct data with AI: eight dimensions in the app, zero dimension tables in the warehouse. Fivetran lands two strings per row, no lookup, no hierarchy.

Data Agent

Sage Intacct's Dimensions Don't Land. The Semantic Model Puts Them Back.

Sage Intacct's product is its dimensions, and none of them lands as a table. Every dimension question bins on a string that drifts on rename, until the semantic model binds it to the id instead.

Jira service management request type reporting warehouse: Customer Request Type lives in issue_field_history under an instance-specific customfield id, not as a column on issue

Data Agent

Your Jira Portal Has Dozens of Request Types. Your Warehouse Has Three.

Customer Request Type is not a column on issue. It lives under a customfield id that differs per instance, so the obvious grouping returns three buckets. Bind the id once in the semantic model.

NetSuite multi-book double counting: one transaction line posts a debit and a credit per accounting book, so a two-book estate lands four rows and revenue reads at twice the true figure

Data Agent

One NetSuite Dollar, Counted Twice, Until the Semantic Model Says Once

NetSuite's reports filter the accounting line to one book automatically. The warehouse copy does not, so a naive SUM adds every dollar once per book. The semantic model is where that filter lives.

ServiceNow semantic model inheritance: the same query's joins flagged undefined_relationship before, and governed after the inheritance is declared

Data Agent

How to Build a ServiceNow Semantic Model That Governs Inherited Tables

Declare the inheritance join and the missing company FK, sign them off, and the incidents-by-customer chain comes back governed instead of flagged.

ServiceNow incident task join: incident extends task through inheritance, so the two tables share a sys_id and no foreign key connects them

Data Agent

In ServiceNow, Incident and Task Are the Same Record

Incident extends task, so the join between them is not a foreign key and introspection has nothing to infer. The semantic model is where you declare it once, for every agent.

Stopping Salesforce pipeline double counting: a proposed relationship moving from unreviewed to approved with a sign-off block that records the grain

Data Agent

How to Build a Salesforce Semantic Model That Can't Double-Count Your Pipeline

A four-step walkthrough on replicated Salesforce: introspect the schema, correct the grain and the custom fields, sign the definitions off, then verify the breakdown ties to the total.

Connect MCP server to Claude, Cowork and ChatGPT: a local server launched on your own machine, against an HTTPS endpoint dialled inbound from the vendor cloud

Data Agent

How to Connect Your Warehouse to Claude, Cowork and ChatGPT

Claude Desktop launches an MCP server on your machine. Cowork, claude.ai and ChatGPT connect inbound from the vendor's cloud. Which route you need is decided by one question.

How to query Salesforce data with AI: the same pipeline question answered at two grains, giving 245 million and 98 million

Data Agent

How to Query Salesforce Data with AI Without Double-Counting Your Pipeline

The QBR needs eight quarters of pipeline by product. Salesforce cannot produce it, so the data lands in a warehouse. There the same question overstates pipeline 2.5x and reorders the slide.

data lake without a context layer: state = 6, a sys_id for assigned_to and ACV_Renewal_2019__c beside what each one actually meant, Resolved, a person and Annual Contract Value

Data Agent

The Data Lake Promise Might Finally Be Coming True

We dumped Salesforce and ServiceNow into the lake and got a landfill of codes and GUIDs. Why the missing ingredient was always context, and how AI finally makes it affordable.

fan trap chasm trap: two loans totalling 35,000 returned as 55,000 because the join to payments repeats loan 1 once per payment, with no error and valid SQL against a valid schema

Data Agent

The query we refuse to run

A fan trap or chasm trap can inflate an aggregate by 3x and nothing errors. Here is how our Analytics Agent detects the hazard from join cardinality and refuses to ship the wrong number.

Snowflake Cortex vs Databricks Genie vs BigQuery Gemini side-by-side architecture; cloud scope, semantic model, governance, assistant compatibility

Data Agent

Snowflake Cortex Analyst vs Databricks Genie vs BigQuery Gemini: warehouse-native AI compared

An honest head-to-head of the three warehouse-native AI products. Where each wins, where each loses, and the architectural blind spot they all share.