Status
Live
Production-ready
Data & Analytics
Data Analyst Agent
Deterministic validation agent that enriches SQL query results with business-friendly labels, coerces PostgreSQL string-typed numbers to actual numbers, filters garbage rows, strips internal columns, and translates raw c
Enterprise AssistantLiveInternal (Colaberry Enterprise)Verified
Department
Data & Analytics
Enterprise Assistant department for Colaberry Enterprise agents
Source
Internal (Colaberry Enterprise)
Built by Colaberry
About
About the Agent
What this agent does, the challenges it addresses, and where it delivers value.
Deterministic validation agent that enriches SQL query results with business-friendly labels, coerces PostgreSQL string-typed numbers to actual numbers, filters garbage rows, strips internal columns, and translates raw column names in insights to business language. No LLM calls - pure functions with sub-2ms latency.
Challenges This Agent Addresses
- 1**Business Intelligence**: Ensures query results display "Total Leads" instead of "lead_count" in reports and dashboards
- 2**Executive Reporting**: Removes technical database artifacts from insights before presenting to non-technical stakeholders
- 3**Data Quality**: Filters out meaningless zero-value rows that would clutter charts and tables
Workflow
How the Agent Works
Step-by-step operational flow showing how this agent processes tasks end-to-end.
1
Step 1
**Strip internal columns**: Removes columns not useful for display (id, uuid, created_by, updated_by, deleted_at, and most _id columns)
2
Step 2
**Coerce numeric strings**: PostgreSQL returns bigint COUNT/SUM results as strings. The agent identifies numeric-looking strings in known aggregate columns (_count, _total, value, amount, score, etc.) and converts them to actual JavaScript numbers
3
Step 3
**Filter garbage rows**: Removes rows where all numeric values are zero or null, keeping only rows with at least one positive value
4
Step 4
**Enrich insight labels**: Scans insight messages for raw column names (e.g., "error_count", "avg_duration_ms") and replaces them with business labels (e.g., "Errors", "Avg Duration (ms)")
5
Step 5
**Business dictionary**: Maintains a 100+ entry mapping of raw column names to labels covering counts, rates, scores, financial fields, entity fields, statuses, and time fields
Execution Modes
Trigger: event (invoked during query response assembly)
Data
Inputs & Outputs
What data this agent consumes and the artifacts or actions it produces.
Input Data
- List of `SqlResult` objects (rows of raw database query results)
- List of `Insight` objects (analytical findings with raw metric names)
Deliverables
- Enriched `SqlResult` list with:
- Numeric string values coerced to numbers
- Internal columns stripped (id, uuid, created_by, etc.)
- Garbage rows removed (all-zero or all-null numeric values)
- Enriched `Insight` list with:
- Raw column names replaced by business labels in messages
- Metric fields translated to business-friendly names
Core Tasks
- Data & Analytics
Integrations
Systems Connected
Internal systems, APIs, and tools this agent integrates with.
Tools & APIs
Invoked by the **Query Engine** during response assembly**Chart Validation Agent** uses `getBusinessLabel` for chart title generation**Report Quality Agent** uses `getBusinessLabel` for insight enrichmentBusiness dictionary is the single source of truth for column name translation across the assistant layer
Specifications
Agent Specs
Technical specifications, requirements, and deployment details.
Related
Related Agents
Other agents in the same department or industry.
Enterprise AI
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