kimtth Fabric Data Agent
Use this skill for Fabric Data Agent design and lifecycle tasks. Data Agents are read-only conversational analytics artifacts; they are not general-purpose automation agents.
Required References
- Data Agent reference: FABRIC-DATA-AGENT-CORE.md
- ALM reference: FABRIC-ALM-CORE.md
- Governance patterns: COMMON-CORE.md
Workflow
- Clarify the audience, business domain, and questions the Data Agent should answer.
- Select data sources and tables deliberately. Prefer certified semantic models for governed metrics, Warehouse/Lakehouse tables for relational exploration, and KQL databases for telemetry.
- Confirm permissions, capacity, tenant AI settings, cross-region constraints, and Purview/DLP implications.
- Draft the Data Agent instructions: glossary, source routing, metrics, fiscal calendar, ambiguity handling, and refusal rules.
- Add validated SQL/KQL example query pairs where supported.
- Build an evaluation set and test generated queries/intermediate steps before publishing.
- Plan sharing, monitoring, periodic review, and ALM promotion through Git/deployment pipelines where supported.
Delegation
| Need | Delegate |
|---|---|
| Querying or validating Lakehouse/Warehouse data with T-SQL | sqldw-consumption-cli |
| Querying or validating KQL database behavior | eventhouse-consumption-cli |
| Semantic model metadata or DAX validation | powerbi-consumption-cli |
| Source table design for Data Agent readiness | fabric-lakehouse, sqldw-authoring-cli |
| ALM and deployment of Data Agent configuration | fabric-alm-cicd |
| Governance/security review | FabricAdmin |
Must
- Keep Data Agent behavior read-only.
- Respect Purview, RLS/CLS, workspace roles, model Read permissions, and selected data-source scope.
- Use clear English instructions and examples.
- Validate generated SQL/KQL/DAX against the actual source schema when possible.
- Surface limitations early, especially row/column caps, unsupported unstructured files, semantic-model example-query limitations, and cross-region constraints.
Prefer
- Narrow, trusted source selection over broad workspace exposure.
- Business glossary and metric definitions embedded in Data Agent instructions.
- Evaluation questions covering happy paths, ambiguous wording, access-denied scenarios, and sensitive data boundaries.
- Published Data Agent descriptions that explain purpose, scope, owner, data freshness, and escalation path.
Avoid
- Using a Data Agent for write operations, remediation automation, or full dataset export.
- Adding raw files directly as sources; expose them through tables first.
- Routing certified KPI questions to raw Lakehouse tables when a semantic model owns the business logic.
- Ignoring Purview audit/eDiscovery implications for sensitive workloads.
Output Format
For design requests, return:
- Use-case and audience summary
- Source selection and routing rules
- Governance and permission model
- Data Agent instructions draft
- Example query plan
- Evaluation set
- Publish/share/ALM plan