Azure Fabric :: Part 5 — Governance, Security and DevOps with Agentic AI

Series: E-Commerce Data & AI Control Tower — Part 5 of 6

Scenario: An international e-commerce business operates petabyte-scale batch and streaming data products across Microsoft Fabric OneLake and Azure Databricks. It needs consistent identity, privacy, lineage and deployment controls across teams and regions. Examples and financial estimates are illustrative; confirm controls with your security, privacy and platform owners.

How agentic AI supports Parts 1–4

The agent should assist each stage with evidence, recommendations and policy checks. It does not replace architecture ownership, data owners or production approvals.

1. Part 1 — Plan the platform: summarize source inventory, data classes, freshness needs and workload profiles; compare candidate ingestion and storage patterns; draft sizing questions, dependency maps and architecture decisions for review. Time benefit: reduce repeated discovery and documentation effort.
2. Part 2 — Ingest at scale: correlate source limits, CDC lag, API throttling, network throughput, checkpoint state and retries; prioritize late or stuck feeds; recommend a bounded replay or source-side investigation. Cost benefit: avoid unnecessary full reloads and uncontrolled retries.
3. Part 3 — Process and protect: identify schema drift, duplicate events, failed quality checks and possible PII exposure from approved metadata; create a review queue and suggest rule or quarantine updates. Time benefit: accelerate triage while data owners approve rule changes.
4. Part 4 — Operate the control tower: combine pipeline, capacity, network and freshness signals; explain likely causes with evidence; route an approved, idempotent action through a guarded workflow. ROI benefit: reduce time to diagnose and recover while tracking the actual cost of the agent.