Tuesday, September 22, 2026 | 10 mins read09/22/2026 | 10 mins read
This article explores the shift from AI that answers to AI that acts. As AI Agents gain more autonomy, the challenge is no longer only capability—it is trust. The article argues that Agentic AI makes Data Governance more important, because poor or misunderstood data can lead not only to bad answers, but to bad actions. Trusted AI requires more than governed data. Agents need business context: understanding entities, relationships, semantics, signals, and how the business actually works. The article connects Data Governance, business semantics, Fabric IQ, Responsible AI, observability, permissions, and human control into a foundation for Trusted AI Agents. The central idea is: Capability will move AI forward. Context will help it understand. Governance will keep it grounded. Trust will determine how much autonomy we give it.
Tuesday, September 1, 2026 | 9 mins read09/01/2026 | 9 mins read
This edition explores why the question should not be Databricks or Fabric? but rather What business outcome are we trying to achieve, and where is the best place to achieve it? The article compares architectural considerations across data integration, warehousing, BI, governance, real-time analytics, AI and agents, automation, and observability. Databricks offers strong engineering, lakehouse, streaming, and AI capabilities, while Fabric provides an integrated SaaS experience across Data Factory, OneLake, Power BI, Real-Time Intelligence, and AI. The key message: organizations do not need to force every workload into one platform. A combination of technologies may deliver greater business value. Ultimately, architecture should be business-centric, not platform-centric—choosing the right tool for each requirement rather than simply using what is already available.
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