AI
5 core pillars of Large Language Model (LLM) operations
The below outlines the 5 main pillars of using an AI LLM. The terminology is the basis of discussing ‘AI’ within an enterprise. 1. Tokenization: Un…
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AIAzure Agentic AI and Foundry
Based on the journey from initial Power BI insights to enterprise-grade automation in Azure AI Foundry. From Insights to Autopilot: Building Enterprise AI Agen…
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AzureAzure, Data and the Semantic Layer
Definition: What is a Semantic Layer? A Semantic Layer is a business representation of corporate data that helps end users and AI agents access data autonomous…
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AIEnterprise AI Agents in Azure
Overview of Microsoft Foundry as a unified platform for building, optimizing, and governing enterprise AI agents. Microsoft Foundry: The Enterprise AI Agent Fa…
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AIAzure & AI: Unifying the Enterprise Agent Stack
In the rush to adopt AI, many organizations have accidentally created a fragmented agentic stack. Every department from HR to Finance is building its own agent…
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AICopilot Agent Management
Copilot Studio The lifecycle of a copilot agent begins in the agent workshop where a producer uses the copilot studio portal to develop the core logic. This in…
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AIArchitecture Summary: Secure Copilot Deployment for Data, RAG Access
This architecture defines a highly secure, enterprise-grade path for deploying Copilot Studio agents within a regulated environment. It prioritizes zero-trust …
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AIPowerBI, Ontology, Semantics, and AI Agents
In the modern enterprise, a silent revolution is occurring within the layers of business intelligence. For years, we viewed Power BI as a simple tool for chart…
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AIAzure AI Foundry — Agents as key entities in the Architecture
Microsoft’s AI Foundry Agents offer a declarative, configuration-first model for building agents. The architecture, outlined in Microsoft Learn, revolves aroun…
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