Building a Test Harness to test AI Produced Code
Why Coding Agents Can’t Validate Their Own Output (And How to Fix the 80/20 Inversion) A passing unit test does not prove a feature works. It only proves a fun…
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Data Patterns in Cloud
Reference: Adapted and expanded from The 12 Data Architecture Patterns Every Data Engineer Should Master. The Data Lake vs Warehouse debate is effectively over…
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Machine Learning APIs with the Model Context Protocol (MCP)
In enterprise AI, a major engineering challenge is bridging the gap between existing backend systems and the fast-growing ecosystem of autonomous AI agents. A …
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Mastering Enterprise Agent Success with Microsoft IQ
A striking prediction by industry analysts at Gartner reveals that more than 40 percent of agentic AI projects will be canceled by the end of 2027. When these …
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Azure AI Foundry / Anthropic Claude Multi-Agent Pattern
Bringing Anthropic’s Claude into Azure AI Foundry is a massive shift for enterprise AI. Previously, if a bank wanted to use OpenAI’s GPT models, they used Azur…
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From Retrieval to Reasoning: Demystifying Microsoft Foundry IQ
Building production-grade Generative AI for the enterprise requires moving far beyond simple vector search or basic RAG pipelines. When launching true enterpri…
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The Fabric Overlay: Why OneLake Above Delta Lake and ADLS is the Ultimate Data Strategy
For years, building an enterprise data lake followed a familiar blueprint: spin up Azure Data Lake Storage (ADLS Gen2), format your data into Delta Lake (or Pa…
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Scaling and Hosting Intelligent AI Agents in Production
Overview Moving AI agents from a prototype “promise” to a production reality requires a shift in focus from model selection to engineering rigors. …
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