Uday Kapur
Data Engineer building AI-ready data platforms.
// melbourne, australia
// latest
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Agent Intelligence Outran Agent Reliability
Model capability leapt while production reliability lagged. The next eighteen months belong to verification loops, harness engineering, and teams that close the gap.
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Agentic Systems Have Three Learning Layers and the Model Is the Slowest
Agentic systems improve at the model, harness, and context layers. Most teams wait for the model. The compounding advantage sits in the two you own.
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Loop Engineering for Microsoft Fabric: AI Agents, TE2, and Building DAX at Scale
TE2 C# scripts and an AI agent built hundreds of DAX measures across twenty-five Fabric semantic models. Loop engineering, not prompt engineering.
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What Makes a Data Platform AI-Ready Before Anyone Adds an LLM
Organisations chase AI by bolting models onto fragile pipelines. The real work is upstream: quality, lineage, contracts, and governance that make AI safe.
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Why Your Semantic Model Does Not Belong in a PBIX File
Twenty-five Power BI reports on one Fabric lakehouse. Decoupled semantic models, medallion layers, Direct Lake, and PBIX files with nothing in them.
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Tokens Are Currency and the Subsidy Will Not Last
AI providers are subsidising tokens to drive adoption. When that ends, engineers without optimisation habits will feel it. Is your spend big enough to care?
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Designing Data Platform Architecture for the Team You Will Have in Two Years
Most platform architecture is designed for today's team. In two years the team is bigger, the requirements are different, and the shortcuts are load-bearing.
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Why Your dbt Project Will Become Unmaintainable
dbt makes it easy to start a data transformation layer. It also makes it easy to build one nobody can reason about six months later. The patterns that rot.
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Test Harnesses for Data Pipelines: Engineering Confidence at Every Layer
Testing data pipelines isn't like testing application code. Messy inputs, probabilistic outputs, expensive infrastructure. How to build harnesses that work.
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Data Quality Gates That Actually Work in Production
Most quality gates fire so often they get ignored or sit silent while bad data flows downstream. Build gates that catch real problems without crying wolf.