I’m Lukas, a software engineer and cloud/AI consultant focused on building robust, scalable systems with .NET and Azure.
I write practical guides on .NET, AI, agents, evaluation, observability, retrieval, testing, guardrails, and Azure.
Engineering Tips
65 concise, searchable notes for .NET, Azure, and production AI systems. New tips are added regularly.
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Microsoft Agent Framework: Introduction
Where Agent Framework fits in the .NET AI stack, from agents and tools to MCP integration and workflows.
Read the introductionRAG with EF Core and pgvector
Build RAG retrieval in .NET with PostgreSQL, pgvector, and EF Core.
Read the articleObservability for Agents
Trace model calls, tool calls, token usage, and failures with OpenTelemetry, Aspire, and Application Insights.
Read the guideBuild and deploy an MCP server with .NET and Azure Container Apps
Build, containerize, deploy, and call a Streamable HTTP MCP server with the official .NET SDK.
Read the guideLatest posts
Your First Qdrant Collection
Run Qdrant locally, connect with the official .NET client, and create a collection whose vector size and distance match the embedding model.
AI Systems Need Runtime Budgets
How to bound time, tokens, retries, tool calls, and estimated cost across a complete AI execution instead of limiting each call in isolation.
Provider Independence from Day One
A C# implementation for keeping provider coupling at explicit boundaries while making model capabilities and provider-specific behavior visible.
Retries Are Not a Recovery Strategy
Why bounded retries help with transient faults but cannot resolve unknown outcomes, partial workflows, or duplicated side effects in AI systems.
Embeddings in .NET
How to generate text embeddings with Microsoft.Extensions.AI and keep the model, dimensions, comparison metric, and indexing behavior compatible.