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.
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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
Assembling Context for the Model
Assemble model context from Qdrant results in .NET: deduplicate exact text, check a local token allowance, and trace citations to admitted evidence.
Payload Modeling and Metadata Filters in Qdrant with .NET
Extend the .NET Qdrant chunking sample with payload metadata, keyword indexes, and filters that restrict retrieval to published runbooks.
Your AI System Needs Backpressure
Use admission control, bounded waiting, and shared provider limits to keep slow AI work from turning overload into an outage, with C# examples.
Chunking for Qdrant in .NET Before It Becomes a Quality Problem
Extend .NET and Qdrant indexing with section boundaries, paragraph overlap, deterministic chunk IDs, and queries that expose incomplete evidence.
Fallback Models Are Not Transparent Replacements
Qualify fallback models against the feature contract, define eligible failures, bound attempts, and make reduced behavior visible to callers.