I’m Lukas Walter, 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
51 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
Designing an AI Service Layer
How to place model access, prompts, retrieval, tools, and application policy behind a maintainable use-case boundary in a .NET application.
Trust Boundaries Around AI Features
How to keep model proposals separate from trusted application behavior with authorization, approval policy, current-state checks, and least privilege.
Why Retrieval Exists
Why application knowledge does not belong in model weights, what retrieval contributes, and when a prompt, database query, or deterministic tool is simpler.
The Model Is Only One Dependency. Map the Rest.
How to map the execution, prerequisite, and operational dependencies around an AI feature and give each interaction a usable failure contract.
Stop Letting Provider SDKs Define Your .NET AI Architecture
How Microsoft.Extensions.AI keeps provider SDKs at the edge of .NET applications through shared abstractions, dependency injection, and middleware.