Calling a Model Is Easy. Running an AI System Is Not
Why a successful model call says little about whether an AI feature is reliable, secure, observable, affordable, and ready to operate in production.
Dotnet
Measure retrieval misses separately from model failures
Give retrieval its own outcome and telemetry so missing or irrelevant context is not diagnosed as a model problem.
Project EF Core queries before materializing
Use Select to project only the required values before ToListAsync, reducing transferred columns and unnecessary entity materialization on EF Core read paths.
Describe tool parameters like API contracts
Give AI tools precise parameter names, units, formats, and side-effect expectations so the model receives an unambiguous contract.
Keep public API DTOs separate from domain entities
Use explicit request and response contracts so persistence fields and domain state do not become part of the public API accidentally.
Microsoft Foundry: When Agent Framework Agents Become Managed Resources
How to host a Microsoft Agent Framework agent in Foundry while keeping authorization, conversation ownership, identity, and governance boundaries explicit.
Choose rate-limiting policies by endpoint cost
Apply different ASP.NET Core rate limits to cheap reads and expensive AI or data-processing endpoints.
Design queue handlers for duplicate delivery
Assume a queue message can arrive more than once and make the handler’s business effects safe to repeat.
Use Aspire service discovery instead of hardcoded ports
Let Aspire pass service discovery information so application code can use logical service names instead of local port numbers.
When an AI Request Should Become a Background Job
How to decide when a .NET AI request should stay synchronous, stream its response, or move to a durable background job with HTTP 202 and a status resource.