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.
Ai-Engineering
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.
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.
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.
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.
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.
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.