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.
Reliability
Separate ingestion health from retrieval readiness
A failed ingestion run should not stop retrieval while an accepted index remains queryable and eligible to serve.
Cap tool-call loops explicitly
Set a hard iteration limit on automatic function invocation and treat hitting it as an explicit application outcome.
What Happens When Your AI Feature Fails?
A practical way to identify failure modes, choose explicit application behavior, and test an AI feature before its successful path hides the hard decisions.
Use App Configuration for operational switches
Keep runtime switches and adjustable operational settings in Azure App Configuration instead of requiring a deployment for every change.
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.
Set request body limits intentionally
Choose global and endpoint-specific request body limits instead of inheriting accidental defaults for uploads and APIs.
Use streaming for large or frequent uploads
Use MultipartReader when buffered ASP.NET Core uploads put too much pressure on memory, temporary-disk capacity, or disk I/O.
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.
Choose rate-limiting policies by endpoint cost
Apply different ASP.NET Core rate limits to cheap reads and expensive AI or data-processing endpoints.