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.
Reliable-Ai-Systems
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.
Graceful Degradation for AI Features
How to keep an AI-enabled product useful when a dependency fails, without hiding reduced capability or returning unsafe substitutes.
What Should You Actually Evaluate?
Choose AI evaluation criteria from the feature contract: answer quality, retrieval, tool effects, safety, reliability, latency, and cost.
Human Approval as a System Boundary
Design human approval around an exact operation, with reviewable payloads, durable decisions, expiry, and execution checks that survive retries.
AI Systems Need Runtime Budgets
How to bound time, tokens, retries, tool calls, and estimated cost across a complete AI execution instead of limiting each call in isolation.
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.
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.
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.
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.