标签：#enterprisearchitecture

Quick Answer AI Orchestration for Enterprise .NET Applications: AI orchestration adds a disciplined layer to .NET apps, coordinating agents, caching, state, and compliance to reduce latency, cost, and hallucinations. AI Orchestration for Enterprise .NET Applications – A Production‑Ready Playbook Scaling Pitfalls of Single-Request AI Calls In many .NET shops the first step to “add AI” is to fire a single HttpClient request from a Razor page. That works for a handful of users, but as traffic grows the pattern quickly turns into a latency, cost, and reliability nightmare . The root cause isn’t the LLM – it’s the absence of a disciplined orchestration layer that can coordinate agents, cache prompts, persist state, and enforce compliance. When you look at the stack, the pain points are clear: Unpredictable token usage and cost spikes Inconsistent latency across users and regions Hallucinated results that break downstream business logic Duplicated retry and state‑management code in every microservice Hard‑coded secrets and opaque audit trails Real‑World Example Consider the U.S. retail platform that added a product‑price‑alert feature. The initial prototype wired a Razor page directly to GPT‑4. Within a few days the service hit 10 k concurrent users, token costs blew past the budget, and the model started hallucinating prices. The team eventually built a lightweight orchestration layer that: Cached the last known price in Redis to avoid duplicate LLM calls. Persisted price history in Cosmos DB for audit and compliance. Enforced a maxTokensPerConversation policy to keep costs predictable. Used Azure Service Bus for long‑running workflows and SignalR for real‑time alerts. Result: latency dropped from 1.2 s to < 150 ms per SKU, token usage fell 40 %, and the feature survived a 50× traffic spike during a holiday sale. Trade‑offs Every architectural decision in AI orchestration comes with a cost. Below are the key trade‑offs you’ll face and how to evaluate them: Decision Pros