Building AI Applications with Semantic Kernel. Questo articolo non è disponibile.Building AI Applications with Semantic Kernel

Ship production ready AI apps in .NET with Semantic Kernel, agents, RAG, and Microsoft.Extensions.AI.

Many teams can prototype an LLM feature, fewer can ship one that is observable, cost aware, and easy to maintain. Model choices, tool calling, retrieval, safety, and deployment details often derail real projects.

This book gives .NET developers a clear path from first chat to production. You will use provider neutral abstractions, add structured tools, integrate retrieval and memory, and deploy with confidence on Azure or containers.

• Set up clean architecture with Semantic Kernel, Microsoft.Extensions.AI, and Azure AI Agent Service
• Bootstrap a kernel with dependency injection, then stream chat responses with minimal latency
• Switch providers at runtime, OpenAI, Azure OpenAI, GitHub Models, Azure AI Inference, and local Ollama
• Define tools and argument schemas, validate inputs, and return structured outputs with typed results
• Adopt reliable planning, native function calling by default, plus Handlebars and Stepwise when they fit
• Build agents with the SK Agent Framework for single agent and multi agent collaboration with tool routing
• Add safety gates, Azure AI Content Safety, prompt shields, and failure handling in request pipelines
• Use the Process Framework for deterministic workflows, events, steps, retries, and idempotency
• Design retrieval with Microsoft.Extensions.VectorData, schemas, metadata, filters, and hybrid search
• Stand up Azure AI Search vector indexes, ingestion, scoring profiles, and grounded RAG with citations
• Tune performance, top k, context sizing, freshness, and token budgets for predictable cost
• Implement OpenTelemetry traces, logs, and metrics, plus usage tracking, alerts, and dashboards
• Run continuous evaluation in CI with quality gates for prompts, tools, and retrieval changes
• Ship with .NET Aspire or containers, manage environment config, secrets, and private networking
• Handle rate limits with backoff, batching, streaming design, and per tenant quotas
• Prepare incident playbooks, degraded modes, kill switches, and safe rollback plans
• Follow end to end case studies, a chat assistant with tools, a multi agent workflow, and a RAG service

This is a code heavy guide with working C#, Bash, YAML, JSON, SQL, and configuration snippets that map directly to real services.

Grab your copy today and ship AI features your team can operate with confidence.