Dec30202530 December 202530 December 20250 Commentby Gregor Suttie

Azure Web Apps and AI — What’s New from Microsoft Ignite 2025

Microsoft Ignite 2025 reaffirmed that AI is now a first-class development model across the Azure platform, not just an optional add-on. Among the many announcements at Ignite, updates to Azure App Service (Web Apps) and the broader Azure AI ecosystem directly affect how developers build intelligent, cloud-native applications — including web apps augmented with AI capabilities, semantic search, and agentic workflows.

This article walks through:

Key App Service / Web Apps announcements
The AI platform enhancements that Web Apps can leverage
Architectural patterns for integrating AI into web workloads
Infrastructure automation with Terraform and secure deployment patterns

What’s New in Azure App Service at Ignite 2025

At Ignite 2025, the Azure App Service team announced a set of updates focused on modernization, developer productivity, and AI integration readiness:

1. Managed Instance on Azure App Service (Public Preview)

A major new capability — Managed Instance on Azure App Service — entered public preview. It is designed to simplify migration of legacy web applications (especially classic ASP.NET and .NET apps with Windows dependencies) to a managed PaaS runtime with minimal code changes.

Key benefits:

Run legacy Windows web apps with Hyper-V nested virtualization.
Use configuration and installation scripts for dependencies rather than rewriting code.
Maintain automatic OS and .NET patching and updates.
Direct RDP access to instances for troubleshooting in complex migration scenarios.

This matters for AI because modernization often precedes adding AI-driven features — a classic app first needs to run reliably on App Service before layering in services like semantic search, chat UI APIs, or automated content enrichment workflows.

2. Enhanced Runtime and Language Support

Alongside the Managed Instance preview, App Service continues to expand support for modern runtimes, frameworks, and developer experiences. While not AI-specific, these enhancements make it easier to host intelligent applications built with:

.NET 8/9 and ASP.NET Core
Node.js 20+
Java 25 and beyond
Containers on App Service (Linux) with improved tooling

These runtimes interoperate cleanly with Azure AI APIs and SDKs. For example, a Node.js web app can call Azure AI Search or an LLM API directly via SDK or REST.

Azure AI Platform Enhancements from Ignite 2025

Ignite 2025 made clear that the foundation for AI-driven web apps is not just the compute layer, but platform services that encapsulate semantic understanding, retrieval-augmented generation (RAG), and agentic workflows.

1. Microsoft Foundry — Unified AI Agent Platform

At Ignite, Azure AI Foundry was rebranded and enhanced as “Microsoft Foundry”, a unified platform for building, deploying, and governing enterprise-grade AI agents across workloads. Foundry now supports:

Multi-agent orchestration
Open standards for models (including Anthropic Claude and OpenAI models)
Seamless integration with enterprise data and APIs

Foundry is not App Service, but it is the AI backbone you are likely to call from web apps for:

Conversational AI interfaces
Workflow automation (e.g., ticket triaging, contextual assistants)
Long-running agentic tasks tied to user sessions or backend triggers

More on Foundry’s updates are in the Microsoft documentation. TECHCOMMUNITY.MICROSOFT.COM+1

2. Semantic Retrieval and AI Search

Azure AI Search (formerly Cognitive Search) continues to evolve with RAG-friendly patterns that integrate vector search, semantic ranking, and LLMs. This makes it much easier to add “chat with your data” experiences into web UIs.

Azure AI Search documentation is here:
🔗 https://learn.microsoft.com/azure/search/what-is-azure-ai-search

Typical patterns for Web Apps include:

Document ingestion pipelines (Azure Blob, OneLake, Cosmos DB)
Indexer + semantic search for natural language queries
LLM integration to summarize and respond conversationally

Patterns for Integrating AI into Azure Web Apps

Below are architectural patterns you might adopt when extending web apps with AI capabilities following Ignite 2025.

Pattern 1 — RAG-Driven Conversational UI

Web App Frontend on App Service (e.g., React, Blazor).
API Layer in Azure Functions or .NET backend to handle requests.
Azure AI Search + vector store for semantic retrieval.
OpenAI or Foundry Models for response generation.

Flow:

User asks a question in web UI.
Backend calls Azure AI Search to retrieve relevant documents.
Retrieved context is sent to a generative model.
Model output is returned to the UI.

var client = new Azure.AI.OpenAI.OpenAIClient(new Uri(endpoint), new DefaultAzureCredential());
var response = await client.GetCompletionsAsync(
    deploymentOrModelName: "gpt-4.1-enterprise",
    new Azure.AI.OpenAI.CompletionsOptions
    {
        Prompts = { "Summarize these docs for a tech user: ..." }
    });

Pattern 2 — Agentic Backend Workflows

If your web app needs to trigger longer-running workflows (e.g., order fulfillment automation, customer support routing), you can:

Expose an HTTP trigger from your Web App or Azure Function.
Hand off processing to a Foundry agent (via API) that orchestrates multi-step logic.
Use Queues (Service Bus, Storage Queues) for reliable message passing.

This pattern decouples UI from backend processing, letting agents execute tasks with traceability and governance — critical for compliance.

Infrastructure as Code — Terraform & Bicep

Automation and repeatability are essential. Below is a sample Terraform snippet to provision an App Service with a Managed Identity (for secure calls to Azure AI).

Notes:

provider "azurerm" {
  features {}
}

resource "azurerm_resource_group" "rg" {
  name     = "rg-webapp-ai"
  location = "WestEurope"
}

resource "azurerm_app_service_plan" "plan" {
  name                = "asp-webai"
  resource_group_name = azurerm_resource_group.rg.name
  sku {
    tier = "PremiumV4"
    size = "P1v4"
  }
}

resource "azurerm_app_service" "webapp" {
  name                = "webapp-ai"
  resource_group_name = azurerm_resource_group.rg.name
  location            = azurerm_resource_group.rg.location
  app_service_plan_id = azurerm_app_service_plan.plan.id

  identity {
    type = "SystemAssigned"
  }

  site_config {
    dotnet_framework_version = "v6.0"
  }

  app_settings = {
    "WEBSITE_RUN_FROM_PACKAGE" = "1"
    "AZURE_OPENAI_ENDPOINT"     = var.openai_endpoint
    "AZURE_OPENAI_MODEL"        = var.openai_model
  }
}

Use managed identities to authenticate to Azure AI services instead of static keys.
You can extend this snippet with Private Endpoints, Key Vault references, and app settings for AI service integration.

Terraform vs Bicep vs ARM:

Terraform excels with multi-cloud teams and state management.
Bicep/ARM provide first-class Azure tooling and tighter integration with Azure RBAC.
Choose based on team skills and governance requirements.

Security, Governance, and Identity

Ignite announcements highlighted enterprise-grade security for AI agents and services, including identity integration and access policies. For Web Apps calling AI APIs:

Use Managed Identity and Azure RBAC instead of connection strings.
Store secrets (if needed) in Azure Key Vault with MSI access.
Secure backend APIs with Azure AD tokens.

Summary

Ignite 2025 reinforced that AI is deeply woven into the future of Azure compute and application platforms:

Azure App Service continues to modernize with managed instances, enhanced runtimes, and improved migration tooling.
The Azure AI ecosystem — especially Microsoft Foundry and Azure AI Search — enables developers to add semantic, conversational, and agentic capabilities to web apps.
Architectural patterns (RAG, agent workflows) and secure automation (Terraform, managed identity) help deliver production-grade intelligent applications.

As you plan your next wave of web apps, consider AI as a core design axis, not an afterthought.