Category Archives: Azure AI

Getting Started with Azure Resiliency Agent: From Identifying Gaps to Planning Improvements

AI, Azure, Azure AI, Copilot August 30, 2026 Leave a comment

Deploying an application in Azure is only the beginning. The more important question is what happens when part of that application becomes unavailable.Can the application continue serving users if an availability zone fails? Is the database protected against the same failure? Are backups sufficient for the recovery time the business expects?

These questions become harder to answer when an application depends on resources spread across multiple resource groups and subscriptions. Each service has its own configuration options, limitations, and recovery behaviour. A healthy resource dashboard alone does not answer whether the application can survive an outage.

Azure Resiliency agent provides a conversational starting point for this work. This article explains where it fits, how to get started, and how to use it in a practical assessment involving Azure Database for PostgreSQL Flexible Server.

Preview note: This article reflects Microsoft documentation checked on 30 August 2026. The Resiliency agent is in preview; access requirements, portal labels, supported capabilities, and pricing can change. The example below is illustrative, not a report of a completed deployment or failover test.

What is Azure Resiliency agent?

Resiliency agent is integrated into Azure Copilot and helps users explore application resilience through natural language conversations. For new workloads, it can review an architecture description and generate ARM or Bicep templates incorporating resilience configurations. For existing workloads, it guides assessment and remediation. Microsoft Learn: Use the Resiliency agent

It helps to distinguish the surrounding experiences:

ExperiencePurpose
Resiliency in AzureThe broader portal experience, formerly Azure Business Continuity Center, bringing together zone resilience, high availability, backup, disaster recovery, and ransomware protection.
Infrastructure Resiliency ManagerThe experience within Resiliency for assessing and improving infrastructure resilience against availability-zone outages.
Resiliency agentThe conversational assistant that helps users work through resilience questions and improvements.
Continue reading →

Deploy Phi 4 mini on Azure Kubernetes Service with the AI Toolchain Operator (KAITO): Step by Step Guide

AI, AI Agent, Azure AI, GenAI August 21, 2026 Leave a comment

Running large language models on Kubernetes usually means dealing with GPU infrastructure, model serving, scheduling, networking, and operational complexity.

Instead of calling a hosted LLM API, we will deploy an open source model directly on Azure Kubernetes Service (AKS), let KAITO provision the required GPU infrastructure, expose the model through an inference service, and send a real prompt to it.

Azure Kubernetes Service provides another approach through the AI Toolchain Operator add on, based on the open source Kubernetes AI Toolchain Operator (KAITO) project.

KAITO allows us to describe an AI workload through a Kubernetes Workspace resource. From there, the operator can coordinate the GPU compute, model deployment, inference runtime, and Kubernetes resources required to serve the model.

In this guide, we will walk through the steps required to deploy Microsoft Phi 4 mini-instruct on AKS using:

  • Azure Kubernetes Service
  • AI Toolchain Operator / KAITO
  • NVIDIA A100 GPU compute
  • Phi 4 mini instruct
  • vLLM
  • An OpenAI compatible inference API

Microsoft’s AKS documentation describes KAITO as a managed add on for deploying and operating open source LLM workloads on Kubernetes, with capabilities including vLLM integration, prompt formatting, streaming responses, and OpenAI compatible APIs.

The target architecture is:

Azure Subscription → AKS → KAITO → Workspace → GPU Node Pool → Phi-4-mini + vLLM → OpenAI-compatible API → Application

Why Use KAITO on AKS?

Without an AI operator, running an LLM on Kubernetes can require manually coordinating several components:

  • GPU node pools
  • GPU scheduling
  • NVIDIA configuration
  • Model containers
  • Model downloads
  • Inference servers
  • Kubernetes Services
  • Scaling
  • Resource scheduling
Continue reading →

Build Trusted AI with Guardrails and Controls in Azure Foundry

Azure AI, Azure AI Foundry, Cloud Security, compliance January 25, 2026 Leave a comment

As AI systems move from proof of concepts to production, organizations must ensure their applications are safe, secure, and compliant without slowing teams down. Microsoft Azure Foundry brings these capabilities together under Guardrails & Controls, giving builders a central place to filter harmful content, govern agent behavior, block sensitive terms, and receive security insights.

In this walkthrough, We’ll learn how to use the Guardrails & Controls workspace in Azure Foundry with a focus on four areas:

  1. Try it out : experiment with safety checks (text, images, prompts, groundedness)
  2. Content filters : create and assign policy to deployments
  3. Blocklists :ban specific words/phrases from inputs and outputs
  4. Security recommendations : get posture guidance via Defender for Cloud

Why Guardrails Matter ?

Production AI faces unpredictable inputs, sensitive data, and regulatory requirements. Without guardrails, systems can hallucinate, leak private information, or produce unsafe content. Azure Foundry’s Guardrails & Controls reduce those risks by combining content moderation, agent behavior governance, blocked terms, and security posture insights in one place.

Navigate to Guardrails & Controls.

From your Foundry project:

Foundry → (Your Project) → Guardrails & controls

Guardrails & Controls Overview

The Guardrails & Controls landing page in Azure Foundry with tabs for Try it out, Content filters, Blocklists, and Security recommendations.

What you’re seeing:
The overview introduces the guardrails surface with quick entry points for Safety & security guardrails (content filters, blocklists, alerts) and Agent controls (behavior and tool use governance). Use this page as your starting point to design and test safety policies.

Continue reading →

Creating Your First AI Agent with Azure AI Agent Service

AI, AI Agent, Azure AI March 31, 2025 Leave a comment

Introduction

Azure AI Agent Service allows you to create, deploy, and manage AI agents that can perform various tasks. This service leverages powerful AI models to enable agents to perform a wide range of tasks, from answering queries to automating complex workflows. With its user-friendly interface and robust infrastructure, Azure AI Agent Service makes it easy for developers to build intelligent agents that can enhance applications and improve productivity.

This guide will walk you through the steps to set up and run your first agent with the help of Azure AI agent service.

Prerequisites:

  • An Azure subscription.
  • You need a GitHub Account.
  • Basic knowledge of PowerShell and Python.

So first step is to setup your workspace in the GitHUb

GitHub Codespaces: A Convenient Cloud-Based Development Environment

GitHub Codespaces offers a virtual machine in the cloud, providing a clean environment with all necessary prerequisites pre-installed. This makes it incredibly easy to set up and run your code, even on a standard laptop without high-end specifications.

Key Features:

  • Cloud-Based Computation: All computations are performed in the cloud, allowing you to work efficiently on a standard laptop.
  • Easy Setup: Setting up Codespaces is straightforward and quick, making it accessible for developers of all levels.
Continue reading →