How Agentic AI can ease Azure operations for IT teams

During recent conversations with IT and technology managers, one question that I kept repeatedly hearing was: "Is there a way to get immediate insights and control over our Azure services without all the manual overhead?"

For most IT teams managing cloud infrastructure, this scenario plays out daily. Despite the power of Azure's services, getting quick visibility and control remains surprisingly difficult. 

Engineers don't want more tools or interfaces. They want something that works the way they think, responds to simple questions, and executes tasks securely without complexity.

This is where the conversation about cloud operations is changing. What if instead of adapting to the interfaces of our tools, our tools adapted to us?

Imagine having a knowledgeable team member who can interact with your Azure environment through a simple conversation. AI agents like Atom can understand what you're asking for in plain language, translate your requests into the appropriate technical actions, and deliver results without requiring you to navigate complex interfaces. 

You can both retrieve information and take action, maintaining security and comprehensive audit trails.

Let's explore how this works in real Azure scenarios.

Use case 1: Real-time visibility into cloud services

One of the most common tasks for IT teams is getting a quick overview of their Azure environment. Traditionally, this means logging into the Azure portal, navigating through multiple screens, and possibly exporting data for analysis.

With an agentic approach, you can simply ask Atom to show all the active Azure services.

Behind the scenes, several things happen:

1. The request is logged for compliance and audit purposes
2. Your permissions are verified
3. The agent connects to Azure's APIs
4. Live data is retrieved and formatted for easy consumption

Within seconds, you get a complete overview showing:

• All active applications and their status
• Their deployment regions (like Australia East)
• Connected workspaces
• Disk configurations
• Other relevant resources

The key difference here is context. Rather than just showing you raw data, the agent understands what "active services" means in the context of Azure and returns meaningful information organized in a way that makes sense to you.

Use case 2: Performance monitoring made simple

When troubleshooting issues, you need specific performance data. Rather than clicking through various monitoring dashboards or writing custom queries, you can just ask for what you need.

For instance: "What's the performance of the services inside the 'Sample app RG' resource group for the last few weeks?"

The agent:

1. Logs your request (maintaining that critical audit trail)
2. Identifies which metrics are most relevant for the services in that resource group
3. Queries Azure Monitor for memory usage, disk performance, and uptime statistics
4. Presents the data in a clear, actionable format

All of this happens without you ever having to log into Azure directly. The information is presented conversationally, and you can ask follow-up questions to drill deeper into specific metrics or compare performance across different periods.

Use case 3: Resource provisioning without the complexity

Provisioning new resources traditionally involves either working through multiple portal screens or writing infrastructure-as-code scripts. Both approaches require technical knowledge and attention to detail to avoid mistakes.

With an agentic approach, the process becomes much more straightforward. Let's say traffic is increasing and you need more capacity.

You can just say: "I need to provision a new VM with 32GB of RAM inside the 'sample app RG' resource group."

The agent recognizes this as a resource-changing operation that has cost implications, so it handles it differently:

1. The request is documented in detail
2. An approval workflow is triggered (since adding resources affects costs)
3. Once approved, the agent automatically:
   
   • Creates a new Virtual Network if needed
   • Sets up a Network Interface Card
   • Deploys the VM with the specified configuration

When you check the Azure portal a few minutes later, everything is there and properly configured. No portal navigation, no scripts to write, and significantly less room for human error.

The beauty of this approach is that it handles the complexity of resource dependencies automatically. The agent knows that a VM needs networking components and takes care of creating those supporting resources without you having to specify each one.

Use case 4: Cost management insights in seconds

Understanding cloud costs is crucial, but often involves navigating through cost management tools or setting up special reports. When you need quick insights, the agent approach shines.

Let's say you want to check the total cost for the 'sample app RG' resource group between April 1st and April 10th.

The agent:

1. Logs the request (maintaining consistent audit practices)
2. Connects to Azure Cost Management APIs
3. Retrieves detailed cost information for the specified period
4. Breaks down the spending by service type

Within moments, you get a complete cost breakdown showing exactly where your money is going—from storage and compute to more granular services that might only cost a few cents.

This ability to quickly access financial data helps teams make better decisions about resource allocation and optimization without waiting for monthly reports or specialized cost analysis.

Conclusion 

When we step back and look at what agentic AI brings to cloud operations, the benefits become crystal clear. Traditional approaches to managing Azure infrastructure lead to constant context switching, technical overhead, and increased opportunities for human error. By contrast, conversational agents like Atom create a more straightforward, more intuitive way to work with complex systems.

• ‍Speed and efficiency: Tasks that once took multiple steps across different interfaces now happen in seconds. There's no need to remember specific portal navigation paths or command syntax—just ask for what you need in plain language.
• ‍Complete audit trails: Every action is automatically logged, including details about who requested it, when it was performed, and what was specifically changed. This creates accountability without additional documentation work.‍
• Reduced context switching: Instead of jumping between dashboards, scripts, and portals, you stay in one conversation flow. This not only saves time but also helps maintain your focus on solving the actual problem rather than fighting with tools.‍
• Fewer mistakes: By removing the need to manually configure complex resources, you eliminate many familiar sources of error. The agent handles dependencies and configuration details correctly every time.‍
• Better governance: Permissions checks and approval workflows are built into the process, ensuring that sensitive operations follow proper protocols without hindering legitimate requests.

As we look ahead, it's clear that the most successful IT teams won't be those with the most dashboards or the most elaborate scripts, but those who can harness AI to create a simpler, more human way to manage increasingly complex cloud environments.

If you're interested in this new style of managing your IT infra, reach out to us and we'll guide you :)