2025 Azure Essentials: Top 10 AI Services to Transform Business

Sachin Tah
Jun 9
6 min read

Microsoft has consistently demonstrated leadership in providing high-quality AI solutions and services. From its initial strategic partnership with OpenAI, the company has gained a significant advantage in the rapidly evolving AI landscape.

Azure current holding a 22% market share and growing, it ranks as the second largest cloud provider, following AWS.

Microsoft is investing significantly in Agentic AI, with this technology influencing nearly every service release. The integration of Agentic AI is enhancing all core services remarkably.

For technology companies aiming to develop superior technology solutions and gain a competitive advantage, here are the top 10 services that I believe will be transformative for an enterprise in 2025.

#01. GITHub CoPilot - AI Agent

GitHub Copilot has launched Coding Agents, which can be regarded as your peer programmers or an autonomous code collaborators. This concept is introduced as a part of the agentic revolution within the existing GitHub Copilot services.

Users are still required to act as the human in the loop to approve pull/push requests and validate the code generated by these agents. The following functions performed by Copilot are impressive when utilized appropriately.

Bug Fixes/Feature Addition (Low & Medium Complex) - Agents can be used for adding new features, solving bugs, code refactoring, enhancing documentation etc.
Issues/Backlogs - You can directly assign tasks to your coding agent, which will execute the work in the background and present it for your review. While I am confident in the overall efficiency of the agent, I am certain it will soon surpass the performance of a programmer.
Code Reviews: Now this is one of the best feature which I would love to use, to begin with. Enabling agents to perform code reviews as per provided guidelines would be super helpful, the agent also goes a step further and helps in fixing these issues if we want to.
Code Generation - Create source code from contents like data diagrams.
Test Coverage - Writing test cases using agents are more effective and extensive, you need to write appropriate prompt to ask the agent to create test cases for your code.

Here is the link for the pricing details for adding these AI Agents into your engineering team.

#02. Multi-Agent Orchestration

As Agentic spaces evolve, enterprise architecture is increasingly utilizing agents to address enterprise challenges. It is becoming increasingly important to decompose single agent capabilities into multiple agents based on functional boundaries or any other criteria. As the number of agents grows, so does the need for collaboration and orchestration among these agents.

Azure AI Foundation Agent Services now includes built-in multi-agent orchestration capabilities, facilitating the construction, collaboration, orchestration, and scaling of multi-agent systems.

Offerings such as Connected Agents, Multi-Agent Workflow, MCP/A2A Support, and the Agent Catalogue are powerful tools that will aid in developing the next generation of Agentic AI systems. Multi-Agent Workflows could be a game changer because of their capabilities like sharing context, persistent state management, error recovery.

#03. Agent’s Identity Management

AI agents increasingly contribute to enterprises, they function either as autonomous entities executing independent tasks or as collaborative partners with human personnel to address complex, multi-step problems.

As the ecosystem grows, these agents will interact with various systems and subsystems, access resources, and perform tasks. Consequently, it is essential to monitor their activities, log their actions, and maintain oversight of their operations.

Microsoft has now introduced Entra Ids for these agents, enabling management through Microsoft’s Entra ID Center. Each agent created via CoPilot Studio and Azure AI Foundry will possess its own identity and will be accessible in the Entra ID Center.

For more insignts on what is Microsoft's Entra Id, you can refer to this link.

#04. Site Reliability Engineering - AI Agent

One of the most effective use cases for Agentic AI is in Site Reliability Engineering (SRE). While we could have configured an agent ourselves, Microsoft has already launched agent-based services specifically designed to assist in this area.

With agents having access to logs, metrics, and events, gaining insights into your application through prompts becomes significantly easier.

I would also like to try configuring and using this agent to conduct predictive and prescriptive analysis. Additionally, I can envision aligning your scaling needs with the SRE Agent to create an autonomous entity.

#05. SQL Server 2025

SQL Server 2025 appears to be highly intriguing and well-aligned with current technological trends and AI support.

The latest version of SQL Server 2025 includes features such as support for the JSON data type, RegEx search capabilities, external REST endpoint invocations, and CoPilot integration.

Additionally, it introduces advanced functionalities like built-in vector store support, offering a native vector store as a data type in the new SQL. This feature could be particularly beneficial if you plan to implement vector search functionality within your application.

Other enhancements include Vector Search, AI Model Management, and Direct API Service Calls.

#06. Azure AI Foundry Model Router

Azure AI Foundry offers nearly 10,000 ready-to-use machine learning models.

These models encompass a comprehensive range, including foundational, industry-specific, multimodal, and reasoning models.

The AI Foundry router is an intelligent service that automatically directs requests to the appropriate model within its model pool. It selects the optimal model for a given input prompt based on factors such as query complexity, cost, and performance. This setup can be utilized to provide adaptive intelligence within your application.

#07. App Modernization Services - AI Driven

Application and Technology Modernization has consistently been a challenging and ongoing task that enterprises struggled to address, and it remains an endless endeavor. These modernization initiatives typically do not yield a direct return on investment and are often seen as liabilities that must be managed to address issues such as scalability, compliance, security, and end-of-service concerns. These processes are complex, time-consuming, risky, and costly.

Till now, we have utilized various tools and technologies to assist in these efforts; however, AI has not traditionally been part of this initiative.

GitHub Copilot now offers integrated application modernization capabilities, facilitating a more seamless and engaging collaboration between developers and AI agents in the modernization process.

It analyzes your code and provides actionable recommendations and plans, followed by an automated transformation engine that aids in performing code upgrades. A notable feature is that developers can train Copilot based on recurring patterns to apply remediation across applications.

Microsoft has recently published App Modernization guidance, offering a step-by-step playbook for executing such modernization activities. You can refer to it here.

#08. Power BI - Analytics Delivered Via AI Agent

Power BI now features a standalone Copilot, an AI agent that enables users to gain insights from their data using natural language processing (NLP) and a chat interface.

This development greatly enhances data analysis and decision-making capabilities.

Users can inquire about their reports, datasets, and other elements accessible within their persona. Instead of navigating through individual reports, users can simply request Copilot to retrieve the necessary information across various reports and datasets. For instance, one might ask, "Show monthly case inflows summary with high priorities and SLAs nearing completion."

These powerful capabilities allow business users to directly leverage the potential of generative AI, facilitating valuable data insights and improving decision-making processes.

#09. Azure AI Infrastructure

The NVIDIA Grace Blackwell represents a new generation of AI supercomputers and platforms designed to support cutting-edge AI applications. These GPUs feature 208 billion transistors, enabling them to execute computational operations at remarkable speeds. They also incorporate a unified memory model, allowing memory to be shared seamlessly between the CPU and GPU.

NVIDIA Grace Blackwell systems are now accessible through Azure AI Infrastructure services. This enables advanced AI research, development, training, and deployment of state-of-the-art AI solutions without the need for specialized hardware. With direct connectivity to Azure Foundry and a wide array of Azure services, researchers can efficiently develop, test, and package their AI offerings without upfront investments.

#10. Project Amelie - ML Agent

Amelie is an autonomous agent capable of constructing comprehensive machine learning pipelines from a single prompt.

Project Amelie is still in preview mode, however I could see immense benefit to ML programmers who are trying to create ML models for their projects. Using prompts developers should be able to create ML pipeline, selection of right ML algorithm, trained ML model and ready to use and modifiable Python Code.

You can sign up for private preview here.

In conclusion, I trust that services outlined above will help build solutions designed to not only transform your business but also equip you with cutting-edge technology for the next-generation market.

Embracing these innovations will empower your organization to thrive in an ever-evolving landscape.

Thank you for taking the time to read this blog. I welcome your feedback and any queries you may have, and I would be pleased to collaborate.

For those seeking a quick overview, here is a link to the YouTube video.