Build image with containerised self-hosted Azure DevOps agent and private Azure Container Registry
Do you want to know how to use containerized self-hosted agents for Azure Pipelines? In this blog post, you will discover the benefits, challenges, and solutions of running your self-hosted agents in a container. You will also see a proof of concept that demonstrates how to build container images using Azure Container Registry and Azure Container Instances. Read on to find out how you can improve your CI/CD workflow with containerization.Automating End-to-End testing with Playwright and Azure Pipelines
The article discusses the importance of end-to-end testing in software development. It introduces Playwright, an open-source automation framework developed by Microsoft, as a superior alternative to Selenium for writing automated browser tests.
Kubernetes & GitHub: Effortless Deployment with GitHub Actions
Did you know that containerised applications are revolutionising how software is developed and deployed? With the rise of technologies like Kubernetes and GitHub Actions, developers now have powerful tools that automate the deployment, scaling, and management of applications, making development faster, more reliable and more efficient. Traditional software development processes can be time-consuming, error-prone, and resource-intensive, with challenges such as inconsistent deployment configurations, manual errors, and complex coordination between development and operations teams. This can result in delays, downtime, and increased costs, impacting the overall efficiency and reliability of software development. Let’s dive in and explore how Kubernetes and GitHub Actions are changing the game for software development!Building AI Agents: Workflow-First vs. Code-First vs. Hybrid
AI Agents are no longer just a developer’s playground. They’re becoming essential for enterprise automation, decision-making, and customer engagement. But how do you build them? Do you go workflow-first with drag-and-drop designers, code-first with SDKs, or adopt a hybrid approach that blends both worlds? In this article, I’ll walk you through the landscape of AI Agent design. We’ll look at workflow-first approaches with drag-and-drop designers, code-first approaches using SDKs, and hybrid models that combine both. The goal is to help you understand the options and choose the right path for your organization. Why AI Agents Need Orchestration Before diving into tools and approaches, let’s talk about why orchestration matters. AI Agents are not just single-purpose bots anymore. They often need to perform multi-step reasoning, interact with multiple systems, and adapt to dynamic workflows. Without orchestration, these agents can become siloed and fail to deliver real business value. Here’s what I’ve observed as the key drivers for orchestration: Complexity of Enterprise Workflows Modern business processes involve multiple applications, data sources, and decision points. AI Agents need a way to coordinate these steps seamlessly. Governance and Compliance Enterprises require control over how AI interacts with sensitive data and systems. Orchestration frameworks provide guardrails for security and compliance. Scalability and Maintainability A single agent might work fine for a proof of concept, but scaling to hundreds of workflows requires structured orchestration to avoid chaos. Integration with Existing Systems AI Agents rarely operate in isolation. They need to plug into ERP systems, CRMs, and custom apps. Orchestration ensures these integrations are reliable and repeatable. In short, orchestration is the backbone that turns AI Agents from clever prototypes into enterprise-ready solutions. Behind the Scenes I’ve always been a pro-code guy. I started my career on open-source coding in Unix and hardly touched the mouse. Then I discovered Visual Studio, and it completely changed my perspective. It showed me the power of a hybrid approach, the best of both worlds. That said, I won’t let my experience bias your ideas of what you’d like to build. This blog is about giving you the full picture so you can make the choice that works best for you. Workflow-First Approach Workflow-first platforms are more than visual designers and not just about drag-and-drop simplicity. They represent a design paradigm where orchestration logic is abstracted into declarative models rather than imperative code. These tools allow you to define agent behaviors, event triggers, and integration points visually, while the underlying engine handles state management, retries, and scaling. For architects, this means faster prototyping and governance baked into the platform. For developers, it offers extensibility through connectors and custom actions without sacrificing enterprise-grade reliability. Copilot Studio Building conversational agents becomes intuitive with a visual designer that maps prompts, actions, and connectors into structured flows. Copilot Studio makes this possible by integrating enterprise data and enabling agents to automate tasks and respond intelligently without deep coding. Building AI Agents using Copilot Studio Design conversation flows with adaptive prompts Integrate Microsoft Graph for contextual responses Add AI-driven actions using Copilot extensions Support multi-turn reasoning for complex queries Enable secure access to enterprise data sources Extend functionality through custom connectors Logic Apps Adaptive workflows and complex integrations are handled through a robust orchestration engine. Logic Apps introduces Agent Loop, allowing agents to reason iteratively, adapt workflows, and interact with multiple systems in real time. Building AI Agents using Logic Apps Implement Agent Loop for iterative reasoning Integrate Azure OpenAI for goal-driven decisions Access 1,400+ connectors for enterprise actions Support human-in-the-loop for critical approvals Enable multi-agent orchestration for complex tasks Provide observability and security for agent workflows Power Automate Multi-step workflows can be orchestrated across business applications using AI Builder models or external AI APIs. Power Automate enables agents to make decisions, process data, and trigger actions dynamically, all within a low-code environment. Building AI Agents using Power Automate Automate repetitive tasks with minimal effort Apply AI Builder for predictions and classification Call Azure OpenAI for natural language processing Integrate with hundreds of enterprise connectors Trigger workflows based on real-time events Combine flows with human approvals for compliance Azure AI Foundry Visual orchestration meets pro-code flexibility through Prompt Flow and Connected Agents, enabling multi-step reasoning flows while allowing developers to extend capabilities through SDKs. Azure AI Foundry is ideal for scenarios requiring both agility and deep customization. Building AI Agents using Azure AI Foundry Design reasoning flows visually with Prompt Flow Orchestrate multi-agent systems using Connected Agents Integrate with VS Code for advanced development Apply governance and deployment pipelines for production Use Azure OpenAI models for adaptive decision-making Monitor workflows with built-in observability tools Microsoft Agent Framework (Preview) I’ve been exploring Microsoft Agent Framework (MAF), an open-source foundation for building AI agents that can run anywhere. It integrates with Azure AI Foundry and Azure services, enabling multi-agent workflows, advanced memory services, and visual orchestration. With public preview live and GA coming soon, MAF is shaping how we deliver scalable, flexible agentic solutions. Enterprise-scale orchestration is achieved through graph-based workflows, human-in-the-loop approvals, and observability features. The Microsoft Agent Framework lays the foundation for multi-agent systems that are durable and compliant. Building AI Agents using Microsoft Agent Framework Coordinate multiple specialized agents in a graph Implement durable workflows with pause and resume Support human-in-the-loop for controlled autonomy Integrate with Azure AI Foundry for hosting and governance Enable observability through OpenTelemetry integration Provide SDK flexibility for custom orchestration patterns Visual-first platforms make building AI Agents feel less like coding marathons and more like creative design sessions. They’re perfect for those scenarios when you’d rather design than debug and still want the option to dive deeper when complexity calls. Pro-Code Approach Remember I told you how I started as a pro-code developer early in my career and later embraced a hybrid approach? I’ll try to stay neutral here as we explore the pro-code world. Pro-code frameworks offer integration with diverse ecosystems, multi-agent coordination, and fine-grained control over logic. While workflow-first and pro-code approaches both provide these capabilities, the difference lies in how they balance factors such as ease of development, ease of maintenance, time to deliver, monitoring capabilities, and other non-functional requirements. Choosing the right path often depends on which of these trade-offs matter most for your scenario. LangChain When I first explored LangChain, it felt like stepping into a developer’s playground for AI orchestration. I could stitch together prompts, tools, and APIs like building blocks, and I enjoyed the flexibility. It reminded me why pro-code approaches appeal to those who want full control over logic and integration with diverse ecosystems. Building AI Agents using LangChain Define custom chains for multi-step reasoning [it is called Lang“Chain”] Integrate external APIs and tools for dynamic actions Implement memory for context-aware conversations Support multi-agent collaboration through orchestration patterns Extend functionality with custom Python modules Deploy agents across cloud environments for scalability Semantic Kernel I’ve worked with Semantic Kernel when I needed more control over orchestration logic, and what stood out was its flexibility. It provides both .NET and Python SDKs, which makes it easy to combine natural language prompts with traditional programming logic. I found the planners and skills especially useful for breaking down goals into smaller steps, and connectors helped integrate external systems without reinventing the wheel. Building AI Agents using Semantic Kernel Create semantic functions for prompt-driven tasks Use planners for dynamic goal decomposition Integrate plugins for external system access Implement memory for persistent context across sessions Combine AI reasoning with deterministic code logic Enable observability and telemetry for enterprise monitoring Microsoft Agent Framework (Preview) Although I introduced MAF in the earlier section, its SDK-first design makes it relevant here as well for advanced orchestration and the pro-code nature… and so I’ll probably write this again in the Hybrid section. The Agent Framework is designed for developers who need full control over multi-agent orchestration. It provides a pro-code approach for defining agent behaviors, implementing advanced coordination patterns, and integrating enterprise-grade observability. Building AI Agents using Microsoft Agent Framework Define custom orchestration logic using SDK APIs Implement graph-based workflows for multi-agent coordination Extend agent capabilities with custom code modules Apply durable execution patterns with pause and resume Integrate OpenTelemetry for detailed monitoring and debugging Securely host and manage agents through Azure AI Foundry integration Hybrid Approach and decision framework I’ve always been a fan of both worlds, the flexibility of pro-code and the simplicity of workflow drag-and-drop style IDEs and GUIs. A hybrid approach is not about picking one over the other; it’s about balancing them. In practice, this to me means combining the speed and governance of workflow-first platforms with the extensibility and control of pro-code frameworks. Hybrid design shines when you need agility without sacrificing depth. For example, I can start with Copilot Studio to build a conversational agent using its visual designer. But if the scenario demands advanced logic or integration, I can call an Azure Function for custom processing, trigger a Logic Apps workflow for complex orchestration, or even invoke the Microsoft Agent Framework for multi-agent coordination. This flexibility delivers the best of both worlds, low-code for rapid development (remember RAD?) and pro-code for enterprise-grade customization with complex logic or integrations. Why go Hybrid Ø Balance speed and control: Rapid prototyping with workflow-first tools, deep customization with code. Ø Extend functionality: Call APIs, Azure Functions, or SDK-based frameworks from visual workflows. Ø Optimize for non-functional requirements: Address maintainability, monitoring, and scalability without compromising ease of development. Ø Enable interoperability: Combine connectors, plugins, and open standards for diverse ecosystems. Ø Support multi-agent orchestration: Integrate workflow-driven agents with pro-code agents for complex scenarios. The hybrid approach for building AI Agents is not just a technical choice but a design philosophy. When I need rapid prototyping or business automation, workflow-first is my choice. For multi-agent orchestration and deep customization, I go with code-first. Hybrid makes sense for regulated industries and large-scale deployments where flexibility and compliance are critical. The choice isn’t binary, it’s strategic. I’ve worked with both workflow-first tools like Copilot Studio, Power Automate, and Logic Apps, and pro-code frameworks such as LangChain, Semantic Kernel, and the Microsoft Agent Framework. Each approach has its strengths, and the decision often comes down to what matters most for your scenario. If rapid prototyping and business automation are priorities, workflow-first platforms make sense. When multi-agent orchestration, deep customization, and integration with diverse ecosystems are critical, pro-code frameworks give you the flexibility and control you need. Hybrid approaches bring both worlds together for regulated industries and large-scale deployments where governance, observability, and interoperability cannot be compromised. Understanding these trade-offs will help you create AI Agents that work so well, you’ll wonder if they’re secretly applying for your job! About the author Pradyumna (Prad) Harish is a Technology leader in the WW GSI Partner Organization at Microsoft. He has 26 years of experience in Product Engineering, Partner Development, Presales, and Delivery. Responsible for revenue growth through Cloud, AI, Cognitive Services, ML, Data & Analytics, Integration, DevOps, Open-Source Software, Enterprise Architecture, IoT, Digital strategies and other innovative areas for business generation and transformation; achieving revenue targets via extensive experience in managing global functions, global accounts, products, and solution architects across over 26 countries.Azure AI Foundry, GitHub Copilot, Fabric and more to Analyze usage stats from Utility Invoices
Overview With the introduction of Azure AI Foundry, integrating various AI services to streamline AI solution development and deployment of Agentic AI Workflow solutions like multi-modal, multi-model, dynamic & interactive Agents etc. has become more efficient. The platform offers a range of AI services, including Document Intelligence for extracting data from documents, natural language processing and robust machine learning capabilities, and more. Microsoft Fabric further enhances this ecosystem by providing robust data storage, analytics, and data science tools, enabling seamless data management and analysis. Additionally, Copilot and GitHub Copilot assist developers by offering AI-powered code suggestions and automating repetitive coding tasks, significantly boosting productivity and efficiency. Objectives In this use case, we will use monthly electricity bills from the utilities' website for a year and analyze them using Azure AI services within Azure AI Foundry. The electricity bills is simply an easy start but we could apply it to any other format really. Like say, W-2, I-9, 1099, ISO, EHR etc. By leveraging the Foundry's workflow capabilities, we will streamline the development stages step by step. Initially, we will use Document Intelligence to extract key data such as usage in kilowatts (KW), billed consumption, and other necessary information from each PDF file. This data will then be stored in Microsoft Fabric, where we will utilize its analytics and data science capabilities to process and analyze the information. We will also include a bit of processing steps to include Azure Functions to utilize GitHub Copilot in VS Code. Finally, we will create a Power BI dashboard in Fabric to visually display the analysis, providing insights into electricity usage trends and billing patterns over the year. Utility Invoice sample Building the solution Depicted in the picture are the key Azure and Copilot Services we will use to build the solution. Set up Azure AI Foundry Create a new project in Azure AI Foundry. Add Document Intelligence to your project. You can do this directly within the Foundry portal. Extract documents through Doc Intel Download the PDF files of the power bills and upload them to Azure Blob storage. I used Document Intelligence Studio to create a new project and Train custom models using the files from the Blob storage. Next, in your Azure AI Foundry project, add the Document Intelligence resource by providing the Endpoint URL and Keys. Data Extraction Use Azure Document Intelligence to extract required information from the PDF files. From the resource page in the Doc Intel service in the portal, copy the Endpoint URL and Keys. We will need these to connect the application to the Document Intelligence API. Next, let’s integrate doc intel with the project. In the Azure AI Foundry project, add the Document Intelligence resource by providing the Endpoint URL and Keys. Configure the settings as needed to start using doc intel for extracting data from the PDF documents. We can stay within the Azure AI Foundry portal for most of these steps, but for more advanced configurations, we might need to use the Document Intelligence Studio. GitHub Copilot in VS Code for Azure Functions For processing portions of the output from Doc Intel, what better way to create the Azure Function than in VS Code, especially with the help of GitHub Copilot. Let’s start by installing the Azure Functions extension in VS Code, then create a new function project. GitHub Copilot can assist in writing the code to process the JSON received. Additionally, we can get Copilot to help generate unit tests to ensure the function works correctly. We could use Copilot to explain the code and the tests it generates. Finally, we seamlessly integrate the generated code and unit tests into the Functions app code file, all within VS Code. Notice how we can prompt GitHub Copilot from step 1 of Creating the Workspace to inserting the generated code into the Python file for the Azure Function to testing it and all the way to deploying the Function. Store and Analyze information in Fabric There are many options for storing and analyzing JSON data in Fabric. Lakehouse, Data Warehouse, SQL Database, Power BI Datamart. As our dataset is small, let’s choose either SQL DB or PBI Datamart. PBI Datamart is great for smaller datasets and direct integration with PBI for dashboarding while SQL DB is good for moderate data volumes and supports transactional & analytical workloads. To insert the JSON values derived in the Azure Functions App either called from Logic Apps or directly from the AI Foundry through the API calls into Fabric, let’s explore two approaches. Using REST API and the other Using Functions with Azure SQL DB. Using REST API – Fabric provides APIs that we can call directly from our Function to insert records using HTTP client in the Function’s Python code to send POST requests to the Fabric API endpoints with our JSON data. Using Functions with Azure SQL DB – we can connect it directly from our Function using the SQL client in the Function to execute SQL INSERT statements to add records to the database. While we are at it, we could even get GitHub Copilot to write up the Unit Tests. Here’s a sample: Visualization in Fabric Power BI Let's start with creating visualizations in Fabric using the web version of Power BI for our report, UtilitiesBillAnalysisDashboard. You could use the PBI Desktop version too. Open the PBI Service and navigate to the workspace where you want to create your report. Click on "New" and select "Dataset" to add a new data source. Choose "SQL Server" from the list of data sources and enter "UtilityBillsServer" as the server name and "UtilityBillsDB" as the DB name to establish the connection. Once connected, navigate to the Navigator pane where we can select the table "tblElectricity" and the columns. I’ve shown these in the pictures below. For a clustered column (or bar) chart, let us choose the columns that contain our categorical data (e.g., month, year) and numerical data (e.g., kWh usage, billed amounts). After loading the data into PBI, drag the desired fields into the Values and Axis areas of the clustered column chart visualization. Customize the chart by adjusting the formatting options to enhance readability and insights. We now visualize our data in PBI within Fabric. We may need to do custom sort of the Month column. Let’s do this in the Data view. Select the table and create a new column with the following formula. This will create a custom sort column that we will use as ‘Sum of MonthNumber’ in ascending order. Other visualizations possibilities: Other Possibilities Agents with Custom Copilot Studio Next, you could leverage a custom Copilot to provide personalized energy usage recommendations based on historical data. Start by integrating the Copilot with your existing data pipeline in Azure AI Foundry. The Copilot can analyze electricity consumption patterns stored in your Fabric SQL DB and use ML models to identify optimization opportunities. For instance, it could suggest energy-efficient appliances, optimal usage times, or tips to reduce consumption. These recommendations can be visualized in PBI where users can track progress over time. To implement this, you would need to set up an API endpoint for the Copilot to access the data, train the ML models using Python in VS Code (let GitHub Copilot help you here… you will love it), and deploy the models to Azure using CLI / PowerShell / Bicep / Terraform / ARM or the Azure portal. Finally, connect the Copilot to PBI to visualize the personalized recommendations. Additionally, you could explore using Azure AI Agents for automated anomaly detection and alerts. This agent could monitor electricity bill data for unusual patterns and send notifications when anomalies are detected. Yet another idea would be to implement predictive maintenance for electrical systems, where an AI agent uses predictive analytics to forecast maintenance needs based on the data collected, helping to reduce downtime and improve system reliability. Summary We have built a solution that leveraged the seamless integration of pioneering AI technologies with Microsoft’s end-to-end platform. By leveraging Azure AI Foundry, we have developed a solution that uses Document Intelligence to scan electricity bills, stores the data in Fabric SQL DB, and processes it with Python in Azure Functions in VS Code, assisted by GitHub Copilot. The resulting insights are visualized in Power BI within Fabric. Additionally, we explored potential enhancements using Azure AI Agents and Custom Copilots, showcasing the ease of implementation and the transformative possibilities. Finally, speaking of possibilities – With Gen AI, the only limit is our imagination! Additional resources Explore Azure AI Foundry Start using the Azure AI Foundry SDK Review the Azure AI Foundry documentation and Call Azure Logic Apps as functions using Azure OpenAI Assistants Take the Azure AI Learn courses Learn more about Azure AI Services Document Intelligence: Azure AI Doc Intel GitHub Copilot examples: What can GitHub Copilot do – Examples Explore Microsoft Fabric: Microsoft Fabric Documentation See what you can connect with Azure Logic Apps: Azure Logic Apps Connectors About the Author Pradyumna (Prad) Harish is a Technology leader in the GSI Partner Organization at Microsoft. He has 26 years of experience in Product Engineering, Partner Development, Presales, and Delivery. Responsible for revenue growth through Cloud, AI, Cognitive Services, ML, Data & Analytics, Integration, DevOps, Open Source Software, Enterprise Architecture, IoT, Digital strategies and other innovative areas for business generation and transformation; achieving revenue targets via extensive experience in managing global functions, global accounts, products, and solution architects across over 26 countries.
