Logic Apps Aviators Newsletter – February 2024

by Contributed | Feb 5, 2024 | Technology

This article is contributed. See the original author and article here.

We are thrilled to announce that this newsletter edition officially marks one full year of Logic Apps’ Ace Aviators! From our initial newsletter and livestream in February of last year, it’s been an incredible journey witnessing this community take flight. Of course, none of this smooth flying would have been possible without YOU! So, to all our aviators, thank you for reading, watching, and participating this past year. Make sure to keep sharing and posting to #LogicAppsAviators so we can continue to navigate the skies of innovation together.

In this issue:

• Ace Aviator of the Month


• Customer Corner


• News from our product group


• News from our community

Ace Aviator of the Month

February’s Ace Aviator: Maheshkumar Tiwari

What is your role and title? What are your responsibilities associated with your position?

Although my title is Solution Architect, my role is of Practice/Competency Lead, and I lead the Microsoft Integration practice (I’m responsible for leading and growing our expertise) within Birlasoft Limited. My role is multifaceted, encompassing strategic leadership, team development, client engagement, and operational excellence.

Below is a breakdown of my responsibilities:

Strategic Leadership:

• Defining the vision and direction for the practice, aligning it with the overall organizational strategy.


• Identifying and pursuing growth opportunities, including new markets, services, and partnerships.


• Staying abreast of industry trends and innovations to ensure our practice remains competitive (I’m not at my best now, but I’m taking steps to improve).

Team Development:

• Building and nurturing a high-performing team of experts in our practice area.


• Providing mentorship and coaching to help team members develop their skills and expertise.


• Fostering a collaborative and innovative work environment.

Client Engagement:

• Building strong relationships with clients and understanding their needs and challenges.


• Developing and delivering high-quality solutions that meet client requirements.


• Managing client expectations and ensuring their satisfaction.

Operational Excellence:

• Establishing and maintaining efficient processes and workflows within the practice.


• Managing budgets and resources effectively.


• Measuring and monitoring key performance indicators (KPIs) and driving continuous improvement.

Overall, my goal is to lead the Microsoft Integration Practice to success by delivering exceptional value to our clients, developing our team, and contributing to the growth of the organization.

Can you provide some insights into your day-to-day activities and what a typical day in your role looks like?

Typical days begin with a coffee-fueled review of emails, calendar, and upcoming meetings, deadlines, and calls. A substantial portion of the day is then dedicated to collaborative meetings with project teams and clients, focusing on progress updates, challenge resolution, and recommendation presentations. Drafting proposals for new RFPs/RFIs or executing ongoing project plans occupies another significant segment of the workday. As the practice lead, I am also prepared to address any ad-hoc requests or situations that may arise within the practice.

The positive response to our proposals, built on strong customer focus and industry best practices, has ignited growth in the Birlasoft Integration Practice. To capitalize on this momentum, I’m busy interviewing and assembling a team of exceptional individuals. It’s an honor to be part of this thriving practice (and I can’t wait to see what we achieve together)!

So, my day involves doing development work, working on POC/RFP/RFI, solution designing, Interviews, handling escalations, mentoring team, resources, and project planning etc.

What motivates and inspires you to be an active member of the Aviators/Microsoft community?

I am a very strong believer in The Value of Giving Back and by nature I like helping people (as much as I can).

What Inspired: When I had started learning BizTalk, I took lot of help from the community written articles and Microsoft documentation. I will be very honest, although Microsoft documentation is very apt but the articles written by community members were more easy to understand, had a different perspective, simple ways to explain etc.

And that’s how I started with an intention of helping people like me by sharing whatever limited knowledge I have in a simplified manner (at least I try to) by various means – answering on forums, writing articles etc. I maintain a blog Techfindings…by Maheshkumar Tiwari through which I share my findings/learnings and it’s been over a decade I am doing it, over LinkedIn/Facebook answering individuals to their questions, also sometimes on groups.

What Motivates: When you receive mail/message/thank you note from someone you don’t know, saying that the content really helped them – to solve the problem, to crack the interview, to clear the understanding etc. — It warms my heart more than any award. It’s the fuel that keeps me creating, knowing I’m truly touching lives.

Looking back, what advice do you wish you would have been told earlier on that you would give to individuals looking to become involved in STEM/technology?

While theoretical knowledge is important, prioritize developing practical skills like coding, data analysis, project management, and problem-solving. Don’t wait for the “perfect” moment or project to begin. Try mini-projects, tinker with code, participate in online challenges. While doing this embrace failures as learning opportunities and steppingstones to improvement.

No one knows everything, and reaching out for help is a sign of strength, not weakness. Seek guidance from seniors, peers, online communities, or any available resources.

Focus on the joy of learning, exploring, and problem-solving, not just achieving a specific degree or job title. Curiosity and a love for understanding how things work will fuel your passion and resilience through challenges.

What are some of the most important lessons you’ve learned throughout your career?

The only thing which is constant is Change – the sooner we accept it and develop/have a mindset to adapt, the better it is.

Survival of the fittest is applicable to every phase of personal/professional life. You really can’t blame others.

Maintaining a healthy balance between work and personal life (unfortunately I am failing in this), practicing self-care, and managing stress are crucial for long-term success.

Building a successful career takes time, dedication, and perseverance. Set realistic goals, celebrate milestones, and don’t get discouraged by setbacks.

Enjoy the process, keep learning, and adapt to the ever-changing field.

Imagine you had a magic wand that could create a feature in Logic Apps. What would this feature be and why?

Without a second thought, following is what I would have created – A wizard which asks me questions about my workflow requirement and once the questionnaire ends, complete workflow should be presented.

Well, that’s from magic wand perspective :smiling_face_with_smiling_eyes:, but above is very much doable.

But, as of now following are the things which we can do at present (few points are from my team – want to share maximum ideas to make Logic app more robust)

1. Logic Apps should have a feature of disabling the actions from designer. This will help developers in unit testing the code efficiently. We can achieve this by commenting out Json in code view or by creating a backup workflow but that’s a tedious task.


2. Versioning missing in Azure standard Logic Apps


3. Breakpoint option should be enabled, so that it will help in debugging.


4. Retry from the failed step should be extended to loops and parallel branches as well


5. Need out of box support for Liquid Map debugging, Intellisense support would be also good to have


6. For now only Json schema is supported in http trigger, if xml support can be added to it.


7. CRON expression support in Logic app recurrence trigger


8. Reference documentation as to which WS plan should one choose based on number of workflows, volume of messages processed etc.(will help to justify the cost to clients)


9. Exact error capture for actions within loop/action within a scope inside a scope etc.


10. Support for partial deployment of workflows in a single logic app (adding only the new workflows and not overwrite all)

Customer Corner:

Datex debuts flexible supply chain software based on the Azure Stack and Azure Integration Services 

Check out this customer success story about Datex leveraging Microsoft Azure Integration Services to transform its infrastructure for a more modern solution. Azure Integration Services played a crucial role in enabling flexible integrations, data visualization through Power BI, and efficient electronic data integration (EDI) processes using Azure Logic Apps. Read more in this article about how AIS helped provide Datex with a low-code environment, faster time-to-market, cost savings, and enhanced capabilities.

News from our product group:

	Announcement – Target-Based Scaling Support in Azure Logic Apps Standard Read this exciting announcement about an update refining the underlying dynamic scaling mechanism, resulting in faster scale-out and scale-in times.
	Logic Apps Standard Target-Based Scaling Performance Benchmark — Burst Workloads    Take a deeper dive into the new target-based scaling for Azure Logic Apps Standard update and how it can help you manage your application’s performance with asynchronous burst loads.
	Logic Apps Mission Critical Series: “We Speak: IBM i: COBOL and RPG Applications”   Read more on how Azure Logic Apps can unlock scenarios where it’s required to integrate with IBM i applications in another Mission Critical Series episode.
	Key Vault policies affecting your Logic App Standard functioning This article shows how Key Vault Policies may affect the functioning of a Logic App Standard, the troubleshooting steps, and how to fix it.
	Session support for Service Bus built-in connector (Logic Apps Standard)   Need some help with using Azure Service Bus Sessions in Logic Apps Standard? Check out this article.
	Concurrency support for Service Bus built-in connector in Logic Apps Standard   Learn more about how you can use concurrency control with Service Bus built-in connector in Logic Apps Standard.

News from our community:

Azure Function | Application settings | User Secrets | Azure Key Vault | Options Pattern

Post by Sri Gunnala 

Struggling to manage your application settings during development and testing, especially when switching between local and cloud environments? Watch Sri’s video to learn how to efficiently manage your app settings. 

BizTalk Server to Azure Integration Services: Send zipped messages (or files)  

Post by Sandro Pereira

Need to know how to send zipped messages (or files)? Read Sandro’s post about a solution for this in BizTalk and Azure Integration Services.

Friday Fact: Trigger Conditions Can Help You Optimize Workflows and Conserve Resources

Post by Luis Rigueira 

Learn more in this post by Luis about improving your Logic App design with the ability to set trigger conditions.

Introduction to Azure Logic Apps 

Post by Stephen W Thomas

If you’re new to Logic Apps, then Stephen’s newest video is perfect for you to get started.

Azure Integration Services – Faster Integration, Better Results to realize your AI Strategy   

Post by Horton Cloud Solutions

Read more from Horton Cloud Solutions about how AIS not only improves developer productivity but is also key in executing an effective AI strategy. 

Upgrade an Azure function from .NET 6 to .NET 8   

Post by Mark Brimble

Need help upgrading an Azure function from .NET 6 to .NET 8? Mark has your back in this post.

Monthly news – February 2024

by Contributed | Feb 4, 2024 | Technology

This article is contributed. See the original author and article here.

Microsoft Defender for Cloud Monthly news February2024 Edition

This is our monthly “What’s new” blog post, summarizing product updates and various new assets we released over the past month. In this edition, we are looking at all the goodness from January 2024.

Legend: Product videos Webcasts (recordings) Docs on Microsoft Blogs on Microsoft GitHub External content Product improvements Announcements

Microsoft Defender for Cloud   We’re announcing the release of Defender for Cloud’s agentless malware detection for Azure virtual machines (VM), AWS EC2 instances and GCP VM instances, as a new feature included in Defender for Servers Plan 2. Agentless malware detection for VMs is now included in our agentless scanning platform. Agentless malware scanning utilizes Microsoft Defender Antivirus anti-malware engine to scan and detect malicious files. Any detected threats, trigger security alerts directly into Defender for Cloud and Defender XDR, where they can be investigated and remediated. The Agentless malware scanner complements the agent-based coverage with a second layer of threat detection with frictionless onboarding and has no effect on your machine’s performance.   Learn more about agentless malware scanning for servers. We’re announcing the general availability (GA) of the integration between Defender for Cloud and Microsoft Defender XDR (formerly Microsoft 365 Defender). The integration brings competitive cloud protection capabilities into the Security Operations Center (SOC) day-to-day. With Microsoft Defender for Cloud and the Defender XDR integration, SOC teams can discover attacks that combine detections from multiple pillars, including Cloud, Endpoint, Identity, Office 365, and more.   Learn more about the alerts and incidents in Microsoft Defender XDR. Container security is an integral part of Microsoft Defender for Cloud, a Cloud Native Application Platform (CNAPP) as it addresses the unique challenges presented by containerized environments, providing a holistic approach to securing applications and infrastructure in the cloud-native landscape. As organizations embrace multicloud, the silos between cloud environments can become barriers for a holistic approach to container security. Defender for Cloud continues to adapt, offering new capabilities that resonate with the fluidity of multicloud architecture. Our latest additions to AWS and GCP seamlessly traverse cloud silos and provide a comprehensive and unified view of container security posture.   In this blog we dive deep into agentless container security for AWS and GCP.   We have added nine new Azure security recommendations aligned with the Microsoft Cloud Security Benchmark. These new recommendations are currently in public preview. Cybersecurity risks pose a significant threat to organizations of all sizes. As a result, security teams must be diligent in their efforts to protect their networks and data from potential breaches. However, with the increasing complexity of the digital environment and the expanding attack surface, security teams are faced with more and more tasks to improve the organization’s posture as well as investigating potential incidents. This can lead to critical security risks being overlooked or delayed, leaving organizations vulnerable to cyber-attacks. It becomes increasingly more important to estimate the risk created by the security issues in the environment’s configuration and to prioritize their mitigation correctly.   Prioritized cyber risks allow security teams to focus their efforts and resources on the most critical threats, ensuring that they are addressed promptly and effectively, which ultimately helps to reduce the organization’s overall risk profile.   In this article we discuss a new feature in Defender CSPM helping customers to rank the security issues in their environment configuration and fix them accordingly. This feature is based on the presented framework and enhances the risk prioritization capabilities of Defender CSPM. While containers have revolutionized modern software development, the complexity of dependencies in containerized environments and the expanded attack surface they present are still significant hurdles for security professionals. The initial step in securing these environments involves identifying vulnerabilities within container images. Yet, the most time-consuming task can often be identifying the right development team to address these vulnerabilities, particularly the mission-critical ones. Microsoft Defender for Cloud addresses this critical need with its container mapping feature. This blog post explores how Defender for Cloud streamlines the process of tracing vulnerabilities in container images back to their origins in CI/CD pipelines, specifically within Azure DevOps and GitHub environments. This functionality is key to facilitating effective developer remediation workflows, thereby enhancing the security posture of cloud-native applications.   This blog post explores how Defender for Cloud streamlines the process of tracing vulnerabilities in container images back to their origins in CI/CD pipeline to facilitate the vulnerability remediation process. Watch new episodes of the Defender for Cloud in the Field show to learn about the Agentless malware detection, and Unified insights from Microsoft Entra Permissions Management Microsoft Defender for Servers plans require Azure Arc deployment on AWS/GCP machines. This interactive workbook provides an overview of machines in your environment showing their Azure Arc Connected Machine agent deployment status. Discover how other organizations successfully use Microsoft Defender for Cloud to protect their cloud workloads. This month we are featuring Petrobras – a multinational oil and gas company – that uses Microsoft security solutions, including Defender for Cloud, to secure their environment. Join our experts in the upcoming webinars to learn what we are doing to secure your workloads running in Azure and other clouds.     Note: If you want to stay current with Defender for Cloud and receive updates in your inbox, please consider subscribing to our monthly newsletter: https://aka.ms/MDCNewsSubscribe

Building AI Agent Applications Series – Understanding AI Agents

by Contributed | Feb 3, 2024 | Technology

This article is contributed. See the original author and article here.

Do you know about AI Agents? How to apply AI Agents in different scenarios? For AI Agents, Microsoft released the open source framework Autogen. But what is its relationship with Semantic Kernel and Prompt flow? I hope this series can answer your questions. Let everyone have a clear understanding of AI Agents, how to combine Autogen, Semantic Kernel, and Prompt flow to build intelligent applications

Around artificial intelligence, humans have made many attempts in different industries and different application scenarios. With the emergence of LLMs, we have transitioned from traditional chatbots with process predefinition plus semantic matching to Copilot applications that interact with LLMs through natural language. In the past year or so, everyone has mainly focused on basic theories based on LLMs. In 2024 we should enter the application scenario of LLMs. We have a lot of papers, application frameworks, and practices from large companies to support the implementation of LLMs applications. So what is the final form of our so-called artificial intelligence applications? What you can think of is GitHub Copilot for programming assistance, Microsoft 365 Copilot for office scenarios, and Microsoft Copilot on Windows or Bing, etc. But think about the application of Copilot, which relies more on individuals to guide or correct through prompt words, and does not achieve fully intelligent applications. In the 1980s, we began to try to do fully intelligent work, and AI Agent is a fully intelligent best practice.

The agent interacts with the scene where it is located, receives instructions or data in the application scene, and decides different responses based on the instructions or data to achieve the final goal. Intelligent agents not only have human thinking capabilities, but can also simulate human behavior. They can be simple systems based on business processes, or they can be as complex as machine learning models. Agents use pre-established rules or models trained through machine learning/deep learning to make decisions, and sometimes require external control or supervision.

Characteristics of the AI agent:

1. 
   

   Planning, divide steps based on tasks, and have a chain of though. With LLMs, it can be said that the planning ability of the agent is greatly enhanced, and the understanding of the task can be more accurate.

   
   


2. 
   

   Memory the ability to remember behavior and part of logic, the ability to store experiences, and the ability to self-reflect.

   
   


3. 
   

   Tool Chain, such as code execution capabilities, search capabilities, and computing capabilities. It can be said that he has strong mobility

   
   


4. 
   

   perceive and obtain information such as pictures, sounds, temperatures, etc. based on the scene, thus providing better conditions for execution.

   
   

Technical support for realizing intelligent agents

There is considerable application practice in the application of LLMs.

There are many frameworks for implementing intelligent agents. The previously mentioned Semantic Kernel or Autogen can implement intelligent agents. The Assitants API has also been added under OpenAI to enhance the model’s capabilities in agents. Now OpenAI’s Assitants API opens up the capabilities of code interpretation, retrieval, and function calling. Assitants API of Azure OpenAI Service is also coming soon, which can be said to provide enough wisdom for the application capabilities of agents.

Many people pay more attention to the application layer framework. People often compare Semantic Kernel and Autogen. After all, both are from Microsoft and have good task or plan orchestration capabilities. However, some people always feel that the two have many similarities.

Semantic Kernel vs Autogen

Semantic Kernel focuses on effectively dividing individual tasks into steps in Copilot applications. This is also the charm of the Semantic Kernel Planner API. Autogen, on the other hand, focuses more on the construction of agents, dividing tasks to complete goals and assigning tasks to different agents. Each agent executes individually or interactively according to the assigned tasks. Behind each agent’s task can be a streaming task arrangement, or an extended method for solving problems, or skills triggered by corresponding prompts, which can be organized in conjunction with Semantic Kernel plugins. When we want to have a stable task output, we can also add prompt flow to evaluate the output.

Use Semantic Kernel to implement AI agents.

Semantic Kernel has added support for agents in the Experimental library, introduced AgentBuilder, and combined with the Assistant API to complete the brain configuration of the agent. The corresponding planning, memory and tools are defined using different plugins.

var yourAgent = await new AgentBuilder()

                        .WithOpenAIChatCompletion(“OpenAI Assitants API”, “OpenAI Key”)

                        .WithInstructions(“Your agent instruction”)

                        //.FromTemplate(EmbeddedResource.Read(“Your agent YAML”))

                        .WithName(“Your Agent Name”)

                        .WithDescription(“Your Agent Desctiption”)

                        .WithPlugin(“Your Agent Plugins”)

                        .BuildAsync();

Notice

1. WithOpenAIChatCompletion requires OpenAI/Azure OpenAI Service models that support Assistants API (soon to be released). Currently supported OpenAI models are GPT-3.5 or GPT-4 models.


2. WithInstructions We need to give clear task instructions and inform the agent how to execute it. This is equivalent to a process. You need to describe it clearly, otherwise the accuracy will be reduced.


3. .FromTemplate can also use Template to describe task instructions


4. .WithName The name is required to make the call more clear.


5. .WithPlugin is based on different skills and tool chains for the agent to complete tasks. This corresponds to the content of Semantic Kernel.

Let’s take a simple scenario and hope to build a .NET console application through an agent, compile and run it, and require it to be completed through an agent. From this scenario, we need two agents – the agent that generates the .NET CLI script and the agent that runs the .NET CLI script. In Semantic Kernel, we use different plugins to define the required planning, memory and tools. The following is the relevant structure diagram.

You can get sample code from Semantic Kernel CookBook 
https://github.com/microsoft/SemanticKernelCookBook/tree/main/workshop/dotNET/workshop3/dotNETAgent

Application scenarios of AI agents

AI Agents are an important scenario for LLMs applications, and building agent applications will be an important technical field in 2024. We currently have three main forms of intelligence, such as single AI agent, multi- AI agents, and hybrid AI agent.

Single AI Agent

Work completed in specific task scenarios, such as the agent workspace under GitHub Copilot Chat, is an example of completing specific programming tasks based on user needs. Based on the capabilities of LLMs, a single agent can perform different actions based on tasks, such as requirements analysis, project reading, code generation, etc. It can also be used in smart homes and autonomous driving.

Multi-AI agents

This is the work of mutual interaction between AI agents. For example, the above-mentioned Semantic Kernel agent implementation is an example. The AI agent generated by the script interacts with your AI agent that executes the script. Multi-agent application scenarios are very helpful in highly collaborative work, such as software industry development, intelligent production, enterprise management, etc.

Hybrid AI Agent

This is human-computer interaction, making decisions in the same environment. For example, smart medical care, smart cities and other professional fields can use hybrid intelligence to complete complex professional work.

At present, the application of intelligent agents is still very preliminary. Many enterprises and individual developers are in the exploratory stage. Taking the first step is very critical. I hope you can try it more. I also hope that everyone can use Azure OpenAI Service to build more agent applications.

Resources

1. Microsoft Semantic Kernel https://github.com/microsoft/semantic-kernel


2. Microsoft Autogen https://github.com/microsoft/autogen


3. Microsoft Semantic Kernel CookBook https://github.com/microsoft/SemanticKernelCookBook


4. Pursuit of “wicked smartness” in VS Code https://code.visualstudio.com/blogs/2023/11/13/vscode-copilot-smarter