GPT-5.5 Now in Microsoft Foundry (GA): Build Reliable Agents for Real Enterprise Work

Taswar Bhatti April 25, 2026 No Comments

OpenAI’s GPT-5.5 is now generally available (GA) in Microsoft Foundry, and this is one of those releases that matters less for “chat” and more for getting actual work done—end-to-end.

The big theme: messy, multi-step requests → completed tasks, with stronger reliability in planning, tool use, UI navigation, and recovery when something breaks mid-flow.

If you’re building internal copilots, engineering assistants, or “agentic” workflows that touch code + docs + systems, GPT-5.5 is positioned as a practical step forward in coding/debugging intelligence, long-context reasoning, computer-use accuracy, and token efficiency for longer-running workloads.

Why this release is different

In many enterprise environments, the hardest part isn’t generating an answer—it’s executing a workflow: interpret intent, plan steps, call tools, verify output, and keep going even after a failed command or a UI detour.
Microsoft frames Foundry as the “platform layer” that turns frontier models into governable systems (security, compliance, management), while GPT-5.5 brings improvements specifically tuned for sustained professional workflows.

What’s improved in GPT-5.5 (developer lens)

1) Agentic task execution (plan → act → finish)

GPT-5.5 is designed to handle multi-step execution, not just single-turn responses—planning and following through across tools and systems.
That’s exactly what you want when the task is “fix the bug, update the tests, and summarize the PR impact,” rather than “explain the bug.”

2) Coding + debugging that feels closer to real engineering

OpenAI highlights stronger performance in writing and debugging code, while Microsoft emphasizes agentic coding and more reliable execution for engineering workflows.
In practice, this maps well to: navigating large repos, doing root-cause analysis (RCA), anticipating downstream test updates, and validating changes before you ship.

3) Long-context reasoning that stays coherent

Both announcements point to GPT-5.5 handling large documents, codebases, and extended histories without losing the thread—critical for enterprise work where context is fragmented across tickets, specs, and logs.

4) Computer use (UI navigation) with better accuracy + recovery

A lot of agent workflows eventually hit a UI: portals, dashboards, admin blades, internal tools. GPT-5.5 puts emphasis on improved “computer use” accuracy—clicking the right thing, backtracking, and recovering when the workflow changes.

5) Token efficiency for long-running workflows

Long workflows can be expensive—not only because they’re long, but because retries and drift multiply tokens. GPT-5.5 is positioned as more efficient, often completing tasks with fewer tokens and fewer retries (especially in coding-style flows).

Top use cases (what I’d actually build with it)

A) Software engineering workflows (repo-scale)

Navigate large codebases and keep context across multiple files/modules.
Debug ambiguous failures, do RCA, propose fixes, and anticipate impact.
Identify test gaps and generate validation scripts (unit/integration).

B) Document + knowledge work (high-fidelity extraction)

Pull structured insights from contracts/specs/invoices/research docs.
Synthesize across multiple sources while keeping citations/traceability in your workflow design.

C) Agentic business process automation

Plan and execute multi-step workflows across systems (CRM, ITSM, internal portals).
Generate finished artifacts (docs/sheets/decks) as part of an automated pipeline.

D) Computer-use scenarios (UI-driven automation)

Navigate UI flows more accurately; recover when a click path fails.
Great fit for repeatable tasks that don’t have clean APIs (yet).

E) Spreadsheet reasoning (structured data)

Stronger reasoning on structured tables to support summarization, anomaly detection, and transformations

Minimal “hello world” – Chat Completions (C#)

This example mirrors the OpenAI v1 pattern used by Foundry/Azure OpenAI docs and is a good baseline for API-first apps.
(You’ll plug in your endpoint + deployment name; for enterprise, prefer Entra ID flows.)

using Azure.Identity;
using OpenAI;
using OpenAI.Chat;

// Uses Entra ID (recommended). Docs show token-based auth patterns for Foundry/OpenAI v1 usage.
var credential = new DefaultAzureCredential();
var token = credential.GetToken(
    new Azure.Core.TokenRequestContext(new[] { "https://ai.azure.com/.default" })
);

var client = new OpenAIClient(new OpenAIClientOptions
{
    Endpoint = new Uri("https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/")
});

// If your SDK version expects a bearer token provider/policy, follow the latest Microsoft Learn guidance.
var chat = client.GetChatClient("YOUR-DEPLOYMENT-NAME"); // deployment name maps to GPT-5.5 deployment

var completion = chat.CompleteChat(
    new SystemChatMessage("You are a senior software engineer assistant. Be concise, validate assumptions."),
    new UserChatMessage("Given this stack trace, propose an RCA and a minimal fix plan. Ask for missing logs.")
);

Console.WriteLine(completion.Content[0].Text);

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using Azure.Identity;

using OpenAI;

using OpenAI.Chat;

// Uses Entra ID (recommended). Docs show token-based auth patterns for Foundry/OpenAI v1 usage.

var credential = new DefaultAzureCredential();

var token = credential.GetToken(

new Azure.Core.TokenRequestContext(new[] { "https://ai.azure.com/.default" })

);

var client = new OpenAIClient(new OpenAIClientOptions

{

Endpoint = new Uri("https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/")

});

// If your SDK version expects a bearer token provider/policy, follow the latest Microsoft Learn guidance.

var chat = client.GetChatClient("YOUR-DEPLOYMENT-NAME"); // deployment name maps to GPT-5.5 deployment

var completion = chat.CompleteChat(

new SystemChatMessage("You are a senior software engineer assistant. Be concise, validate assumptions."),

new UserChatMessage("Given this stack trace, propose an RCA and a minimal fix plan. Ask for missing logs.")

);

Console.WriteLine(completion.Content[0].Text);

Minimal “hello world” – Chat Completions (Python)

Python is great for quick evaluation, prompt iteration, and building internal tooling.

import os
from openai import OpenAI
from azure.identity import DefaultAzureCredential, get_bearer_token_provider

token_provider = get_bearer_token_provider(
    DefaultAzureCredential(),
    "https://ai.azure.com/.default"
)

client = OpenAI(
    base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",
    api_key=token_provider
)

resp = client.chat.completions.create(
    model="YOUR-DEPLOYMENT-NAME",  # your GPT-5.5 deployment name
    messages=[
        {"role": "system", "content": "You are a debugging assistant. Provide RCA + fix + tests."},
        {"role": "user", "content": "Here is the error log... (paste). What’s the likely root cause?"}
    ]
)

print(resp.choices[0].message.content)

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import os

from openai import OpenAI

from azure.identity import DefaultAzureCredential, get_bearer_token_provider

token_provider = get_bearer_token_provider(

DefaultAzureCredential(),

"https://ai.azure.com/.default"

)

client = OpenAI(

base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",

api_key=token_provider

)

resp = client.chat.completions.create(

model="YOUR-DEPLOYMENT-NAME", # your GPT-5.5 deployment name

messages=[

{"role": "system", "content": "You are a debugging assistant. Provide RCA + fix + tests."},

{"role": "user", "content": "Here is the error log... (paste). What’s the likely root cause?"}

]

)

print(resp.choices[0].message.content)

Conclusion: GPT-5.5 is built for “execution,” not just “answers”

If your AI roadmap includes agents that:

touch multiple tools,
reason across long contexts,
navigate UIs,
generate real artifacts (code/docs/sheets/decks),
and keep going when workflows break…

…then GPT-5.5 in Microsoft Foundry is a release worth testing early—because it targets the exact failure modes that show up when you move from demo to production.
And with Foundry’s deployment model + enterprise governance story, you can evaluate and productionize without reinventing the operational layer every time a new frontier model lands.

Bonus

Watch this video on YouTube

.NET, AI, Azure

AI Agents, Azure AI Foundry, Azure OpenAI, C#, Developer Tools, GPT-5.5, Microsoft Foundry, python

gpt-image-2-microsoft-foundry-for-developers

GPT‑Image‑2 Comes to Microsoft Foundry: What Developers Need to Know

Taswar Bhatti April 22, 2026 No Comments

Generative AI for images has gone from “cool demo” to something teams actually run in production. Microsoft has now made OpenAI’s GPT‑image‑2 generally available in Microsoft Foundry, and it’s a meaningful upgrade for developers building image-heavy workflows at scale.

This isn’t just “another image model.” GPT‑image‑2 focuses on instruction accuracy, higher resolution output, localization, and enterprise‑ready scaling—all crucial if you’re building real products instead of playing with prompts.

Let’s break down what’s new, why it matters, and how this changes image generation inside Microsoft Foundry.

Key Improvements in GPT‑Image‑2

1. Stronger Real‑World Context

GPT‑image‑2 is trained with knowledge up to December 2025, giving it better awareness of:

Current products
Modern design patterns
Recent cultural references

More importantly, it uses enhanced “thinking” capabilities to:

Refine outputs
Self-check generated content
Create multiple image variations from a single request

That makes it feel less like a static image generator and more like a creative assistant you can automate.

2. Built‑In Multilingual and Localization Support

One standout improvement is better multilingual understanding, especially for:

Japanese
Korean
Chinese
Hindi
Bengali

This matters when you need:

Text rendered correctly inside images
Culturally accurate visuals
Region-specific variations generated automatically

For global products, this alone removes a huge amount of downstream manual work.

Example of image created with GPT-Image-2 vs MAI-Image-2e

var prompt = “Create a simple poster-style graphic with 3 panels showing the same message rendered in Japanese, Korean, and Hindi. The message should be short and generic like ‘Hello World’. Use clean typography, white text on colored blocks, modern UI style, high legibility, no logos.”;

GPT Image 2	MAI Image 2e
GPT-Image-2	MAI-Image-2e

3. High‑Resolution Image Generation (Up to 4K)

GPT‑image‑2 introduces 4K image support, making it viable for:

Marketing assets
Product mockups
High-quality digital content

Important technical constraints to keep in mind:

Maximum pixel count: ~8.3 million pixels
Minimum pixel count: ~655k pixels
Dimensions must be multiples of 16
Requests exceeding limits are automatically resized

Supported resolutions include:

1024 × 1024
1536 × 1024
1024 × 1536
4K custom-sized images (within limits)

This brings image generation much closer to production-grade quality, not just prototyping.

Intelligent Routing: Less Guesswork for Developers

One of the more subtle—but important—features is the intelligent routing layer.

Instead of forcing developers to manually pick image sizes every time, Foundry can now automatically select the best configuration based on the request.

Routing Mode 1: Legacy Size Selection

If you’ve used previous image APIs, this mode maps your request to familiar tiers (small, standard, large) without you changing anything.

Routing Mode 2: Token‑Based Size Buckets

A more advanced mode where requests are routed using token buckets (16 → 96 tokens), offering more granular scaling while still abstracting complexity away from the app layer.

The net result: cleaner code, fewer hardcoded decisions, and more consistent outputs.

C# Code to generate your own image

using Azure.Identity;
using Azure.Core;
using System.Net.Http.Headers;
using System.Text;
using System.Text.Json;

class Program
{
    static async Task Main(string[] args)
    {

        string endpoint_gpt = GetEnvironmentVariable("AZURE_GPT_ENDPOINT") ??
            "https://xyz.cognitiveservices.azure.com/openai/deployments/gpt-image-2/images/generations?api-version=2024-02-01";
                
        string gptModelName = GetEnvironmentVariable("AZURE_GPT_MODEL") ?? "gpt-image-2";
        string prompt = "Create a simple poster-style graphic with 3 panels showing the same message rendered in Japanese, Korean, and Hindi. The message should be short and generic like 'Hello World'. Use clean typography, white text on colored blocks, modern UI style, high legibility, no logos.";        

        var credential = new DefaultAzureCredential();
        AccessToken token = await credential.GetTokenAsync(
            new TokenRequestContext(new[] { "https://cognitiveservices.azure.com/.default" }));

        using var http = new HttpClient();
        http.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Bearer", token.Token);
        
        var gpt_payload = new
        {
            prompt,
            model = gptModelName,
            size = "1024x1024",
            quality = "medium",
            output_compression = 100,
            output_format = "png",
            n = 1
        };
        
        using var content = new StringContent(
            JsonSerializer.Serialize(gpt_payload),
            Encoding.UTF8,
            "application/json");

        using HttpResponseMessage response = await http.PostAsync(endpoint_gpt, content);
        string responseBody = await response.Content.ReadAsStringAsync();

        if (!response.IsSuccessStatusCode)
        {
            Console.WriteLine($"Image generation failed: {(int)response.StatusCode} {response.ReasonPhrase}");
            Console.WriteLine(responseBody);
            throw new InvalidOperationException("GPT Image 2 image generation request failed.");
        }

        using JsonDocument json = JsonDocument.Parse(responseBody);
        string? b64 = json.RootElement
            .GetProperty("data")[0]
            .GetProperty("b64_json")
            .GetString();

        if (string.IsNullOrWhiteSpace(b64))
        {
            throw new InvalidOperationException("Response did not contain data[0].b64_json.");
        }

        byte[] imageBytes = Convert.FromBase64String(b64);
        string outputPath = Path.Combine(Environment.CurrentDirectory, "generated_image.png");
        await File.WriteAllBytesAsync(outputPath, imageBytes);
        Console.WriteLine($"Image saved to: {outputPath}");
    }

    static string? GetEnvironmentVariable(string name) => Environment.GetEnvironmentVariable(name);
}

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using Azure.Identity;

using Azure.Core;

using System.Net.Http.Headers;

using System.Text;

using System.Text.Json;

class Program

{

static async Task Main(string[] args)

{

string endpoint_gpt = GetEnvironmentVariable("AZURE_GPT_ENDPOINT") ??

"https://xyz.cognitiveservices.azure.com/openai/deployments/gpt-image-2/images/generations?api-version=2024-02-01";

string gptModelName = GetEnvironmentVariable("AZURE_GPT_MODEL") ?? "gpt-image-2";

string prompt = "Create a simple poster-style graphic with 3 panels showing the same message rendered in Japanese, Korean, and Hindi. The message should be short and generic like 'Hello World'. Use clean typography, white text on colored blocks, modern UI style, high legibility, no logos.";

var credential = new DefaultAzureCredential();

AccessToken token = await credential.GetTokenAsync(

new TokenRequestContext(new[] { "https://cognitiveservices.azure.com/.default" }));

using var http = new HttpClient();

http.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Bearer", token.Token);

var gpt_payload = new

{

prompt,

model = gptModelName,

size = "1024x1024",

quality = "medium",

output_compression = 100,

output_format = "png",

n = 1

};

using var content = new StringContent(

JsonSerializer.Serialize(gpt_payload),

Encoding.UTF8,

"application/json");

using HttpResponseMessage response = await http.PostAsync(endpoint_gpt, content);

string responseBody = await response.Content.ReadAsStringAsync();

if (!response.IsSuccessStatusCode)

{

Console.WriteLine($"Image generation failed: {(int)response.StatusCode} {response.ReasonPhrase}");

Console.WriteLine(responseBody);

throw new InvalidOperationException("GPT Image 2 image generation request failed.");

}

using JsonDocument json = JsonDocument.Parse(responseBody);

string? b64 = json.RootElement

.GetProperty("data")[0]

.GetProperty("b64_json")

.GetString();

if (string.IsNullOrWhiteSpace(b64))

{

throw new InvalidOperationException("Response did not contain data[0].b64_json.");

}

byte[] imageBytes = Convert.FromBase64String(b64);

string outputPath = Path.Combine(Environment.CurrentDirectory, "generated_image.png");

await File.WriteAllBytesAsync(outputPath, imageBytes);

Console.WriteLine($"Image saved to: {outputPath}");

}

static string? GetEnvironmentVariable(string name) => Environment.GetEnvironmentVariable(name);

}

Final Thoughts

GPT‑image‑2 is less about flashy demos and more about operational reality:

Fewer retries
Better instruction adherence
Cleaner localization
Higher‑quality visuals at scale

If you’re building AI‑powered apps, internal tools, or content pipelines on Azure, this model is a strong signal that image generation is now a serious enterprise capability, not an experiment.

AI

AI, GPT, GPT-Image-2

5 Highlights Thursday | 29th February 2024

Taswar Bhatti February 29, 2024 No Comments

Hi everyone,

Your weekly dose of 5 Highlights Thursday is here, a list of information and links that you may find helpful in your Azure journey. Feel free to forward this along to anyone who you think may enjoy such sharing.

1. Microsoft partnership with Mistral AI

Microsoft on Monday announced a new partnership with French start-up Mistral AI as the U.S. tech giant seeks to expand its footprint in the fast-evolving artificial intelligence industry. Read more in the link below:

Mistral AI

2. Research Forum Keynote: Research in the Era of AI

Peter Lee, Corporate Vice President, Microsoft Research and Incubations, discusses how recent developments in AI have transformed the way Microsoft approaches research.

3. Develop Your Copilot Skills (Part 2)

Artificial Intelligence (AI) is fundamentally changing how we relate to and benefit from technology. In this episode of the #AzureEnablementShow, Aaron and Natalie share some of the amazing—and FREE—training resources available to help you get the most out of #MicrosoftCopilot. In this second of three episodes, Natalie will share some of the video and self-paced learning resources available for GitHub Copilot, including Introduction to GitHub CoPilot, GitHub Copilot for Visual Studio, Learning AI with GitHub Copilot, and GitHub Copilot Fundamentals – Understand the AI pair programmer.

4. DevOps Security Capabilities in Defender CSPM

In this episode of Defender for Cloud in the Field, Charles Oxyer joins Yuri Diogenes to talk about DevOps security capabilities in Defender CSPM. Charles explains the importance of DevOps security in Microsoft CNAPP solution, what are the free capabilities available as part of Foundational CSPM, and what are the advanced DevOps security features included in Defender CSPM. Charles demonstrates how to improve the DevOps security posture by remediating recommendations, and how to use code to cloud contextualization with Cloud Security Explorer.

5. Microsoft Azure Virtual Training Day: AI Fundamentals – March 6th 2023

Explore core AI concepts at Azure Virtual Training Day: AI Fundamentals from Microsoft Learn. Join us for this free training event to learn how organizations use AI technology to solve real-world challenges and see how to build intelligent applications using Azure AI services. This training is suitable for anyone interested in AI solutions—including those in technical or business roles.

Azure Virtual Training Day

You can complement this edition of 5 Highlights Thursday with our MEA Developer Channel on YouTube where we have weekly interviews and learning material on Microsoft Azure, and much more.

And, as always, please give me feedback on LinkedIn. Which bullet above is your favorite? What do you want more or less of? Other suggestions? Please let me know.

Last by not least, know someone who might be interested in this newsletter? Share it with them.

Subscribe on LinkedIn

Have a wonderful Thursday 🙂

Taswar

Azure, Newsletter

azure, newsletter

5 Highlights Thursday | 22nd February 2024

Taswar Bhatti February 22, 2024 No Comments

Hi everyone,

Your weekly dose of 5 Highlights Thursday is here, a list of information and links that you may find helpful in your Azure journey. Feel free to forward this along to anyone who you think may enjoy such sharing.

1. Demo of Azure AI & pgvector with Azure Database for PostgreSQL

This video showcases how you can improve the relevance of search results on a recipe website by using Azure OpenAI. Specifically the demo shows how you can use the new azure_ai (Preview) extension along with the open source pgvector extension and the Azure Database for PostgreSQL managed service to deliver semantic search results—as compared to search results you could obtain in the past via pattern matching with the LIKE clause in Postgres, or via Postgres full text search. The demo also shows how azure_ai gives you an integration between Azure Database for PostgreSQL and the Azure AI Language service, so you can do things like sentiment analysis, language detection, and PII redaction. In combination with the Azure Database for PostgreSQL service and pgvector, the new azure_ai extension to Postgres gives you the capability to build entirely new classes of applications—entirely in Postgres.

2. Encryption and Ledger in Azure SQL Database | Data Exposed

In this episode of Data Exposed, learn about the recent Azure SQL security innovations with Anna Hoffman and Pieter Vanhove.

3. Microsoft Fabric February 2024 Update

Read the Microsoft Fabric Feb Update. They have a lot of great features this month including Fabric Git Integration REST APIs, Fabric notebook status bar upgrade, Copilot in Dataflow Gen2, and many more!

Microsoft Fabric February 2024 Update

4. Save money with Arc SQL Server licensing – what you need to know | Data Exposed

If you are used to traditional licensing options and pay for software assurance, you may wonder why you would ever use pay-as-you-go billing. In this episode of Data Exposed with Anna Hoffman and Sasha Nosov, we’ll cover how to use our new PayG model and understand how you can use Extended Security Updates.

5. MVP TechBytes – An Overview of Azure Virtual Desktop with Mahammad Kubaib

Dive into Azure Virtual Desktop with MVP Tech Bytes! 💻 Join Mahammad Kubaib and Taswar Bhatti to explore its benefits, deployment, and configuration. Discover how Azure Virtual Desktop can streamline and secure your virtual desktop experience. Don’t miss out on expanding your software development skills.

You can complement this edition of 5 Highlights Thursday with our MEA Developer Channel on YouTube where we have weekly interviews and learning material on Microsoft Azure, and much more.

And, as always, please give me feedback on LinkedIn. Which bullet above is your favorite? What do you want more or less of? Other suggestions? Please let me know.

Last by not least, know someone who might be interested in this newsletter? Share it with them.

Subscribe on LinkedIn

Have a wonderful Thursday 🙂

Taswar

Azure, Newsletter

AVD, azure, fabric, Ledger, pg_vector, postgres

MVP TechBytes – An Overview of Azure Virtual Desktop with Mahammad Kubaib

Taswar Bhatti February 20, 2024 No Comments

Dive into Azure Virtual Desktop with MVP Tech Bytes! 💻 Join Mahammed Kubaibi and Taswar Bhatti to explore its benefits, deployment, and configuration. Discover how Azure Virtual Desktop can streamline and secure your virtual desktop experience. Don’t miss out on expanding your software development skills.

To learn more:

🔗 YouTube channel: / @vdibuzz

🔗Udemy: https://www.udemy.com/user/mahammad-kubaib/

🔗 Blog: https://vdiclub.wordpress.com/blog-2/

Summary

What is Azure Virtual Desktop:
Azure Virtual Desktop is a cloud service that provides application and desktop virtualization hosted on Microsoft’s cloud. It allows users to access their Windows desktops and applications from any device and location.

Benefits of Azure Virtual Desktop:
Azure Virtual Desktop has many benefits, such as: simplicity, security, cost-effectiveness, scalability, flexibility, and sustainability. It enables users to work from anywhere, use their own devices, reduce hardware and licensing costs, adjust the number of virtual machines according to demand, and lower the environmental impact of e-waste.

Licensing and prerequisites for Azure Virtual Desktop:
Azure Virtual Desktop requires a Microsoft subscription, an identity provider (Azure Active Directory and Active Directory), an operating system license, a network connectivity to Microsoft cloud URLs, and a remote desktop client. Users with Office 365, Windows Enterprise, or Education licenses can use Azure Virtual Desktop for free, and only pay for the Azure infrastructure costs.

Architecture and types of Azure Virtual Desktop:
Azure Virtual Desktop has a PaaS model, where some components are managed by Microsoft (control plane) and some are managed by the customer (virtual machines, applications, Azure files, and Active Directory). The customer can choose between two types of virtual desktops: personal or pooled, and single session or multi session. Personal desktops are dedicated to one user, while pooled desktops are shared by multiple users. Single session desktops allow only one user per virtual machine, while multi session desktops allow more than one user per virtual machine

Azure

AVD, MVP TechBytes

5 Highlights Thursday | 15th February 2024

Taswar Bhatti February 15, 2024 No Comments

Hi everyone,

Your weekly dose of 5 Highlights Thursday is here, a list of information and links that you may find helpful in your Azure journey. Feel free to forward this along to anyone who you think may enjoy such sharing.

1. Satya highlights AI transformation in India

While visiting India, Microsoft CEO Satya Nadella sat down for an interview with CNBC and discussed the AI transformation we’re living through. He said AI is “tangibly changing economic productivity.”

2. Security Update Release Summary February 2024

Our February mini-briefing video includes an overview of the release and then a discussion of a few items from today’s release.

3. Develop Your Copilot Skills (Part 1)

We are entering a new era of AI—one that is fundamentally changing how we relate to and benefit from technology. In this episode of the #AzureEnblementShow, Aaron and Natalie share some of the amazing—and FREE—training resources available to help you get the most out of #MicrosoftCopilot. In this first of three episodes, you’ll get an overview of Copilot, learn about Copilot for Microsoft 365, and get a look at some of the skilling resources for Dynamics 365 and Power Platform. Be sure to stay tuned for episodes two and three, when we’ll dive a little deeper into the resources being offered for GitHub Co-Pilot and demo one of the learning paths that is available to you.

4. Microsoft unveils Face Check for secure identity verification

Check out Face Check, the latest addition to our Entra Verified ID digital identity platform. Face Check, powered by Azure AI, allows businesses to match a user’s selfie to their government ID or employee credentials, providing an extra layer of security for sensitive operations like password resets or help desk access #secruty #microsoftentraid

5. Azure Takeoff – Azure Availability Sets

Join Taswar Bhatti & Hatim Nagarwala to learn how to ensure high availability and reliability for your Azure VMs. Discover availability options like availability sets, zones, and proximity placement groups. See a demo of creating and configuring availability sets in Azure portal. Master best practices for your Azure VMs’ resilience journey.

You can complement this edition of 5 Highlights Thursday with our MEA Developer Channel on YouTube where we have weekly interviews and learning material on Microsoft Azure, and much more.

And, as always, please give me feedback on LinkedIn. Which bullet above is your favorite? What do you want more or less of? Other suggestions? Please let me know.

Last by not least, know someone who might be interested in this newsletter? Share it with them.

Subscribe on LinkedIn

Have a wonderful Thursday 🙂

Taswar

Azure, Newsletter

azure, newsletter

MVP TechBytes – Power Platform Collaborative PCF Development with Betim Beja

Taswar Bhatti February 15, 2024 No Comments

Don’t miss me and Betim Beja on MVP TechBytes discussing Power Platform PCF Development!
Learn about code component pros/cons, framework mockups, standard vs virtual components, and contributing to open-source projects.

Summary

In this episode Betim, a Microsoft MVP, talks about code components for the Power Platform and how to use Storybook to test and showcase them.

The benefits and challenges of code components: Code components allow professional developers to extend the Power Platform and help low-code developers to deliver solutions faster and bettr. However, code components also have some limitations, such as the test harness, the Web API, and the virtual components.

The role of component framework mock and Storybook: Betim has created a component framework mock that mocks the whole API of the component framework and integrates it with Storybook, a tool that helps to build and display components in different scenarios and configurations. This way, developers can test their components more easily and collaborate with other stakeholders.

The examples of code components: Betim shows some examples of code components, such as the colorful option set, the switches, the audio player, and the calendar. He also explains how to set up stories and how to use citations for the components.

The call for action for the viewers: Betim invites the viewers to check out his GitHub repository and blog post where he shares his code components and how to use them. He also encourages the viewers to contribute to the component framework mock and to use it for their own components.910

Azure

PCF, Power Platform

Azure Takeoff – Azure Availability Sets

Taswar Bhatti February 8, 2024 No Comments

Join Me & Hatim Nagarwala to learn how to ensure high availability and reliability for your Azure VMs.
Discover availability options like availability sets, zones, and proximity placement groups.
See a demo of creating and configuring availability sets in Azure portal.
Master best practices for your Azure VMs’ resilience journey.

Learn More:
https://learn.microsoft.com/en-us/azure/virtual-machines/availability-set-overview

Summary

Ensuring High Availability for your Virtual Machines in Azure

When it comes to running virtual machines in the cloud, one of the key concerns is ensuring high availability. In Azure, there are several options available to help you achieve this, including availability sets, availability zones, and proximity placement groups.

Availa2bility Sets

Availability sets are a way to ensure that your virtual machines are distributed across multiple physical hardware units within a data center. This helps to mitigate the impact of hardware failures, power interruptions, or other issues that might affect a single hardware unit.

It’s important to 4note that existing virtual machines cannot be added to an availability set; you need to create new virtual machines to join an availability set.

Availability Zones

Availability zones take the concept of availability sets a step further by distributing your virtual machines across multiple data centers within a region. This helps to protect against larger-scale issues that might affect an entire data center, such as natural disasters or power outages.

Proximity Placement Groups

Proximity placement groups are used when you need to ensure low latency between virtual machines. By placing virtual machines in a proximity placement group, you can ensure that they are physically located close to each other within a data center.

In summary, Azure provides several options to help you ensure high availability for your virtual machines. By using availability sets, availability zones, and proximity placement groups, you can protect against hardware failures, power interruptions, and other issues that might impact the availability of your virtual machines.

Azure

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5 Highlights Thursday | 8th February 2024

Taswar Bhatti February 8, 2024 No Comments

Hi everyone,

Your weekly dose of 5 Highlights Thursday is here, a list of information and links that you may find helpful in your Azure journey. Feel free to forward this along to anyone who you think may enjoy such sharing.

1.Mr. Maedas Cozy AI Kitchen: Designing Trust with Sarah Gold

Sarah Gold, Founder and CEO of Projects by IF, is the guest in the Cozy AI Kitchen this week. Together with John Maeda, they discuss the meaning of ‘trust’ and ‘trustworthy’ in an AI world.

2. Build a recommender Full stack App using OpenAI and Azure SQL: Step-by-Step Guide

Check out this step-by-step guide for creating an intelligent web app with #AzureOpenAI Service. This blog post shows you how to create a recommender full stack app with OpenAI and #AzureSQL.

3. How to Convert Audio to .WAV for Speech Service Using MoviePy

Azure Speech Service requires audio files to adhere to specific standards. Find out how to use MoviePy to easily convert your audio files to make them compatible with #Azure Speech Service.

4. Build a production RAG using Azure AI Studio & Prompt Flow

Learn how to build a production-level RAG app for a customer support agent – and integrate it with your web-based product catalog. Streamline your end-to-end app development from prompt engineering to LLMOps with prompt flow in #Azure #AI Studio.

5. MVP TechBytes – Message Brokers for Microservices Communication with Mukhammadkarim Tukhtaboev

Join me and Mukhammadkarim Tukhtaboev‘s discussion on microservices and message brokers in this MVP TechBytes episode. Learn about improving application performance, choosing communication methods, comparing Kafka and RabbitMQ, and setting up RabbitMQ in a.NET app with a live demo. Enhance your software development skills now. 🚀

You can complement this edition of 5 Highlights Thursday with our MEA Developer Channel on YouTube where we have weekly interviews and learning material on Microsoft Azure, and much more.

And, as always, please give me feedback on LinkedIn. Which bullet above is your favorite? What do you want more or less of? Other suggestions? Please let me know.

Last by not least, know someone who might be interested in this newsletter? Share it with them.

Subscribe on LinkedIn

Have a wonderful Thursday 🙂

Taswar

Azure, Newsletter

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MVP TechBytes – Message Brokers for Microservices Communication with Mukhammadkarim Tukhtaboev

Taswar Bhatti February 6, 2024 No Comments

Here is video I did with Mukhammadkarim (from Uzbekistan) where we discuss on microservices and message brokers on MVP TechBytes episode. Learn about improving application performance, choosing communication methods, comparing Kafka and RabbitMQ, and setting up RabbitMQ in a.NET app with a live demo. Enhance your software development skills now.

🚀 To learn more about Mukhammadkarim:

🔗Twitter Handle: / dotnetgooo

🔗Blog: https://dotnetgo.com

🔗YouTube channel: / dotnetuz

🔗LinkedIn: / mukhammadkarim-tukhtaboev

Summary

Message Brokers in Microservice Architecture

In a recent episode of MVP TechBytes, Taswar and Mokhammadkarim discussed the benefits and challenges of using message brokers in microservice architecture. Mokhammadkarim, a software engineer with 10 years of experience in the field, shared his insights on the topic.

Mokhammadkarim began by explaining the difference between monolith and microservice architecture. In monolith architecture, there is a single deployable application, which can be difficult to scale and maintain. On the other hand, microservice architecture consists of a collection of independent and deployable services that can distribute the load and handle failures better.

One of the benefits of microservice architecture is the ability to distribute applications and pressures to multiple services and servers. This can help to distribute client requests and prevent system crashes. Additionally, if there is an error or exception in one service, it does not affect the other services, unlike in monolith architecture where a crash in one part of the system can bring down the entire system.

Taswar and Mokhammadkarim then discussed the role of message brokers in microservice architecture. A message broker is a third-party application that facilitates asynchronous communication between services. It can collect, store, and deliver messages to the appropriate consumers. It can also help with load balancing, fault tolerance, and decoupling.

There are several types of message brokers available, including Rabbit MQ and Kafka. Rabbit MQ is more reliable and flexible for queuing and delivering messages, while Kafka is more suitable for streaming and distributing large amounts of data. The choice of message broker depends on the specific needs of the application.

Mokhammadkarim also demonstrated how to set up Rabbit MQ in a .net application, including installing Rabbit MQ, the Erlang programming language, and the Rabbit MQ client library for .net. He showed how to create a custom service, a controller, and publisher and consumer methods for sending and receiving messages.

In summary, message brokers can provide many benefits in microservice architecture, including improved scalability, fault tolerance, and decoupling. The choice of message broker depends on the specific needs of the application, and there are several options available, including Rabbit MQ and Kafka.

Azure

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GPT-5.5 Now in Microsoft Foundry (GA): Build Reliable Agents for Real Enterprise Work

Why this release is different

What’s improved in GPT-5.5 (developer lens)

1) Agentic task execution (plan → act → finish)

2) Coding + debugging that feels closer to real engineering

3) Long-context reasoning that stays coherent

4) Computer use (UI navigation) with better accuracy + recovery

5) Token efficiency for long-running workflows

Top use cases (what I’d actually build with it)

A) Software engineering workflows (repo-scale)

B) Document + knowledge work (high-fidelity extraction)

C) Agentic business process automation

D) Computer-use scenarios (UI-driven automation)

E) Spreadsheet reasoning (structured data)

Minimal “hello world” – Chat Completions (C#)

Minimal “hello world” – Chat Completions (Python)

Conclusion: GPT-5.5 is built for “execution,” not just “answers”

Bonus

GPT‑Image‑2 Comes to Microsoft Foundry: What Developers Need to Know

Key Improvements in GPT‑Image‑2

1. Stronger Real‑World Context

2. Built‑In Multilingual and Localization Support

Example of image created with GPT-Image-2 vs MAI-Image-2e

3. High‑Resolution Image Generation (Up to 4K)

Intelligent Routing: Less Guesswork for Developers

Routing Mode 1: Legacy Size Selection

Routing Mode 2: Token‑Based Size Buckets

C# Code to generate your own image

Final Thoughts

MVP TechBytes – An Overview of Azure Virtual Desktop with Mahammad Kubaib

Summary

MVP TechBytes – Power Platform Collaborative PCF Development with Betim Beja

Summary

Azure Takeoff – Azure Availability Sets

Summary

MVP TechBytes – Message Brokers for Microservices Communication with Mukhammadkarim Tukhtaboev

Summary

Opinions are my own and not the views of my employer

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