Linking one App.config in multiple projects

I recently had a .NET solution where I wanted to use the same App.config in 2 different projects.  I wanted to always ensure that changes made in the App.config of the original project would be reflected in other project(s) automatically.  Listed below are the steps I used to facilitate that process.

1.     Right-click your project in Solution Explorer

2.     Select "Add" -> "Existing Item..."

3.     Navigate to the file that you want to add to the solution

4.     [Important] Instead of hitting Enter or clicking the Add button, you want to click the down-arrow icon at the right edge of the Add button, and select "Add As Link".

 

 

Uploading documents to AI Foundry Agents

Q: Can we upload documents like pdf as input and configure the agents to retrieve the required content that is expected

 

A: The short answer is yes, but with some configuration.  Working in the Agent Playground, you have the option to “Add” Knowledge.  This knowledge can be from a variety of different data sources as seen below. As stated below “Currently only a single instance per each type of data source is supported.”  In my scenario, I had a single text file setup when I configured my agent, before being published.

 

Another option would be to utilize the Azure AI Search to index multiple documents from a data store, where documents can be uploaded after the agent is published.  As of today, there are 6 other options available for accessing documents

 

 

AI Agents vs. Agentic AI

AI agents and agentic AI are related but not the same. AI agents are task oriented systems built around LLMs, while agentic AI refers to a broader paradigm where AI systems exhibit autonomy, goal directed behavior, and self improving capabilities.

 

AI Agents vs. Agentic AI

AI Agents: perform tasks but do not necessarily set their own goals.

•             Modular systems built around LLMs or LIMs.

•             Designed for narrow, task specific automation.

•             Operate through tool integration, prompt engineering, and workflow orchestration.

 

Examples:

·                  A customer service chatbot

·                  A research assistant that retrieves and summarizes documents

·                  A coding agent that fixes bugs when prompted

 

Agentic AI: behave like agents (goal driven, adaptive, and self improving)

•             A broader paradigm where AI systems exhibit autonomy, self direction, and persistent goal pursuit.

•             Goes beyond task execution to include:

·                  Planning

·                  Reflection and self correction

·                  Long horizon reasoning

·                  Adaptive behavior

•             Often involves multi step, self initiated workflows.

 

 

Side by Side Comparison

 

Feature	AI Agents	Agentic AI
Scope	Narrow tasks	Broad, multi step goals
Autonomy	Low to moderate	High
Goal Setting	User defined	AI may refine or generate goals
Reasoning Depth	Shallow to moderate	Deep, reflective, iterative
Architecture	Modular workflows	Self directed cognitive loops
Examples	Chatbots, RPA-like tools	Autonomous research systems, self improving agents

 

Why the Distinction Matters

The research argues that the two concepts diverge in design philosophy and capabilities:

•             AI agents are an engineering pattern—a way to wrap LLMs in tools and workflows.

•             Agentic AI is a behavioral paradigm—systems that act with increasing independence.

 

This matters for:

•             Safety (agentic systems require stronger oversight)

•             Applications (agentic AI can handle long term, complex tasks)

•             Regulation (autonomy introduces new risks and responsibilities)

 

 

Examples to Make It Concrete

AI Agent Example

You ask: "Summarize these 10 PDFs."

The agent:

1. Retrieves files
2. Summarizes them
3. Returns results

It does not decide to read more papers or refine the topic unless instructed.

 

Agentic AI Example

You ask: "Research the best battery technology for drones."

An agentic system might:

1. Break the problem into sub goals
2. Search literature
3. Evaluate trade offs
4. Generate experiments
5. Identify missing data
6. Suggest next steps

It acts like a researcher, not just a tool.

 

Deployment Types in AI Foundry

Deploying a model in Azure AI Foundry can be done in 9 different ways.  Depending on the type of deployment chosen, it may impact one of more factors, such as cost, latency, efficiency for processing large datasets, compliance.  Listed below is a description of each deployment type, along with advantages and disadvantages.  For more details, please visit https://learn.microsoft.com/en-us/azure/ai-foundry/foundry-models/concepts/deployment-types

Deployment Type	Description	Advantage	Disadvantage
Global Standard	Shared global infrastructure for general-purpose model inference.	Cost-effective and easy to scale.	Performance may vary under high demand.
Global Provisioned	Dedicated global infrastructure for consistent performance.	Reliable throughput and latency.	Higher cost due to dedicated resources.
Global Batch	Asynchronous global batch processing for large-scale inference jobs.	Efficient for processing large datasets.	Not suitable for real-time applications.
Data Zone Standard	Shared infrastructure within a specific data zone for compliance needs.	Meets data residency requirements affordably.	Limited performance consistency.
Data Zone Provisioned	Dedicated infrastructure in a data zone for high-performance workloads.	Combines compliance with consistent performance.	More expensive than shared options.
Data Zone Batch	Batch processing within a data zone for regulated data workflows.	Ideal for compliant, large-scale processing.	Slower response times; not real-time.
Standard	Default shared deployment for general use across Azure AI Foundry.	Simple setup and broad compatibility.	May lack advanced performance or compliance features.
Regional Provisioned	Dedicated infrastructure in a specific region for localized performance.	Optimized for regional latency and control.	Higher cost and limited to regional availability.
Developer (Fine-tuned)	Lightweight deployment for testing and iterating fine-tuned models.	Fast iteration and low cost for development.	Not suitable for production-scale workloads.

Nov '25 Regional Tech Events

User Groups

• Nov 4: Capital Area .NET Users Group
• Nov 12: Azure Cleveland
• Nov 13: Akron AITP
• Nov 19: Cleveland C# User Group
• Nov 20: GLUG.NET

 

Conferences

• Nov 11-13: .NET Conf 2025

 

Converting .NET Application from Oracle to SQL Server

The SQL Server equivalent of Oracle.ManagedDataAccess.Client is either System.Data.SqlClient or Microsoft.Data.SqlClient

System.Data.SqlClient is the older built-in provider.

Microsoft.Data.SqlClient is the newer, actively maintained version with better support for .NET Core and .NET 5+.

 

Feature	Oracle.ManagedDataAccess.Client	System.Data.SqlClient / Microsoft.Data.SqlClient
Database	Oracle	SQL Server
Namespace	Oracle.ManagedDataAccess.Client	System.Data.SqlClient or Microsoft.Data.SqlClient
Connection class	OracleConnection	SqlConnection
Command class	OracleCommand	SqlCommand
Data reader class	OracleDataReader	SqlDataReader
NuGet package	Oracle.ManagedDataAccess	System.Data.SqlClient (legacy) or Microsoft.Data.SqlClient (modern)

 

How can I remove GitHub bindings from a Visual Studio 2022 Solution

To remove Git from a solution in Visual Studio 2022, effectively unbinding it from source control, follow these steps:

1. Ensure the solution is NOT open in the Visual Studio IDE.
2. Navigate to the root directory of your solution using File Explorer.
3. If you cannot see the .git folder, you need to enable the display of hidden files and folders in File Explorer. In Windows, open File Explorer, go to the "View" tab, and check "Hidden items."
4. Delete the .git folder within your solution's root directory. This folder contains all the Git repository information, including history, branches, and tags for the solution and all projects within it.
5. Visual Studio should now recognize that the Git repository is no longer present and will no longer manage it with Git source control.

Oct '25 Regional Tech Events

User Groups

• Oct 7: Ohio North Database Training
• Oct 8: Azure Cleveland
• Oct 16: Akron AITP
• Oct 16: GLUG.NET
• Oct 22: Cleveland C# User Group

 

Conferences

• Oct 18: SQL Saturday PGH

 

.NET MAUI with Blazor vs. XAML

 

What's better for mobile development with .NET Maui, Blazor or XAML?

Each has its pros and cons.  Listed below is a brief comparison of the 2 options:

Maui with Blazor: Web Tech, Shared Logic

Best for: Web developers or teams already using Blazor for web apps who want to reuse components and logic.

Pros:

- Write UI in Razor syntax (HTML + C#)

- Share components across web and mobile

- Easier onboarding for web devs

- Great for internal tools or hybrid apps

Cons:

- Slight performance overhead compared to native XAML

- Limited access to some native features (though improving)

- Smaller ecosystem for mobile-specific Blazor components

Maui with XAML: Native Feel, Rich Control

Best for: Developers with WPF, UWP, or Xamarin.Forms experience, or those wanting full control over native UI.

Pros:

- Deep integration with MAUI’s native controls

- Rich styling and layout capabilities

- More mature tooling and community support for mobile-specific features

- Better performance for complex or animation-heavy UIs

Cons:

- Steeper learning curve if you're new to XAML

- Less web-like, so not ideal if you're coming from a web dev background

Conclusion

- If you're building consumer-facing apps with polished native UI and animations: go XAML.

- If you're building internal tools, cross-platform dashboards, or want to reuse Blazor components: go Blazor.

- If you're new to mobile dev but strong in web: Blazor will feel more natural.

- If you're already deep in .NET desktop or mobile: XAML will give you more power.

Aug '25 Regional Tech Events

User Groups

• Aug 5: Ohio North Database Training
• Aug 7: Azure Cleveland
• Aug 21: GLUG.NET
• Aug 27: Cleveland C# User Group

 

Conferences

• Aug 1: Cincy Deliver
• Aug 13-15: Kansas City Developer Conference

 

Jul '25 Regional Tech Events

User Groups

• Jul 16: Akron AITP
• Jul 17: GLUG.NET
• Jul 23: Cleveland C# User Group

 

Conferences

• Aug 1: Cincy Deliver
• Aug 13-15: Kansas City Developer Conference

 

Jun '25 Tech Events

Virtual User Groups

• Jun 3: Ohio North Database Training
• Jun 5: Azure Cleveland
• Jun 19: GLUG.NET
• Jun 26: Cleveland C# User Group

 

Conferences

• Aug 1: Cincy Deliver
• Aug 13-15: Kansas City Developer Conference

 

7 Business Advantages of a Custom Copilot

Microsoft Copilot is an AI generative assistant, designed to understand users requests in every day common spoken language.

This assistant can be custom tailored for a specific business to quickly provide answers to customers. 

This eliminates the need for customers to browse a web site or use a search engine to get answers for specific products (i.e. pricing, sizes, etc.) 

Instead, customers can now ask questions like "What colors are available?" and receive direct answers.

 

This provides every business with an edge over the competition.

Here's why investing in a custom Copilot could be one of the smartest moves for your business.

 

1. Enhanced Efficiency & Productivity

A custom Copilot is designed to integrate seamlessly into your website or app.

 

2. Business Specific Expertise

A custom Copilot can be trained with your business-specific knowledge, making it an expert in your particular business domain.

This would allow customers to ask it specific questions and get specific answers, in plain everyday English

 

3. Reach More Customers

A custom copilot can offer Speech Capabilities where questions can be asked verbally and answers provided via audio. 

This would help reach visually or physically impaired customers.

 

4. Reach More International Customers

By using Language Translation, your custom Copilot can communicate with international customers in the language of their choice,

providing more reach for the foreign customer base. More Customers = More Sales.

 

5. Competitive Edge

By quickly providing answers to your customers' questions, a business can quickly gain an advantage over its competition. 

No longer do customers need to use a search engine or browse a web site for answers on pricing & availability. 

Instead, a quick question and answer with copilot will get the job done quickly leading to a better customer experience and potentially increased sales.

 

6. Increased Data Security & Compliance

Custom Copilots can be designed with specific security protocols and compliance measures that align with your business needs.

By controlling how data is processed, stored, and shared, companies can minimize risks associated with third-party AI services while ensuring compliance with regulatory requirements.

In addition, Microsoft's data policy on AI is "your data remains your data only".

 

7. Cost Savings & ROI

While a custom Copilot requires a single upfront investment in development, the long-term benefits far outweigh the costs.

It will quickly provide answers to customers without negatively impacting CSRs.

Businesses can reduce labor costs, improve service efficiency, and capitalize on AI-driven insights to boost profitability over time.

 

 

In a Nutshell…

A custom Copilot isn't just an AI tool—it's a strategic advantage. By tailoring AI to your business needs, you can drive efficiency, enhance customer experiences,

maintain security, and gain a competitive edge in your industry. In a world where technology is reshaping the way we work, a personalized AI solution could be the

key to unlocking the next level of success for your business.

Ready to explore the possibilities? Let's build a future where AI works for you, not just with you.