GitHub Copilot Desktop App Launches a New Era of AI Development: The Future of Coding Is Here
Artificial intelligence is changing the way software is created, and developers are entering a new chapter where coding is no longer just about writing lines of code. It is becoming a smarter, faster, and more collaborative experience. https://dellenny.com/github-copilot-desktop-app-launches-a-new-era-of-ai-development-the-future-of-coding-is-here/Multi-Agent Systems with Semantic Kernel: Beyond Single Copilots
The AI landscape is moving beyond the era of single assistants. Early AI applications focused on creating one powerful copilot that could answer questions, generate content, write code, and automate tasks. But as real-world use cases become more complex, a new architecture is emerging: multi-agent systems. https://dellenny.com/multi-agent-systems-with-semantic-kernel-beyond-single-copilots/25 Copilot Studio Agent Ideas You Can Build This Weekend
Artificial intelligence is no longer something only big technology companies can experiment with. Today, anyone can build smart AI assistants that solve real problems, automate repetitive work, and improve daily productivity. With tools like Microsoft Copilot Studio, creating your own AI agent has become easier than ever. https://dellenny.com/25-copilot-studio-agent-ideas-you-can-build-this-weekend/Automating Technical Proposal Drafts Using Copilot: The Future of Smarter Proposal Writing
Technical proposals are an essential part of modern business. Whether a company is responding to a client request, competing for a major project, or explaining a complex solution, a well-written technical proposal can make the difference between winning and losing an opportunity. However, creating these proposals is often a time-consuming process that requires research, collaboration, technical knowledge, and careful attention to detail. https://dellenny.com/automating-technical-proposal-drafts-using-copilot-the-future-of-smarter-proposal-writing/Build Your First Autonomous Copilot Agent (Step-by-Step) Using Microsoft Copilot Studio
AI agents are changing how businesses automate daily work. In the past, building an intelligent assistant required developers, APIs, complex frameworks, and hundreds of lines of code. https://dellenny.com/build-your-first-autonomous-copilot-agent-step-by-step-using-microsoft-copilot-studio-almost-no-code/Mastering Query Fields in Azure AI Document Intelligence with C#
Introduction Azure AI Document Intelligence simplifies document data extraction, with features like query fields enabling targeted data retrieval. However, using these features with the C# SDK can be tricky. This guide highlights a real-world issue, provides a corrected implementation, and shares best practices for efficient usage. Use case scenario During the cause of Azure AI Document Intelligence software engineering code tasks or review, many developers encountered an error while trying to extract fields like "FullName," "CompanyName," and "JobTitle" using `AnalyzeDocumentAsync`: The error might be similar to Inner Error: The parameter urlSource or base64Source is required. This is a challenge referred to as parameter errors and SDK changes. Most problematic code are looks like below in C#: BinaryData data = BinaryData.FromBytes(Content); var queryFields = new List<string> { "FullName", "CompanyName", "JobTitle" }; var operation = await client.AnalyzeDocumentAsync( WaitUntil.Completed, modelId, data, "1-2", queryFields: queryFields, features: new List<DocumentAnalysisFeature> { DocumentAnalysisFeature.QueryFields } ); One of the reasons this failed was that the developer was using `Azure.AI.DocumentIntelligence v1.0.0`, where `base64Source` and `urlSource` must be handled internally. Because the older examples using `AnalyzeDocumentContent` no longer apply and leading to errors. Practical Solution Using AnalyzeDocumentOptions. Alternative Method using manual JSON Payload. Using AnalyzeDocumentOptions The correct method involves using AnalyzeDocumentOptions, which streamlines the request construction using the below steps: Prepare the document content: BinaryData data = BinaryData.FromBytes(Content); Create AnalyzeDocumentOptions: var analyzeOptions = new AnalyzeDocumentOptions(modelId, data) { Pages = "1-2", Features = { DocumentAnalysisFeature.QueryFields }, QueryFields = { "FullName", "CompanyName", "JobTitle" } }; - `modelId`: Your trained model’s ID. - `Pages`: Specify pages to analyze (e.g., "1-2"). - `Features`: Enable `QueryFields`. - `QueryFields`: Define which fields to extract. Run the analysis: Operation<AnalyzeResult> operation = await client.AnalyzeDocumentAsync( WaitUntil.Completed, analyzeOptions ); AnalyzeResult result = operation.Value; The reason this works: The SDK manages `base64Source` automatically. This approach matches the latest SDK standards. It results in cleaner, more maintainable code. Alternative method using manual JSON payload For advanced use cases where more control over the request is needed, you can manually create the JSON payload. For an example: var queriesPayload = new { queryFields = new[] { new { key = "FullName" }, new { key = "CompanyName" }, new { key = "JobTitle" } } }; string jsonPayload = JsonSerializer.Serialize(queriesPayload); BinaryData requestData = BinaryData.FromString(jsonPayload); var operation = await client.AnalyzeDocumentAsync( WaitUntil.Completed, modelId, requestData, "1-2", features: new List<DocumentAnalysisFeature> { DocumentAnalysisFeature.QueryFields } ); When to use the above: Custom request formats Non-standard data source integration Key points to remember Breaking changes exist between preview versions and v1.0.0 by checking the SDK version. Prefer `AnalyzeDocumentOptions` for simpler, error-free integration by using built-In classes. Ensure your content is wrapped in `BinaryData` or use a direct URL for correct document input: Conclusion Using AnalyzeDocumentOptions provides a cleaner and more reliable way to work with query fields in Azure AI Document Intelligence using C#. By aligning with the latest SDK approach, developers can simplify implementation, reduce common errors, and improve code maintainability. Keeping up with SDK enhancements and recommended practices ensures more accurate and efficient document data extraction. As Azure AI capabilities continue to evolve, adopting modern integration patterns will help you build scalable and future-ready document processing solutions with greater confidence. Reference Official AnalyzeDocumentAsync Documentation. Official Azure SDK documentation. Azure Document Intelligence C# SDK support add-on query field.
Can Copilot help summarize employee feedback and review data?
My boss asked me to look into whether Copilot can help managers prep for performance reviews. Like, instead of reading through months of feedback and notes manually, could Copilot pull together a summary of key themes from peer feedback, goal progress, and past 1:1 notes? If we keep all of these on Sharepoint, can we prompt Copilot to automatically access and summarize them? Do we need to build something custom for this or are there apps in the M365 ecosystem that already have AI built in for this kind of thing?Best Copilot Studio Agents for HR Teams: Transforming the Future of Human Resources
Human Resources teams are constantly looking for better ways to support employees, improve productivity, and reduce repetitive administrative work. From answering employee questions to managing onboarding tasks, HR professionals handle hundreds of daily requests that can consume valuable time. This is where AI-powered solutions like Microsoft Copilot Studio agents are changing the way HR departments operate. https://dellenny.com/best-copilot-studio-agents-for-hr-teams-transforming-the-future-of-human-resources/
