What's the future of RDLC ("client-side SSRS", aka "ReportViewer")?
This is the information I could gather so far: Getting an RDLC renderer for .NET 5+ is currently the https://feedback.azure.com/d365community/idea/ec1af842-4d25-ec11-b6e6-000d3a4f0da0. Unfortunately, there are currently no plans to do that (see https://devblogs.microsoft.com/dotnet/announcing-net-5-0-preview-6/). There are some enthusiast ports/recompilations floating around on github and nuget, but they are not official. The https://docs.microsoft.com/en-us/archive/blogs/sqlrsteamblog/ is dead, the last entry is from 2018. There's a third-party company providing an RDLC renderer, but https://docs.microsoft.com/en-us/archive/blogs/sqlrsteamblog/microsoft-acquires-report-rendering-technology-from-forerunner-software. Nothing has been heard since. There is currently no ReportViewer designer for Visual Studio 2022. Getting one is currently the https://developercommunity.visualstudio.com/search?space=8&sort=votes&q=2022. From a business perspective, I can totally understand that Microsoft is not giving this highly-loved feature the resources it needs. After all, they are basically giving away a great reporting engine for free, undermining their own SQL Server and Power BI sales. And they are not even hiding the fact that they'd rather have people purchase Power BI subscriptions, which is perfectly fine. They are a company, not a charity. Unfortunately, adding a dependency to a third-party cloud service is a no-go for many software development scenarios. Thus, I would like to start a discussion on the following points: It seems to me that MS no longer wants people to use their RLDC reporting engine in new projects. Is this observation correct? If you have a large repository of RDLC reports in your project, what are your migration plans? Are there drop-in replacements from third parties? Would Microsoft consider open-sourcing the RLDC engine, so that the community can "keep the product alive" for legacy scenarios and prevent this from being a blocker in .NET 5+ migrations? Best regards HeinziReview GitHub Pull Requests in Visual Studio 2022
Used the GitHub Extension for Visual Studio 2019 to review Pull Requests. Now it looks like that extension is included in VS 2022. However, when going to 'Git/GitHub/View Pull Requests' it redirects me to the browser. Do you know how you can review pull requests in VS 2022 ?Implementing A2A protocol in NET: A Practical Guide
As AI systems mature into multi‑agent ecosystems, the need for agents to communicate reliably and securely has become fundamental. Traditionally, agents built on different frameworks like Semantic Kernel, LangChain, custom orchestrators, or enterprise APIs do not share a common communication model. This creates brittle integrations, duplicate logic, and siloed intelligence. The Agent‑to‑Agent Standard (A2AS) addresses this gap by defining a universal, vendor‑neutral protocol for structured agent interoperability. A2A establishes a common language for agents, built on familiar web primitives: JSON‑RPC 2.0 for messaging and HTTPS for transport. Each agent exposes a machine‑readable Agent Card describing its capabilities, supported input/output modes, and authentication requirements. Interactions are modeled as Tasks, which support synchronous, streaming, and long‑running workflows. Messages exchanged within a task contain Parts; text, structured data, files, or streams, that allow agents to collaborate without exposing internal implementation details. By standardizing discovery, communication, authentication, and task orchestration, A2A enables organizations to build composable AI architectures. Specialized agents can coordinate deep reasoning, planning, data retrieval, or business automation regardless of their underlying frameworks or hosting environments. This modularity, combined with industry adoption and Linux Foundation governance, positions A2A as a foundational protocol for interoperable AI systems. A2AS in .NET — Implementation Guide Prerequisites • .NET 8 SDK • Visual Studio 2022 (17.8+) • A2A and A2A.AspNetCore packages • Curl/Postman (optional, for direct endpoint testing) The open‑source A2A project provides a full‑featured .NET SDK, enabling developers to build and host A2A agents using ASP.NET Core or integrate with other agents as a client. Two A2A and A2A.AspNetCore packages power the experience. The SDK offers: A2AClient - to call remote agents TaskManager - to manage incoming tasks & message routing AgentCard / Message / Task models - strongly typed protocol objects MapA2A() - ASP.NET Core router integration that auto‑generates protocol endpoints This allows you to expose an A2A‑compliant agent with minimal boilerplate. Project Setup Create two separate projects: CurrencyAgentService → ASP.NET Core web project that hosts the agent A2AClient → Console app that discovers the agent card and sends a message Install the packages from the pre-requisites in the above projects. Building a Simple A2A Agent (Currency Agent Example) Below is a minimal Currency Agent implemented in ASP.NET Core. It responds by converting amounts between currencies. Step 1: In CurrencyAgentService project, create the CurrencyAgentImplementation class to implement the A2A agent. The class contains the logic for the following: a) Describing itself (agent “card” metadata). b) Processing the incoming text messages like “100 USD to EUR”. c) Returning a single text response with the conversion. The AttachTo(ITaskManager taskManager) method hooks two delegates on the provided taskManager - a) OnAgentCardQuery → GetAgentCardAsync: returns agent metadata. b) OnMessageReceived → ProcessMessageAsync: handles incoming messages and produces a response. Step 2: In the Program.cs of the Currency Agent Solution, create a TaskManager , and attach the agent to it, and expose the A2A endpoint. Typical flow: GET /agent → A2A host asks OnAgentCardQuery → returns the card POST /agent with a text message → A2A host calls OnMessageReceived → returns the conversion text. All fully A2A‑compliant. Calling an A2A Agent from .NET To interact with any A2A‑compliant agent from .NET, the client follows a predictable sequence: identify where the agent lives, discover its capabilities through the Agent Card, initialize a correctly configured A2AClient, construct a well‑formed message, send it asynchronously, and finally interpret the structured response. This ensures your client is fully aligned with the agent’s advertised contract and remains resilient as capabilities evolve. Below are the steps implemented to call the A2A agent from the A2A client: Identify the agent endpoint: Why: You need a stable base URL to resolve the agent’s metadata and send messages. What: Construct a Uri pointing to the agent service, e.g., https://localhost:7009/agent. Discover agent capabilities via an Agent Card. Why: Agent Cards provide a contract: name, description, final URL to call, and features (like streaming). This de-couples your client from hard-coded assumptions and enables dynamic capability checks. What: Use A2ACardResolver with the endpoint Uri, then call GetAgentCardAsync() to obtain an AgentCard. Initialize the A2AClient with the resolved URL. Why: The client encapsulates transport details and ensures messages are sent to the correct agent endpoint, which may differ from the discovery URL. What: Create A2AClient using new Uri (currencyCard.Url) from the Agent Card for correctness. Construct a well-formed agent request message. Why: Agents typically require structured messages for roles, traceability, and multi-part inputs. A unique message ID supports deduplication and logging. What: Build an AgentMessage: • Role = MessageRole.User clarifies intent. • MessageId = Guid.NewGuid().ToString() ensures uniqueness. • Parts contains content; for simple queries, a single TextPart with the prompt (e.g., “100 USD to EUR”). Package and send the message. Why: MessageSendParams can carry the message plus any optional settings (e.g., streaming flags or context). Using a dedicated params object keeps the API extensible. What: Wrap the AgentMessage in MessageSendParams and call SendMessageAsync(...) on the A2AClient. Outcome: Await the asynchronous response to avoid blocking and to stay scalable. Interpret the agent response. Why: Agents can return multiple Parts (text, data, attachments). Extracting the appropriate part avoids assumptions and keeps your client robust. What: Cast to AgentMessage, then read the first TextPart’s Text for the conversion result in this scenario. Best Practices 1. Keep Agents Focused and Single‑Purpose Design each agent around a clear, narrow capability (e.g., currency conversion, scheduling, document summarization). Single‑responsibility agents are easier to reason about, scale, and test, especially when they become part of larger multi‑agent workflows. 2. Maintain Accurate and Helpful Agent Cards The Agent Card is the first interaction point for any client. Ensure it accurately reflects: Supported input/output formats Streaming capabilities Authentication requirements (if any) Version information A clean and honest card helps clients integrate reliably without guesswork. 3. Prefer Structured Inputs and Outputs Although A2A supports plain text, using structured payloads through DataPart objects significantly improves consistency. JSON inputs and outputs reduce ambiguity, eliminate prompt‑engineering edge cases, and make agent behavior more deterministic especially when interacting with other automated agents. 4. Use Meaningful Task States Treat A2A Tasks as proper state machines. Transition through states intentionally (Submitted → Working → Completed, or Working → InputRequired → Completed). This gives clients clarity on progress, makes long‑running operations manageable, and enables more sophisticated control flows. 5. Provide Helpful Error Messages Make use of A2A and JSON‑RPC error codes such as -32602 (invalid input) or -32603 (internal error), and include additional context in the error payload. Avoid opaque messages, error details should guide the client toward recovery or correction. 6. Keep Agents Stateless Where Possible Stateless agents are easier to scale and less prone to hidden failures. When state is necessary, ensure it is stored externally or passed through messages or task contexts. For local POCs, in‑memory state is acceptable, but design with future statelessness in mind. 7. Validate Input Strictly Do not assume incoming messages are well‑formed. Validate fields, formats, and required parameters before processing. For example, a currency conversion agent should confirm both currencies exist and the value is numeric before attempting a conversion. 8. Design for Streaming Even if Disabled Streaming is optional, but it’s a powerful pattern for agents that perform progressive reasoning or long computations. Structuring your logic so it can later emit partial TextPart updates makes it easy to upgrade from synchronous to streaming workflows. 9. Include Traceability Metadata Embed and log identifiers such as TaskId, MessageId, and timestamps. These become crucial for debugging multi‑agent scenarios, improving observability, and correlating distributed workflows—especially once multiple agents collaborate. 10. Offer Clear Guidance When Input Is Missing Instead of returning a generic failure, consider shifting the task to InputRequired and explaining what the client should provide. This improves usability and makes your agent self‑documenting for new consumers.Open AI’s GPT-5.1-codex-max in Microsoft Foundry: Igniting a New Era for Enterprise Developers
Announcing GPT-5.1-codex-max: The Future of Enterprise Coding Starts Now We’re thrilled to announce the general availability of OpenAI's GPT-5.1-codex-max in Microsoft Foundry Models; a leap forward that redefines what’s possible for enterprise-grade coding agents. This isn’t just another model release; it’s a celebration of innovation, partnership, and the relentless pursuit of developer empowerment. At Microsoft Ignite, we unveiled Microsoft Foundry: a unified platform where businesses can confidently choose the right model for every job, backed by enterprise-grade reliability. Foundry brings together the best from OpenAI, Anthropic, xAI, Black Forest Labs, Cohere, Meta, Mistral, and Microsoft’s own breakthroughs, all under one roof. Our partnership with Anthropic is a testament to our commitment to giving developers access to the most advanced, safe, and high-performing models in the industry. And now, with GPT-5.1-codex-max joining the Foundry family, the possibilities for intelligent applications and agentic workflows have never been greater. GPT 5.1-codex-max is available today in Microsoft Foundry and accessible in Visual Studio Code via the Foundry extension . Meet GPT-5.1-codex-max: Enterprise-Grade Coding Agent for Complex Projects GPT-5.1-codex-max is engineered for those who build the future. Imagine tackling complex, long-running projects without losing context or momentum. GPT-5.1-codex-max delivers efficiency at scale, cross-platform readiness, and proven performance with top scores on SWE-Bench (77.9), the gold standard for AI coding. With GPT-5.1-codex-max, developers can focus on creativity and problem-solving, while the model handles the heavy lifting. GPT-5.1-codex-max isn’t just powerful; it’s practical, designed to solve real challenges for enterprise developers: Multi-Agent Coding Workflows: Automate repetitive tasks across microservices, maintaining shared context for seamless collaboration. Enterprise App Modernization: Effortlessly refactor legacy .NET and Java applications into cloud-native architectures. Secure API Development: Generate and validate secure API endpoints, with `compliance checks built-in for peace of mind. Continuous Integration Support: Integrate GPT-5.1-codex-max into CI/CD pipelines for automated code reviews and test generation, accelerating delivery cycles. These use cases are just the beginning. GPT-5.1-codex-max is your partner in building robust, scalable, and secure solutions. Foundry: Platform Built for Developers Who Build the Future Foundry is more than a model catalog—it’s an enterprise AI platform designed for developers who need choice, reliability, and speed. • Choice Without Compromise: Access the widest range of models, including frontier models from leading model providers. • Enterprise-Grade Infrastructure: Built-in security, observability, and governance for responsible AI at scale. • Integrated Developer Experience: From GitHub to Visual Studio Code, Foundry connects with tools developers love for a frictionless build-to-deploy journey. Start Building Smarter with GPT-5.1-codex-max in Foundry The future is here, and it’s yours to shape. Supercharge your coding workflows with GPT-5.1-codex-max in Microsoft Foundry today. Learn more about Microsoft Foundry: aka.ms/IgniteFoundryModels. Watch Ignite sessions for deep dives and demos: ignite.microsoft.com. Build faster, smarter, and with confidence on the platform redefining enterprise AI.Build your first ML-Model with ML.NET Model Builder
Excited to dive into machine learning in .NET? With the aid of tools like ML.NET Model Builder and Visual Studio, it's a breeze. Here's a preview of the steps you'll take: 1. Download Visual Studio 2022 with .NET desktop development and ML.NET Model Builder. 2. Create a .NET console app named myMLApp. 3. Add a machine learning model named SentimentModel.mbconfig. 4. Choose the Data classification scenario. 5. Select Local (CPU) as the training environment. 6. Prepare and import your data. 7. Train the model. 8. Evaluate its performance. 9. Consume the model using provided code. 10. Run and debug to observe the results. Now you're all set to leverage ML.NET's prowess for predictive models in your .NET apps!How to use Comments as Prompts in GitHub Copilot for Visual Studio
GitHub Copilot is a coding assistant powered by Artificial Intelligence (AI), which can run in various environments and help you be more efficient in your daily coding tasks. In this new short video, Bruno shows you how to use inline comments to generate code with GitHub Copilot.The Evolution of Conversational AI in Microsoft’s Ecosystem
Over the past two decades, conversational AI has shifted from a futuristic curiosity to a core part of how we interact with technology. And while many companies have shaped this field, one of the most influential players has consistently been Microsoft. From the early days of clippy (yes, that Clippy) to the cutting-edge Copilot ecosystem today, Microsoft’s journey mirrors the broader evolution of conversational AI itself. It’s a story of ambition, experimentation, setbacks, breakthroughs, and ultimately, transformation. In this blog, we’ll explore how conversational AI has developed within Microsoft’s ecosystem—how it started, the key milestones along the way, and where it’s all heading next. https://dellenny.com/the-evolution-of-conversational-ai-in-microsofts-ecosystem/
