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.2026 Is different—Are you ready to win?
2026 Is different—Are you ready to win? 2026 isn’t just another year—it’s a turning point. Cloud go-to-market strategies are being rewritten in real time by AI, marketplaces, co-sell, and ecosystem-led growth. The hard truth? If your strategy isn’t fully aligned this year, you’re going to feel it. That’s why Ultimate Partner is kicking off the year with a must-attend free livestream designed to give you clarity and actionable steps—not theory. On January 13 | 11:00–12:30 pm ET, Vince Menzione, CEO of Ultimate Partner will join two industry leaders for an inside look at what’s next: Jay McBain, Chief Analyst at Omdia, will share his predictions for 2026 and beyond. Cyril Belikoff, VP of Commercial Cloud & AI Marketing at Microsoft, will reveal exciting changes at Microsoft and how to align your GTM strategy for success. This is your chance to ask the tough questions during a LIVE Q&A and walk away with insights you can put into action immediately. ______________________________________________________ 📅 January 13 | 11:00–12:30 pm ET 🎥 Livestream: “Winning in 2026 and Beyond” 👉 Register for FREE: HERECopilot Pages & Notebooks, Microsoft Loop: IT Admin Update – December 2025
For background, check out last year's Nov 2024 IT Admin update. Here's this year's progress and summary: Many key governance, lifecycle, and compliance features for Loop workspaces and Copilot Pages & Notebooks are now available. Learn more here Key deliverables remaining: M365 Group enforcement for shared Loop workspaces Departed User workflows for Copilot Pages, Notebooks, and the My workspace in Loop Multi-Geo Create in user's PDL for shared Loop workspaces Read the rest for details What’s Delivered (since Nov 2024) Sensitivity Labels for Loop workspaces Learn more here Guest Sharing for Loop (Entra B2B: Jul 2024 | for orgs with Sensitivity Labels: Mar 2025) Learn more here Retention Labels for Loop pages and components Learn more here Admin Management: Membership, ownership, deletion, restoration, search, filter, in SharePoint Embedded Admin Center and PowerShell for containers Learn more here Promote Members to Owners for Loop workspaces Learn more here M365 Group owned workspaces: managed by M365 Groups for workspaces created within Teams channels Learn more here Also, check out the latest from Ignite 2025 on Unlocking Productivity with Copilot Pages. What’s In Progress / Coming Soon Feature / Scenario Status Target Date Notes Enforce Microsoft 365 group-owned Loop workspaces In development Q1 CY'26 - 422725 IT policy to require Microsoft 365 groups for lifecycle management of shared Loop workspaces Multi-Geo Create In development Q4 CY'25 - 421616 All new Loop workspaces saved in creator’s PDL geo Departed User Workflow In development Q1 CY’26 - 421612 Temporary or permanent reassignment of existing user-owned containers, copy capability for data URL to Open Containers in app In development Q1 CY'26 - 421612 Application Redirect URL that opens in app when clicked if user has permissions User-Accessible Recycle Bin In development H1 CY’26 - 421615 Restore deleted Copilot Pages, Notebooks from Microsoft 365 Copilot app, restore deleted workspaces from Loop app Groups as Members (tenant-owned) In development H1 CY’26 Invite Microsoft 365 groups as members to Notebooks and workspaces Graph APIs for management In development H1 CY'26 For organizations with dev teams and in house management tools Read-only members Paused Due to lower overall feedback volumes, this work is paused Target date disclaimer: dates and features are estimates and may change. For the latest status, see the Microsoft 365 Public Roadmap links. Instead of creating and repeating content directly in the post this year, our IT Admin documentation on learn.microsoft.com and the Microsoft 365 Public Roadmap has been updated based on the above. We recognize that lack of some of these capabilities may still block your rollout. Please drop questions in the comments or reach out to us through your account team. We're excited to be enabling the rollouts of Copilot Pages, Notebooks, and Loop workspaces in your organization.
Powering career and business growth through AI-led, human-enhanced skilling experiences
Every day, it seems like there’s a new AI tool making headlines. In fact, this year alone, thousands of new AI-powered apps and platforms have launched—reshaping how we work, create, and solve problems. Instead of tech that demands more attention, we’re focused on AI that helps you make better decisions and gives you the skills to grow your career and your business. Work Change Report: AI is Coming to Work. January 2025. All this innovation makes one thing clear: evolving your skills at the pace businesses expect is essential—and really challenging. With over 3.6[1] billion people in the global workforce, organizations and individuals everywhere are grappling with the same question: How do we keep pace with AI? It’s not just a technical challenge—it’s a human one. With the steady stream of new courses, articles, and videos, finding exactly what you need—in the right format, with the right depth, and ready to share with your team—can feel overwhelming. We’ve heard from business leaders, developers, and employees alike: you want learning that’s relevant to your roles and projects, easily accessible, and short enough to fit into your busy day. That’s why we’re committed to deliver clear, role-based skilling paths and AI-led, human-enhanced skilling in a unified and accessible way—so teams can adopt AI faster and lead with confidence. Introducing AI Skills Navigator Today, at Microsoft Ignite, we’re releasing the next-generation AI Skills Navigator—an agentic learning space, bringing together AI-powered skilling experiences and credentials that help individuals build career skills and organizations worldwide accelerate their business. This is a smarter, more personalized way to build both technology skills and the uniquely human skills required to set yourself apart in an AI-dominated workplace. A single, unified experience: Build and verify your skills with AI and cloud content and credentials from Microsoft, LinkedIn Learning, and GitHub—all in one spot. Personalized recommendations: Get learning content curated just for you—based on your role, goals, and learning style, whether you prefer videos, guides, or hands-on labs. Innovative learning experiences: Immerse yourself in interactive skilling sessions—videos of human instructors, combined with real-time agentic AI coaching. Watch, engage, and understand concepts more deeply, like you would in a live classroom. Learn the way you like: Prefer to listen? Instantly convert skilling materials into AI-generated podcasts to fit learning effortlessly into your day. Custom, shareable skilling playlists: Use AI to build tailored learning paths that you can easily assign to your team or share with your friends—and track their progress—turning upskilling into a collaborative social experience. & Digital Skills, Vodafone. AI Skills Navigator is now available to everyone around the world as a public preview! For now, all features are in English, but stay tuned—we’re working quickly to add more features and languages, so you can keep growing your skills—wherever you are. Showcasing expertise in action Learning new skills is important. Proving to employers that you have them is just as critical. That’s why we’re expanding Microsoft Credentials—trusted for over 30 years—to help you verify your real-world skills in AI, cloud, and security. Whether you’re looking to stand out in your career or find the right employee to build out your team, our credentials are here to help highlight and verify great talent. Gartner Unveils Top Predictions for IT Organizations and Users in 2026 and Beyond (press release). October 21, 2025. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. Here’s how we’re evolving Microsoft Credentials: New AI credentials for business professionals, leaders, and early-career talent. More technical credentials focused on secure, scalable AI solutions. Flexible, short-form training content and skills validation for busy schedules. Unlocking human potential with strategic partnerships We know that building AI skills is a team effort. That’s why we’re partnering with leaders like LinkedIn, GitHub, and Pearson to bring you even more ways to learn and grow. Together, we’re making sure you have the resources and support you need—no matter your industry or role. LinkedIn and Microsoft are working together to set a new global standard for AI upskilling. With AI Skills Navigator, you find curated LinkedIn Learning courses that blend essential human and AI skills for every business and technical role. Whether you’re in marketing, finance, HR, operations, or IT, discover practical training that helps you stay ahead. This is just the beginning. We’ll continue to bring you more learning that helps you build professional and leadership skills. GitHub and Microsoft are making it even easier for developers to grow and shine in the AI era. By joining forces within AI Skills Navigator, we’re opening the door for over 100 million developers worldwide to build, prove, and keep expanding their AI skills. Our ongoing partnership is all about nurturing a vibrant developer community that is ready to innovate and keep pace with the fast-changing world of AI. Pearson and Microsoft are teaming up to make it easier than ever to earn and showcase your skills. Credly by Pearson enables professionals to validate their knowledge and gain recognition for their expertise through globally recognized digital credentials. With over 120 million credentials issued and rapid growth in areas like AI, Azure, and cybersecurity, this partnership will empower people to develop in-demand skills and advance their careers. This capability is coming soon. When it’s launched, all Microsoft Credentials will be published to Credly, giving learners a seamless way to earn, manage, and share their achievements. As these exciting partnerships continue to grow, we’re grateful for our Training Services Partners and their long-standing expertise in professional skilling—tailored, human-led training that helps people and organizations everywhere achieve real, impactful results. Helping build careers and businesses, one skill at a time AI combined with your ambition creates a future of tremendous opportunity for you. The hardest part is knowing where to start and direct your focus as the world moves so quickly around us. This is where we can help—whether you’re just getting started or you’re already far along the AI learning path. Together, we’re building a world where everyone can grow, evolve, and lead with confidence. The real frontier isn’t about technology—it’s about what people like you can achieve with it. And we’re here to help you get there, one skill at a time. [1] The World Bank: Labor force, total. Data source: ILO, OECD, and World Bank estimates.APAC Fabric Engineering Connection Call
🚀 Happy New Year to all the amazing Microsoft partners I've had the privilege to work with during 2025. I'm excited to announce the first presenter of 2026 for this week's Fabric Engineering Connection call! Join us Thursday, January 8, from 1–2 am UTC (APAC) for an insightful session from Benny Austin. This week’s focus: 🎯 Updates and Enhancements made to the Fabric Accelerator This is your opportunity to learn more, ask questions, and provide feedback. To participate in the call, you must be a member of the Fabric Partner Community Teams channel. To join, complete the participation form at https://aka.ms/JoinFabricPartnerCommunity. We look forward to seeing you at the call!Americas & EMEA Fabric Engineering Connection
🚀 Happy New Year to all the amazing Microsoft partners I've had the privilege to work with during 2025. I'm excited to announce the first presenters of 2026 for this week's Fabric Engineering Connection calls! Join us Wednesday, January 7, from 8–9 am PT (Americas & EMEA) for an insightful session from Yaron Canari: Discover, Manage and Govern Fabric Data with OneLake Catalog. This is your opportunity to learn more, ask questions, and provide feedback. To participate in the call, you must be a member of the Fabric Partner Community Teams channel. To join, complete the participation form at https://aka.ms/JoinFabricPartnerCommunity. We look forward to seeing you at the calls!
𝐀𝐈 𝐈𝐬 𝐍𝐨𝐭 𝐭𝐡𝐞 𝐑𝐢𝐬𝐤. 𝐔𝐧𝐠𝐨𝐯𝐞𝐫𝐧𝐞𝐝 𝐀𝐈 𝐈𝐬
This blog explores why the real danger lies not in adopting AI, but in deploying it without clear governance, ownership, and operational readiness. Learn how modern AI governance enables speed, trust, and resilience—transforming AI from a risk multiplier into a reliable business accelerator.Engineering a Local-First Agentic Podcast Studio: A Deep Dive into Multi-Agent Orchestration
The transition from standalone Large Language Models (LLMs) to Agentic Orchestration marks the next frontier in AI development. We are moving away from simple "prompt-and-response" cycles toward a paradigm where specialized, autonomous units—AI Agents—collaborate to solve complex, multi-step problems. As a Technology Evangelist, my focus is on building these production-grade systems entirely on the edge, ensuring privacy, speed, and cost-efficiency. This technical guide explores the architecture and implementation of The AI Podcast Studio. This project demonstrates the seamless integration of the Microsoft Agent Framework, Local Small Language Models (SLMs), and VibeVoice to automate a complete tech podcast pipeline. I. The Strategic Intelligence Layer: Why Local-First? At the core of our studio is a Local-First philosophy. While cloud-based LLMs are powerful, they introduce friction in high-frequency, creative pipelines. By using Ollama as a model manager, we run SLMs like Qwen-3-8B directly on user hardware. 1. Architectural Comparison: Local vs. Cloud Choosing the deployment environment is a fundamental architectural decision. For an agentic podcasting workflow, the edge offers distinct advantages: Dimension Local Models (e.g., Qwen-3-8B) Cloud Models (e.g., GPT-5.2) Latency Zero/Ultra-low: Instant token generation without network "jitter". Variable: Dependent on network stability and API traffic. Privacy Total Sovereignty: Creative data and drafts never leave the local device. Shared Risk: Data is processed on third-party servers. Cost Zero API Fees: One-time hardware investment; free to run infinite tokens. Pay-as-you-go: Costs scale with token count and frequency of calls. Availability Offline: The studio remains functional without an internet connection. Online Only: Requires a stable, high-speed connection. 2. Reasoning and Tool-Calling on the Edge To move beyond simple chat, we implement Reasoning Mode, utilizing Chain-of-Thought (CoT) prompting. This allows our local agents to "think" through the podcast structure before writing. Furthermore, we grant them "superpowers" through Tool-Calling, allowing them to execute Python functions for real-time web searches to gather the latest news. II. The Orchestration Engine: Microsoft Agent Framework The true complexity of this project lies in Agent Orchestration—the coordination of specialized agents to work as a cohesive team. We distinguish between Agents, who act as "Jazz Musicians" making flexible decisions, and Workflows, which act as the "Orchestra" following a predefined score. 1. Advanced Orchestration Patterns Drawing from the WorkshopForAgentic architecture, the studio utilizes several sophisticated patterns: Sequential: A strict pipeline where the output of the Researcher flows into the Scriptwriter. Concurrent (Parallel): Multiple agents search different news sources simultaneously to speed up data gathering. Handoff: An agent dynamically "transfers" control to another specialist based on the context of the task. Magentic-One: A high-level "Manager" agent decides which specialist should handle the next task in real-time. III. Implementation: Code Analysis (Workshop Patterns) To maintain a production-grade codebase, we follow the modular structure found in the WorkshopForAgentic/code directory. This ensures that agents, clients, and workflows are decoupled and maintainable. 1. Configuration: Connecting to Local SLMs The first step is initializing the local model client using the framework's Ollama integration. # Based on WorkshopForAgentic/code/config.py from agent_framework.ollama import OllamaChatClient # Initialize the local client for Qwen-3-8B # Standard Ollama endpoint on localhost chat_client = OllamaChatClient( model_id="qwen3:8b", endpoint="http://localhost:11434" ) 2. Agent Definition: Specialized Roles Each agent is a ChatAgent instance defined by its persona and instructions. # Based on WorkshopForAgentic/code/agents.py from agent_framework import ChatAgent # The Researcher Agent: Responsible for web discovery researcher_agent = client.create_agent( name="SearchAgent", instructions="You are my assistant. Answer the questions based on the search engine.", tools=[web_search], ) # The Scriptwriter Agent: Responsible for conversational narrative generate_script_agent = client.create_agent( name="GenerateScriptAgent", instructions=""" You are my podcast script generation assistant. Please generate a 10-minute Chinese podcast script based on the provided content. The podcast script should be co-hosted by Lucy (the host) and Ken (the expert). The script content should be generated based on the input, and the final output format should be as follows: Speaker 1: …… Speaker 2: …… Speaker 1: …… Speaker 2: …… Speaker 1: …… Speaker 2: …… """ ) 3. Workflow Setup: The Sequential Pipeline For a deterministic production line, we use the WorkflowBuilder to connect our agents. # Based on WorkshopForAgentic/code/workflow_setup.py from agent_framework import WorkflowBuilder # Building the podcast pipeline search_executor = AgentExecutor(agent=search_agent, id="search_executor") gen_script_executor = AgentExecutor(agent=gen_script_agent, id="gen_script_executor") review_executor = ReviewExecutor(id="review_executor", genscript_agent_id="gen_script_executor") # Build workflow with approval loop # search_executor -> gen_script_executor -> review_executor # If not approved, review_executor -> gen_script_executor (loop back) workflow = ( WorkflowBuilder() .set_start_executor(search_executor) .add_edge(search_executor, gen_script_executor) .add_edge(gen_script_executor, review_executor) .add_edge(review_executor, gen_script_executor) # Loop back for regeneration .build() ) IV. Multimodal Synthesis: VibeVoice Technology The "Future Bytes" podcast is brought to life using VibeVoice, a specialized technology from Microsoft Research designed for natural conversational synthesis. Conversational Rhythm: It automatically handles natural turn-taking and speech cadences. High Efficiency: By operating at an ultra-low 7.5 Hz frame rate, it significantly reduces the compute power required for high-fidelity audio. Scalability: The system supports up to 4 distinct voices and can generate up to 90 minutes of continuous audio. V. Observability and Debugging: DevUI Building multi-agent systems requires deep visibility into the agentic "thinking" process. We leverage DevUI, a specialized web interface for testing and tracing: Interactive Tracing: Developers can watch the message flow and tool-calling in real-time. Automatic Discovery: DevUI auto-discovers agents defined within the project structure. Input Auto-Generation: The UI generates input fields based on workflow requirements, allowing for rapid iteration. VI. Technical Requirements for Edge Deployment Deploying this studio locally requires specific hardware and software configurations to handle simultaneous LLM and TTS inference: Software: Python 3.10+, Ollama, and the Microsoft Agent Framework. Hardware: 16GB+ RAM is the minimum requirement; 32GB is recommended for running multiple agents and VibeVoice concurrently. Compute: A modern GPU/NPU (e.g., NVIDIA RTX or Snapdragon X Elite) is essential for smooth inference. Final Perspective: From Coding to Directing The AI Podcast Studio represents a significant shift toward Agentic Content Creation. By mastering these orchestration patterns and leveraging local EdgeAI, developers move from simply writing code to directing entire ecosystems of intelligent agents. This "local-first" model ensures that the future of creativity is private, efficient, and infinitely scalable. Download sample Here Resource EdgeAI for Beginners - https://github.com/microsoft/edgeai-for-beginners Microsoft Agent Framework - https://github.com/microsoft/agent-framework Microsoft Agent Framework Samples - https://github.com/microsoft/agent-framework-samplesOpen-Source SDK for Evaluating AI Model Outputs (Sharing Resource)
Hi everyone, I wanted to share a helpful open-source resource for developers working with LLMs, AI agents, or prompt-based applications. One common challenge in AI development is evaluating model outputs in a consistent and structured way. Manual evaluation can be subjective and time-consuming. The project below provides a framework to help with that: AI-Evaluation SDK https://github.com/future-agi/ai-evaluation Key Features: - Ready-to-use evaluation metrics - Supports text, image, and audio evaluation - Pre-defined prompt templates - Quickstart examples available in Python and TypeScript - Can integrate with workflows using toolkits like LangChain Use Case: If you are comparing different models or experimenting with prompt variations, this SDK helps standardize the evaluation process and reduces manual scoring effort. If anyone has experience with other evaluation tools or best practices, I’d be interested to hear what approaches you use
