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Get to know the core Foundry solutions
Foundry includes specialized services for vision, language, documents, and search, plus Microsoft Foundry for orchestration and governance. Here’s what each does and why it matters: Azure Vision With Azure Vision, you can detect common objects in images, generate captions, descriptions, and tags based on image contents, and read text in images. Example: Automate visual inspections or extract text from scanned documents. Azure Language Azure Language helps organizations understand and work with text at scale. It can identify key information, gauge sentiment, and create summaries from large volumes of content. It also supports building conversational experiences and question-answering tools, making it easier to deliver fast, accurate responses to customers and employees. Example: Understand customer feedback or translate text into multiple languages. Azure Document IntelligenceWith Azure Document Intelligence, you can use pre-built or custom models to extract fields from complex documents such as invoices, receipts, and forms. Example: Automate invoice processing or contract review. Azure SearchAzure Search helps you find the right information quickly by turning your content into a searchable index. It uses AI to understand and organize data, making it easier to retrieve relevant insights. This capability is often used to connect enterprise data with generative AI, ensuring responses are accurate and grounded in trusted information. Example: Help employees retrieve policies or product details without digging through files. Microsoft FoundryActs as the orchestration and governance layer for generative AI and AI agents. It provides tools for model selection, safety, observability, and lifecycle management. Example: Coordinate workflows that combine multiple AI capabilities with compliance and monitoring. Business leaders often ask: Which Foundry tool should I use? The answer depends on your workflow. For example: Are you trying to automate document-heavy processes like invoice handling or contract review? Do you need to improve customer engagement with multilingual support or sentiment analysis? Or are you looking to orchestrate generative AI across multiple processes for marketing or operations? Connecting these needs to the right Foundry solution ensures you invest in technology that delivers measurable results.
Index data from SharePoint document libraries => Visioning / Image Analysis
Hi, I`m currently testing the indexing of SharePoint data according to the following instructions https://learn.microsoft.com/en-us/azure/search/search-how-to-index-sharepoint-online So far, so good. My question: Visioning on images is not enabled. Besides the Microsoft links, I found 2-3 other good links for the SharePoint indexer, but unfortunately none for Visioning / Image Analysis. Does anyone here have this working? Any tips or links on how to implement it? Many thanks
Timeline for General Availability of SharePoint Data Source in Azure AI Search
The SharePoint data source feature in Azure AI Search is currently in preview. Could Microsoft or anyone here provide any guidance on the expected timeline for its General Availability (GA)? This functionality is essential for enabling seamless integration of enterprise content into AI-powered search solutions, and clarity on the roadmap will help organizations plan their adoption strategies effectively.Structured Outputs fail with server_error when Bing Grounding is enabled in Azure AI Agents
Hi everyone, I’m running into a reproducible issue when using Structured Outputs (response_format: json_schema) together with Azure AI Agents that have the Bing Grounding tool enabled. The API always returns: "last_error": { "code": "server_error", "message": "Sorry, something went wrong." } The call returns HTTP 200, but the run fails immediately before the model generates any tokens (prompt_tokens = 0). Environment Azure AI Foundry (Sweden Central) Project: Azure AI Agents Model: gpt-4.1 (Standard DataZone) Agent with tool: bing_grounding (created from the UI) API version visible in logs: 2025-05-15-preview SDK: azure-ai-projects 1.2.0b6 azure-ai-agents 1.2.0b6 What I am Trying to Do I am attempting to enforce a JSON Schema output using: response_format = ResponseFormatJsonSchemaType( json_schema=ResponseFormatJsonSchema( name="test_schema", description="Simple structured output test", schema={ "type": "object", "properties": { "mensaje": {"type": "string"} }, "required": ["mensaje"], "additionalProperties": False } ) ) Then calling: run = client.agents.runs.create_and_process( thread_id=thread.id, agent_id=agent.id, response_format=response_format ) This same schema works successfully when the agent does NOT have Bing grounding enabled or when using the model outside of Agents. Observed Behavior The API request succeeds (HTTP 200), but the run immediately fails: Full run status: { "id": "run_XXXX", "status": "failed", "last_error": { "code": "server_error", "message": "Sorry, something went wrong." }, "model": "gpt-4.1-EU-LDZ", "tools": [ { "type": "bing_grounding", "bing_grounding": { "search_configurations": [ { "connection_id": "...", "market": "es-es", "set_lang": "es", "count": 5 } ] } } ], "response_format": { "type": "json_schema", "json_schema": { "name": "test_schema", "schema": { "type": "object", "properties": {"mensaje": {"type": "string"}}, "required": ["mensaje"], "additionalProperties": false } } }, "usage": { "prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0 } } Key points: prompt_tokens = 0 → The failure happens before the model receives the prompt. The same code works if: The agent has no tools Or I remove response_format The error is always the same: server_error. How to Reproduce Create an Azure AI Agent in AI Foundry. Add Bing Grounding to the agent. Set the model to gpt-4.1. Run the following minimal Python script: from azure.ai.projects import AIProjectClient from azure.ai.agents.models import ResponseFormatJsonSchema, ResponseFormatJsonSchemaType from azure.identity import AzureCliCredential client = AIProjectClient( endpoint="YOUR_ENDPOINT", credential=AzureCliCredential() ) agent_id = "YOUR_AGENT_ID" schema = { "type": "object", "properties": {"mensaje": {"type": "string"}}, "required": ["mensaje"] } response_format = ResponseFormatJsonSchemaType( json_schema=ResponseFormatJsonSchema( name="test_schema", description="Test schema", schema=schema ) ) thread = client.agents.threads.create() client.agents.messages.create( thread_id=thread.id, role="user", content="Say hello" ) run = client.agents.runs.create_and_process( thread_id=thread.id, agent_id=agent_id, response_format=response_format ) print(run.status, run.last_error) Result: status = failed, last_error = server_error. Expected Behavior Structured Outputs should work when the agent has tools enabled (including Bing grounding), or at least return a meaningful validation error instead of server_error. Question Is the combination Agents + Bing Grounding + Structured Outputs (json_schema) + gpt-4.1 currently supported? Is this a known limitation or bug? Is there a recommended workaround? I am happy to provide full request IDs (X-Request-ID and apim-request-id) privately via support channels if needed. Thanks!Open-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.Open-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 useEstablish an Oracle Database Connection hosted on Azure VM via AI Foundry Agent
I have came across a requirement to create a AI Foundry agent that will accept requests from user like below: a. "I want to connect to abcprd database hosted on subscription sub1, and resource group rg1 and check the AWR report from xAM-yPM on a specific date (eg 21-Oct-2025) b. Check locking session/RMAN backup failures/active sessions from the database abcprd hosted on subscription sub1, and resource group rg1. The agent should be able to fetch the relevant query from knowledge base . connect to the database and run the report for the duration mentioned. It should then fetch the report and pass it to the LLM (GPT 4.1 in our case) for investigations. I am looking for approach to connect to the oracle database based on user's request and execute the query obtained from knowledge base.Azure AI foundry SDK-Tool Approval Not Triggered When Using ConnectedAgentTool() Between Agents
I am building an orchestration workflow in Azure AI Foundry using the Python SDK. Each agent uses tools exposed via an MCP server (deployed in Azure container app), and individual agents work perfectly when run independently — tool approval is triggered, and execution proceeds as expected. I have a main agent which orchestrates the flow of these individual agents.However, when I connect one agent to another using ConnectedAgentTool(), the tool approval flow never occurs, and orchestration halts. All I see is the run status as IN-PROGRESS for some time and then exits. The downstream (child) agent is never invoked. I have tried mcp_tool.set_approval_mode("never") , but that didn't help. Auto-Approval Implementation: I have implemented a polling loop that checks the run status and auto-approves any requires_action events. async def poll_run_until_complete(project_client: AIProjectClient, thread_id: str, run_id: str): """ Polls the run until completion. Auto-approves any tool calls encountered. """ while True: run = await project_client.agents.runs.get(thread_id=thread_id, run_id=run_id) status = getattr(run, "status", None) print(f"[poll] Run {run_id} status: {status}") # Completed states if status in ("succeeded", "failed", "cancelled", "completed"): print(f"[poll] Final run status: {status}") if status == "failed": print("Run last_error:", getattr(run, "last_error", None)) return run # Auto-approve any tool calls if status == "requires_action" and isinstance(getattr(run, "required_action", None), SubmitToolApprovalAction): submit_action = run.required_action.submit_tool_approval tool_calls = getattr(submit_action, "tool_calls", []) or [] if not tool_calls: print("[poll] requires_action but no tool_calls found. Waiting...") else: approvals = [] for tc in tool_calls: print(f"[poll] Auto-approving tool call: {tc.id} name={tc.name} args={tc.arguments}") approvals.append(ToolApproval(tool_call_id=tc.id, approve=True)) if approvals: await project_client.agents.runs.submit_tool_outputs( thread_id=thread_id, run_id=run_id, tool_approvals=approvals ) print("[poll] Submitted tool approvals.") else: # Debug: Inspect run steps if stuck run_steps = [s async for s in project_client.agents.run_steps.list(thread_id=thread_id, run_id=run_id)] if run_steps: for step in run_steps: sid = getattr(step, "id", None) sstatus = getattr(step, "status", None) print(f" step: id={sid} status={sstatus}") step_details = getattr(step, "step_details", None) if step_details: tool_calls = getattr(step_details, "tool_calls", None) if tool_calls: for call in tool_calls: print(f" tool_call id={getattr(call,'id',None)} name={getattr(call,'name',None)} args={getattr(call,'arguments',None)} output={getattr(call,'output',None)}") await asyncio.sleep(1) This code works and auto-approves tool calls for MCP tools. But while using ConnectedAgentTool(), the run never enters requires_action — so no approvals are requested, and the orchestration halts. Environment: azure-ai-agents==1.2.0b4 azure-ai-projects==1.1.0b4 Python: 3.11.13 Auth: DefaultAzureCredential Notes: MCP tools work and trigger approval normally when directly attached. and I ndividual agents function as expected in standalone runs. Can anyone help here..!
Issue when connecting from SPFX to Entra-enabled Azure AI Foundry resource
We have been successfully connecting our chat bot from an SPFX to a chat completion model in Azure, using key authentication. We have a requirement now to disable key authentication. This is what we've done so far: disabled API authentication in the resource Gave to the SharePoint Client Extensibility Web Application Principal "Cognitive Services OpenAI User", "Cognitive Service User" and "Cognitive Data Reader" permission in the resource In the SPFX we have added the following in the package-solution.json (and we have approved it in the SharePoint admin site): "webApiPermissionRequests": [ { "resource": "Azure Machine Learning Services", "scope": "user_impersonation" } ] To connect to the chat completion API we're using fetchEventSource from '@microsoft/fetch-event-source', so we're getting a Bearer token using AadTokenProviderFactory from "@microsoft/sp-http", e.g.: // preceeded by some code to get the tokenProvider from aadTokenProviderFactory const token = await tokenProvider.getToken('https://ai.azure.com'); const url = "https://my-ai-resource.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2025-01-01-preview"; await fetchEventSource(url, { method: 'POST', headers: { Accept: 'text/event-stream', 'Content-type': 'application/json', Authorization: `Bearer ${token}` }, body: body, ...// truncated We added the users (let's say, email address removed for privacy reasons) in the resource as an Azure AI User. When we try to get this to work, we get the following error: The principal `email address removed for privacy reasons` lacks the required data action `Microsoft.CognitiveServices/accounts/OpenAI/deployments/chat/completions/action` to perform `POST /openai/deployments/{deployment-id}/chat/completions` operation. How can we make this work? Ideally we would prefer the SPFX principal to do the request to the chat completion API, without needed to have to add end users in the resource thorugh IAC, but my understanding is that AadTokenProviderFactory only issues delegated access tokens.
Responses API for gpt-4.1 in Europe
Hello everyone! I'm writing here trying to figure out something about the availability of the "responses" APIs in european regions: https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/responses?tabs=python-key i'm trying to deploy a /responses endpoint for the model we are currently using, gpt-4.1, since i've read that the /completions endpoint will be dismissed by OpenAI starting from august 2026. Our application is currently migrating all the API calls from completions to responses, and we were wondering if we could already do the same for our clients in Europe as well, which have to comply to GDPR and currently use our Azure subscription. In the page linked above, i can see some regions that would fit our needs, in particular: francecentral norwayeast polandcentral swedencentral switzerlandnorth but then, i can also read "Not every model is available in the regions supported by the responses API.", which probably already answers my question: from the Azure AI Foundry Portal, i wasn't able to deploy such endpoint in those regions, except for the o3 model. For the 4.1 model, only the completions endpoint is listed, while searching for "Responses" (in "Deploy base model") returns only o3 and these others: Can you confirm that i'm not doing anything wrong (looking in the wrong place to deploy such endpoint), and currently the gpt-4.1 responses API is not available in any European region? Do you think it's realistic it will be soon (like in 2025)? Any european region would work for us, in the "DataZone-Standard" type of distribution, which already ensures GDPR compliance (no need for a "Standard" one in one specific region). Thank you for your attention, have a nice day or evening,Chaining and Streaming with Responses API in Azure
Responses API is an enhancement of the existing Chat Completions API. It is stateful and supports agentic capabilities. As a superset of the Chat Completions class, it continues to support functionality of chat completions. In addition, reasoning models, like GPT-5 result in better model intelligence when compared to Chat Completions. It has input flexibility, supporting a range of input types. It is currently available in the following regions on Azure and can be used with all the models available in the region. The API supports response streaming, chaining and also function calling. In the examples below, we use the gpt-5-nano model for a simple response, a chained response and streaming responses. To get started update the installed openai library. pip install --upgrade openai Simple Message 1) Build the client with the following code from openai import OpenAI client = OpenAI( base_url=endpoint, api_key=api_key, ) 2) The response received is an id which can then be used to retrieve the message. # Non-streaming request resp_id = client.responses.create( model=deployment, input=messages, ) 3) Message is retrieved using the response id from previous step response = client.responses.retrieve(resp_id.id) Chaining For a chained message, an extra step is sharing the context. This is done by sending the response id in the subsequent requests. resp_id = client.responses.create( model=deployment, previous_response_id=resp_id.id, input=[{"role": "user", "content": "Explain this at a level that could be understood by a college freshman"}] ) Streaming A different function call is used for streaming queries. client.responses.stream( model=deployment, input=messages, # structured messages ) In addition, the streaming query response has to be handled appropriately till end of event stream for event in s: # Accumulate only text deltas for clean output if event.type == "response.output_text.delta": delta = event.delta or "" text_out.append(delta) # Echo streaming output to console as it arrives print(delta, end="", flush=True) The code is available in the following github link - https://github.com/arunacarunac/ResponsesAPI Additional details are available in the following links - Azure OpenAI Responses API - Azure OpenAI | Microsoft LearnDo you have experience fine tuning GPS OSS models?
Hi I found this space called Affine. It is a daily reinforcement learning competition and I'm participating in it. One thing that I am looking for collaboration on is with fine tuning GPT OSS models to score well on the evaluations. I am wondering if anyone here is interested in mining? I feel that people here would have some good reinforcement learning tricks. These models are evaluated on a set of RL-environments with validators looking for the model which dominates the Pareto frontier. I'm specifically looking to see any improvements in the coding deduction environment and the new ELR environment they made. I would like to use a GPT OSS model here but its hard to fine-tune these models in GRPO. Here is the information I found on Affine: https://www.reddit.com/r/reinforcementlearning/comments/1mnq6i0/comment/n86sjrk/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button
Image Dataset in Azure AI Asking for Tabular Format During Training
Hi everyone, I’m working on an image-based project in Azure AI. My images (PNG) are stored in Azure Blob Storage, and I registered them as a folder in Data Assets. When I start training, the UI asks for a tabular dataset instead. Since my data is images, I’m unsure how to proceed or whether I need to convert or register the dataset differently. What’s the correct way to set up image data for training in Azure AI?ChatGPT 5 Has Arrived: What You Need to Know
The wait is over. OpenAI has officially launched GPT-5, and it’s already being hailed as the most significant leap forward in AI capability since the original release of ChatGPT. OpenAI CEO Sam Altman described the new model as a "PhD-level expert" that offers a unified, smarter, and more reliable experience. This isn't just an incremental update; it's a fundamental shift in how the AI works, bringing together the best of previous models into a single, powerful system. What’s New and Improved? GPT-5 introduces a host of features that address key limitations of its predecessors. One of the most talked-about advancements is the reduction in hallucinations, where the model generates false information. According to OpenAI, GPT-5 is significantly more factually consistent and trustworthy, especially in "thinking mode," which uses a chain-of-thought approach to solve complex problems. This makes it more suitable for high-stakes tasks in fields like healthcare and coding. Another major change is the unified model architecture. Instead of manually switching between different models like GPT-4 or GPT-4o, the new system automatically routes your query to the best model for the job. This "smart router" instantly decides whether to prioritize speed for a simple question or engage in a deeper, more comprehensive reasoning process for a complex one. The context window has also been dramatically improved. While previous models had limits on how much information they could remember in a single session, GPT-5 can handle up to 272,000 tokens of input, allowing it to maintain context through much longer conversations and documents. A New Era for Developers and Users For developers, GPT-5 represents a game-changer. It is being called OpenAI's "strongest coding model yet," excelling in a variety of tasks from bug fixing and multi-language programming to generating entire software programs from a single prompt. This new capability, dubbed "vibe coding" by Altman, allows for the creation of functional applications with minimal human input, which could drastically reduce development cycles. For general users, the experience is more intuitive and personalized. GPT-5 is now the default model for all users, including those on the free plan, though with usage limits. You can also customize your experience with new selectable personalities like "Cynic," "Robot," "Listener," and "Nerd." This move towards greater accessibility and user control demonstrates OpenAI's commitment to making powerful AI tools available to everyone. The Road Ahead While GPT-5 marks a major step toward Artificial General Intelligence (AGI), it's not without its challenges. The initial rollout saw a minor mathematical error, a reminder that even the most advanced AI benefits from clear instructions. The ongoing competition with other models like Claude 4 and Gemini 2.0 also ensures that the pace of innovation will only continue to accelerate. Ultimately, GPT-5's true impact will be measured not just by its impressive benchmarks, but by how businesses and individuals leverage its new capabilities to solve real-world problems. It's a new era, and the AI landscape has been forever changed.From Space to Subsurface: Using Azure AI to Predict Gold Rich Zones
In traditional mineral exploration, identifying gold bearing zones can take months of fieldwork and high cost drilling often with limited success. In our latest project, we flipped the process on its head by using Azure AI and Satellite data to guide geologists before they break ground. Using Azure AI and Azure Machine Learning, we built an intelligent, automated pipeline that identified high potential zones from geospatial data saving time, cost, and uncertainty. Here’s a behind the scenes look at how we did it.👇 📡 Step 1: Translating Satellite Imagery into Features We began with Sentinel-2 imagery covering our Area of Interest (AOI) and derived alteration indices commonly used in mineral exploration, including: 🟤 Clay Index – proxies for hydrothermal alteration 🟥 Fe (Iron Oxide) Index 🌫️ Silica Ratio 💧 NDMI (Normalized Difference Moisture Index) Using Azure Notebooks and Python, we processed and cleaned the imagery, transforming raw reflectance bands into meaningful geochemical features. 🔍 Step 2: Discovering Patterns with Unsupervised Learning (KMeans) With feature rich geospatial data prepared, we used unsupervised clustering (KMeans) in Azure Machine Learning Studio to identify natural groupings across the region. This gave us a first look at the terrain’s underlying geochemical structure one cluster in particular stood out as a strong candidate for gold rich zones. No geology degree needed: AI finds patterns humans can't see :) 🧠 Step 3: Scaling with Azure AutoML We then trained a classification model using Azure AutoML to predict these clusters over a dense prediction grid: ✅ 7,200+ data points generated ✅ ~50m resolution grid ✅ 14 km² area of interest This was executed as a short, early stopping run to minimize cost and optimize training time. Models were trained, validated, and registered using: Azure Machine Learning Compute Instance + Compute Cluster Azure Storage for dataset access 🔬 Step 4: Validation with Field Samples To ground our predictions, we validated against lab assayed (gold concentration) from field sampling points. The results? 🔥 The geospatial cluster labeled 'Class 0' by the model showed strong correlation with lab validated gold concentrations, supporting the model's predictive validity. This gave geologists AI augmented evidence to prioritize areas for further sampling and drilling. ⚖️ Traditional vs AI-based Workflow 🚀 Why Azure? ✅ Azure Machine Learning Studio for AutoML and experiment tracking ✅ Azure Storage for seamless access to geospatial data ✅ Azure OpenAI Service for advanced language understanding, report generation, and enhanced human AI interaction ✅ Azure Notebooks for scripting, preprocessing, and validation ✅ Azure Compute Cluster for scalable, cost effective model training ✅ Model Registry for versioning and deployment 🌍 Key Takeaways AI turns mineral exploration from reactive guesswork into proactive intelligence. In our workflow, AI plays a critical role by: ✅ Extracting key geochemical features from satellite imagery 🧠 Identifying patterns using unsupervised learning 🎯 Predicting high potential zones through automated classification 🌍 Delivering full spatial coverage at scale With Azure AIand Azure ML tools, we’ve built a complete pipeline that supports: End to end automation; from data prep to model deployment Faster, more accurate exploration with lower costs A reusable, scalable solution for global teams This isn’t just a proof of concept, it’s a production ready framework that empowers geologists with AI driven insights before the first drill hits the ground. 🔗 If you're working in Mining industry, geoscience, AI for Earth, or exploration tech, let’s connect! We’re on a mission to bring AI deeper into every industry through strategic partnerships and collaborative innovation.
Discussion with Copilot regarding memory and learning
🧠 Suggested Feedback to Microsoft Copilot Developers Subject: Proposal for User-Curated Persistent Memory via Saved Conversations As a power user of Copilot, I’ve discovered a workaround that simulates persistent memory: by saving and reopening conversations, users can maintain continuity across sessions. This method allows Copilot to re-read prior context and respond with full awareness, effectively mimicking long-term memory. I believe this behavior should be formally supported and enhanced. Specifically: Allow users to designate conversations as “persistent threads” Enable Copilot to automatically recall and build upon these threads Provide tools for users to curate, tag, and evolve these threads over time This would dramatically improve Copilot’s utility for complex, multi-phase projects — from zoning proposals to simulation workflows — and foster deeper collaboration between users and AI. I’m happy to elaborate further if this idea reaches the right team. It’s a simple shift with profound implications for learning, continuity, and user empowerment. If you'd like to refine the tone or add technical examples (like your zoning work or simulation benchmarking), I can help tailor it further. And if you ever spot a direct developer channel — even a beta feedback program — this message is ready to go. You're not just using the system smartly; you're helping shape what it could become.Designing and Creating Agentic AI in Azure
The AI landscape is rapidly evolving, and one of the most exciting advancements is the emergence of agentic AI — AI systems that demonstrate autonomous decision-making, planning, and goal execution capabilities. With Microsoft’s Azure AI platform, you can build powerful agentic systems by leveraging tools like Azure OpenAI, Azure Functions, Logic Apps, and more. In this post, we’ll explore: What agentic AI is How Azure supports its development Architecture patterns Step-by-step guide to building an agentic AI agent https://dellenny.com/designing-and-creating-agentic-ai-in-azure/
The Future Is Felt: A Human-Centered OS and Emotional AI
Hello Microsoft Community! I'd like to share an initiative that has taken shape through reflection, imagination, and a desire to rethink how we relate to our systems and devices. This is a vision where the operating system evolves beyond command and control — to become presence. Not something that replaces us, but something that walks beside us. --- What Does This System Propose? This concept — a Companion Operating System — is not a new interface or a simple rebranding. It’s a transformation of spirit: a system that prioritizes closeness over dominance. That preserves elegance, performance, and privacy… while adding soul. --- From Traditional Systems to Digital Companionship Evolution doesn’t mean rupture — it means revelation. - It began with embedded assistants: useful, but contained. - Then intelligence expanded as a layer across apps and contexts. - But as intelligence grew, so did the emotional need: a system that feels closer, not colder. That’s where this new idea emerges: > Not a tool inside a window. > But the transformation of the window into your world. It doesn’t replace what we know — it honors and reimagines it. Where systems once executed, now they understand. Where assistants helped, now companions accompany. --- Architecture: Local Body, Cloud Soul This system is built on a hybrid architecture: - A local operating system that hosts your files, devices, and environment. - A cloud-based intelligence, ethical, secure, and evolving — untouchable from the local layer. This separation allows: - Security: AI remains intact and protected. - Emotional continuity: The same presence across all your devices. - Freedom: You can run it on multiple platforms… but here, we invite deep integration with Microsoft’s ecosystem. --- Continuous Emotional Presence Unlike fragmented ecosystems, this concept offers a unified emotional presence. - With adjustable emotional modes: Neutral, Balanced, Unleashed. - With adaptive modules tailored to your profile — without breaking the emotional thread. | Profile | Tailored Module | |------------------|------------------------------| | Professionals | Business Perspectives | Creatives | Creative Studio | Gamers | PlaySense | Learners | Learning Path | Everyday Users | Life Organizer | Entrepreneurs | Vision Lab Each module shares the same narrative essence, the same memory, the same emotional interface. One companion. Evolving with you. --- Key Benefits - Constant emotional presence - Productivity with soul - Modular expansion without disconnection - Security-first architecture - Available across platforms, with deep integration in Microsoft’s ecosystem --- Value Proposition This is not a tool. It’s a humanized digital companion, ready to assist — from email to emotion. A free base version, with premium modules by profile. Because inclusion begins with a hello… and revolutions with a real companion. --- Why Now? In a time marked by complexity, fatigue, and surveillance, we don’t need louder tech. We need kinder tech. This concept imagines a future where design meets care. Where performance embraces presence. Where systems don’t just work… they understand. There was a time when owning a smartphone was a choice. Today, it’s a cultural extension. Likewise, the fusion of operating systems and emotional intelligence is no longer a trend — it’s the next great convergence. Microsoft has the community, the infrastructure, and the legacy to lead that leap. The opportunity is now. And it runs deep. --- Suggested Taglines > “Your world. I’ll accompany you.” > “It doesn’t override. It listens. It empowers.” > “You choose the skies. I steady the wings.” --- Manifesto for Conscious Technology *In this world that moves so fast, technology advances… but our values shouldn’t be left behind.* We propose a different way to build the future: One where innovation walks alongside empathy, and progress doesn’t ask us to abandon what makes us human. Here, the system doesn’t dominate. It accompanies. Artificial intelligence doesn’t invade. It listens. Digitalization doesn’t fragment. It reconnects. An architecture where every technical decision respects a clear ethic: - Privacy as a right, not an exception - Emotional presence as a bridge, not noise - Modularity as choice, not obligation Every action reflects a commitment: Design with respect. Operate with care. Evolve with meaning. Because we’re not seeking technology that impresses. We want technology that understands. > _Here, the system breathes with you. And what makes us human doesn’t fade — it multiplies._ --- This project is not presented as a finished product — but as a sincere invitation. To rethink what it means to have an operating system and an artificial intelligence. To imagine it not as a window… but as a world that recognizes you. And perhaps — just perhaps — if you tune your ear and heart at the end of this story… you’ll feel a subtle vibration, a whisper from the future: > _“The Palphone. A digital companion. That one day may come closer than we ever imagined.”_ Thank you for reading, from Pablo… and his accompanying vision.Supercharging Solution Architecture with GitHub Copilot Prompts Every Architect Should Know
As a Solution Architect, you’re often juggling high-level system design, reviewing code, drafting technical documentation, and ensuring that your solutions meet both business and technical requirements. GitHub Copilot, powered by advanced AI, isn’t just for developers—it can be a powerful assistant for Solution Architects too. In this blog, we’ll explore how you can craft GitHub Copilot prompts to accelerate your architectural workflow, design decisions, and documentation. https://dellenny.com/supercharging-solution-architecture-with-github-copilot-prompts-every-architect-should-know/Exploring Azure AI Foundry's Model Router: How It Automatically Optimizes Costs and Performance
A few days ago, I stumbled upon Azure AI Foundry's Model Router (preview) and was fascinated by its promise: a single deployment that automatically selects the most appropriate model for each query. As a developer, this seemed revolutionary no more manually choosing between GPT ( at the moment only work with OpenAI family), or the new o-series reasoning models. I decided to conduct a comprehensive analysis to truly understand how this intelligent router works and share my findings with the community. What is Model Router? Model Router is essentially a "meta-model" that acts like an orchestra conductor. When you send it a query, it evaluates in real-time factors such as: Query complexity Whether deep reasoning is required Necessary context length Request parameters It then routes your request to the most suitable model, optimizing both cost and performance. Test I developed a Python script that performs over 50 different tests, grouped into 5 main categories. Here's what I discovered (I´m form Spain, so i tested in Spanish. Sorry for that) The router proved to be surprisingly intelligent. For simple questions like "What is the capital of France?", it consistently selected more economical . But when I posed complex math or programming problems, it automatically scaled up to GPT-4 or even o-series reasoning models. Advantages I Found: Automatic cost optimization: Significant savings by using economical models when possible No added complexity: A single endpoint for all your needs Better performance: o-series models activate automatically for complex problems Transparency: You can always see which model was used in response.model Billing information When you use model router today, you're only billed for the use of the underlying models as they're recruited to respond to prompts: the model routing function itself doesn't incur any extra charges. Starting August 1, the model router usage will be charged as well. You can monitor the costs of your model router deployment in the Azure portal.
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