Chart your AI app and agent strategy with Microsoft Marketplace
Organizations exploring AI apps and agents face a critical choice: build, buy, or blend. There’s no one-size-fits-all—each approach offers unique benefits and trade-offs. Tune in for insights into the pros and cons of each approach and explore how the Microsoft Marketplace simplifies adoption by providing a single source for trusted AI apps, agents, and models. Learn how Marketplace accelerates time-to-value, reduces procurement times and serves as the trusted source to access a catalog of thousands of AI models, enabling you to innovate faster without sacrificing governance or cost control. Where do I post my questions? Scroll to the bottom of this page and select Comment. This session will be recorded and available on demand immediately after airing. It will feature AI-generated captions during the live broadcast. Human-generated captions and a recap of the Q&A will be available by the end of the week.Pantone’s Palette Generator enhances creative exploration with agentic AI on Azure
Color can be powerful. When creative professionals shape the mood and direction of their work, color plays a vital role because it provides context and cues for the end product or creation. For more than 60 years, creatives from all areas of design—including fashion, product, and digital—have turned to Pantone color guides to translate inspiration into precise, reproducible color choices. These guides offer a shared language for colors, as well as inspiration and communication across industries. Once rooted in physical tools, Pantone has evolved to meet the needs of modern creators through its trend forecasting, consulting services, and digital platform. Today, Pantone Connect and its multi-agent solution called the Pantone Palette Generator seamlessly bring color inspiration and accuracy into everyday design workflows (as well as the New York City mayoral race). Simply by typing in a prompt, designers can generate palettes in seconds. Available in Pantone Connect, the tool uses Azure services like Microsoft Foundry, Azure AI Search, and Azure Cosmos DB to serve up the company’s vast collection of trend and color research from the color experts at the Pantone Color Institute. reached in seconds instead of days. Now, with Microsoft Foundry, creatives can use agents to get instant color palettes and suggestions based on human insights and trend direction.” Turning Pantone’s color legacy into an AI offering The Palette Generator accelerates the process of researching colors and helps designers find inspiration or validate some of their ideas through trend-backed research. “Pantone wants to be where our customers are,” says Rohani Jotshi, Director of Software Engineering and Data at Pantone. “As workflows become increasingly digital, we wanted to give our customers a way to find inspiration while keeping the same level of accuracy and trust they expect from Pantone.” The Palette Generator taps into thousands of articles from Pantone’s Color Insider library, as well as trend guides and physical color books in a way that preserves the company’s color standards science while streamlining the creative process. Built entirely on Microsoft Foundry, the solution uses Azure AI Search for agentic retrieval-augmented generation (RAG) and Azure OpenAI in Foundry Models to reason over the data. It quickly serves up palette options in response to questions like “Show me soft pastels for an eco-friendly line of baby clothes” or “I want to see vibrant metallics for next spring.” Over the course of two months, the Pantone team built the initial proof of concept for the Palette Generator, using GitHub Copilot to streamline the process and save over 200 hours of work across multiple sprints. This allowed Pantone’s engineers to focus on improving prompt engineering, adding new agent capabilities, and refining orchestration logic rather than writing repetitive code. Building a multi-agent architecture that accelerates creativity The Pantone team worked with Microsoft to develop the multi-agent architecture, which is made up of three connected agents. Using Microsoft Agent Framework—an open source development kit for building AI orchestration systems—it was a straightforward process to bring the agents together into one workflow. “The Microsoft team recommended Microsoft Agent Framework and when we tried it, we saw how it was extremely fast and easy to create architectural patterns,” says Kristijan Risteski, Solutions Architect at Pantone. “With Microsoft Agent Framework, we can spin up a model in five lines of code to connect our agents.” When a user types in a question, they interact with an orchestrator agent that routes prompts and coordinates the more specialized agents. Behind the scenes an additional agent retrieves contextually relevant insights from Pantone’s proprietary Color Insider dataset. Using Azure AI Search with vectorized data indexing, this agent interprets the semantics of a user’s query rather than relying solely on keywords. A third agent then applies rules from color science to assemble a balanced palette. This agent ensures the output is a color combination that meets harmony, contrast, and accessibility standards. The result is a set of Pantone-curated colors that match the emotional and aesthetic tone of the request. “All of this happens in seconds,” says Risteski. To manage conversation flow and achieve long-term data persistence, Pantone uses Azure Cosmos DB, which stores user sessions, prompts, and results. The database not only enables designers to revisit past palette explorations but also provides Pantone with valuable usage intelligence to refine the system over time. “We use Azure Cosmos DB to track inputs and outputs,” says Risteski. “That data helps us fine-tune prompts, measure engagement, and plan how we’ll train future models.” Improving accuracy and performance with Azure AI Search With Azure AI Search, the Palette Generator can understand the nuance of color language. Instead of relying solely on keyword searches that might miss the complexity of words like “vibrant” or “muted,” Pantone’s team decided to use a vectorized index for more accurate palette results. Using the built-in vectorization capability of Azure AI Search, the team converted their color knowledge base—including text-based color psychology and trend articles—into numerical embeddings. “Overall, vector search gave us better results because it could understand the intent of the prompt, not just the words,“ says Risteski. “If someone types, ‘Show me colors that feel serene and oceanic,’ the system understands intent. It finds the right references across our color psychology and trend archives and delivers them instantly.” The team also found ways to reduce latency as they evolved their proof of concept. Initially, they encountered slow inference times and performance lags when retrieving search results. By switching from GPT-4.1 to GPT-5, latency improved. And using Azure AI Search to manage ranking and filtering results helped reduce the number of calls to the large language model (LLM). “With Azure, we just get the articles, put them in a bucket, and say ‘index it now,’ says Risteski. “It takes one or two minutes—and that’s it. The results are so much better than traditional search.” Moving from inspiration to palettes faster The Palette Generator has transformed how designers and color enthusiasts interact with Pantone’s expertise. What once took weeks of research and review can now be done in seconds. “Typically, if someone wanted to develop a palette for a product launch, it might take many months of research,” says Jotshi. “Now, they can type one sentence to describe their inspiration then immediately find Pantone-backed insight and options. Human curation will still be hugely important, but a strong set of starting options can significantly accelerate the palette development process.” Expanding the palette: The next phase for Pantone’s design agent Rapidly launching the Palette Generator in beta has redefined what the Pantone engineering team thought was possible. “We’re a small development team, but with Azure we built an enterprise-grade AI system in a matter of weeks,” says Risteski. “That’s a huge win for us.” Next up, the team plans to migrate the entire orchestration layer to Azure Functions, moving to a fully scalable, serverless deployment. This will allow Pantone to run its agents more efficiently, handle variable workloads automatically, and integrate seamlessly with other Azure products such as Microsoft Foundry and Azure Cosmos DB. At the same time, Pantone plans to expand its multi-agent system to include new specialized agents, including one focused on palette harmony and another focused on trend prediction.🎉Join the Microsoft Ignite 2025 NYC Community Summit in Times Square!
Get ready, New York! The Microsoft Ignite 2025 NYC Community Summit is coming to the heart of Times Square — and you’re invited to be part of the energy, insights, and innovation. Whether you're a seasoned tech leader, a cloud enthusiast, or just Ignite-curious, this two-day experience is your chance to connect with the local Microsoft customer community, attend live sessions by MVPs and local experts. Watch the live streamed Ignite keynote while engaging in real-time conversations with peers and experts. To attend please register here. 🎤 What to Expect Live Keynote Viewing: Watch Microsoft leaders unveil the latest in AI, cloud, and security. Community Conversations: Join breakout discussions with local customers and Microsoft experts. Exclusive Panels & Lightning Talks: Hear from industry voices and community MVPs. Food & Snacks Included: Because no community event is complete without them. 🌟 Featured Speakers & Sessions Explore a variety of exciting topics, including… Generating Pages in Power Apps Lights, Camera, Akka! The Actor Model & Agentic AI Orchestra How to create Moonshot solutions with AI Transforming Facility, Network and Organization Management with Visio and Power BI Elevating Construction: Real-Time Optimization with Azure Digital Twins and AI Building Agents in AI Foundry! Mastering Vibe Coding: 6 Suggestions for Successful Agentic Development What's new with Azure Load Balancer, NAT Gateway, and Public IP Addresses .NET Apps Everywhere! Accelerating Web Application Development with AI-Powered Tools: From Design to Deployment How (and why) Microsoft's upstream teams engage with multi-stakeholder open-source projects Leveling Up Agents: Copilot Studio for Enterprise Studios RAG Hero: Fast-Track Vector Search in .NET Building Resilient Systems Agentic Orchestration: Building Scalable, Open-Source Automation with A2A, MCP and RAG Patterns Microsoft MVP (Most Valued Professional) Panel Discussion Ignite Keynote Virtual Watch Session 🤝 Sponsors & Partners We’re proud to be supported by a fantastic group of sponsors who help make this event possible. 🔗 RSVP & Stay Connected Spots are limited, must register by November 11th, 2025 — don’t miss out! 👉 To attend please register here. Exact location provided upon registration acceptance.MCP & AI Unlocking Agentic Intelligence with a “USB-C Connector” for AI
MCP, or Model Context Protocol, is an open-source standard introduced by Anthropic in November 2024. It’s designed to create a unified bridge between AI models—especially large language models (LLMs)—and external systems like tools, databases, file repositories, and APIs. Think of MCP as the USB-C port for AI—just plug in, and the AI can access or drive external services without building unique integrations for each connection. Rather than coding separate connections for each model and tool, MCP uses a consistent, structured way for AI agents (MCP clients) to communicate with “MCP servers” that interface with external systems. https://dellenny.com/mcp-ai-unlocking-agentic-intelligence-with-a-usb-c-connector-for-ai/Exploring the Core Components of Microsoft Fabric A Unified Data Platform
As data continues to be the new oil, organizations are increasingly seeking robust platforms that can simplify and unify their data landscape. Enter Microsoft Fabric—a next-generation data platform introduced by Microsoft that brings together all the data and analytics tools needed in the modern enterprise, integrated into a single, SaaS-based solution. In this post, we’ll break down the key components of Microsoft Fabric, explain how they work together, and highlight why this platform is a game-changer for data professionals, developers, and decision-makers alike. https://dellenny.com/exploring-the-core-components-of-microsoft-fabric-a-unified-data-platform/Unlocking Innovation with Azure AI Foundry Agent Service
In today’s AI-driven landscape, the ability to build, orchestrate, and operationalize intelligent agents at scale is becoming increasingly critical for organizations seeking to leverage AI as a core capability. Microsoft’s Azure AI Foundry Agent Service, introduced as part of the Azure AI Studio ecosystem, is a game-changing platform designed to empower developers and enterprises to build sophisticated multi-agent AI systems with minimal friction. https://dellenny.com/unlocking-innovation-with-azure-ai-foundry-agent-service/Essentials to build and modernize AI applications on Azure
Need to confidently design AI applications? In this session we’ll explore the prescriptive guidance, resources and tools available within Azure Essentials to help you build and modernize reliable, secure AI applications. We will demo how to get started designing an AI workload using the AI assessment tool from the Azure Well-Architected Framework and then show how to build it using Azure AI Foundry. This session is part of Tech Accelerator: Mastering Azure and AI adoption. View the full agenda for more great sessions and insights.Govern, manage, and secure your AI deployments
Are you ready to adopt AI at scale? Explore the Azure Essentials AI adoption guidance and provide actionable steps and best practices to get your environment AI-ready. See how to make your AI workloads resilient and secure with solutions from Microsoft Defender for Cloud, Azure Purview, and the Azure Proactive Resiliency Library. This session is part of Tech Accelerator: Mastering Azure and AI adoption. View the full agenda for more great sessions and insights.Best practices for secure and reliable Azure projects
Join us for a conversation with senior Microsoft and AMD leaders as they discuss how organizations can securely and reliably migrate, modernize, and enhance existing deployments with Azure. Hear how Microsoft customers have leveraged Microsoft and AMD products and solutions to design, deploy, govern, manage their Azure workloads so they can drive ongoing performance and innovation in their organizations. Learn from the leaders about how you can best maximize your cloud and AI investment. This session is part of Tech Accelerator: Mastering Azure and AI adoption. Check out the full agenda for more great sessions and insights.Demystifying Gen AI Models - Transformers Architecture : 'Attention Is All You Need'
The Transformer architecture demonstrated that carefully designed attention mechanisms — without the need for sequential recurrence — could model language and sequences more effectively and efficiently. 1. Transformers Replace Recurrence Traditional models such as RNNs and LSTMs processed data sequentially. Transformers use self-attention mechanisms to process all tokens simultaneously, enabling parallelisation, faster training, and better handling of long-range dependencies. 2. Self-Attention is Central Each token considers (attends to) all other tokens to gather contextual information. Attention scores are calculated between every pair of input tokens, capturing relationships irrespective of their position. 3. Multi-Head Attention Enhances Learning Rather than relying on a single attention mechanism, the model utilises multiple attention heads. Each head independently learns different aspects of relationships (such as syntax or meaning). The outputs from all heads are then combined to produce richer representations. 4. Positional Encoding Introduced As there is no recurrence, positional information must be introduced manually. Positional encodings (using sine and cosine functions of varying frequencies) are added to input embeddings to maintain the order of the sequence. 5. Encoder-Decoder Structure The model is composed of two main parts: Encoder: A stack of layers that processes the input sequence. Decoder: A stack of layers that generates the output, one token at a time (whilst attending to the encoder outputs). 6. Layer Composition Each encoder and decoder layer includes: Multi-Head Self-Attention Feed-Forward Neural Network (applied to each token independently) Residual Connections and Layer Normalisation to stabilise training. 7. Scaled Dot-Product Attention Attention scores are calculated using dot products between queries and keys, scaled by the square root of the dimension to prevent excessively large values, before being passed through a softmax. 8. Simpler, Yet More Powerful Despite removing recurrence, the Transformer outperformed more complex architectures such as stacked LSTMs on translation tasks (for instance, English-German). Training is considerably quicker (thanks to parallelism), particularly on long sequences. 9. Key Achievement Transformers became the state-of-the-art model for many natural language processing tasks — paving the way for later innovations such as BERT, GPT, T5, and others. The latest breakthrough in generative AI models is owed to the development of the Transformer architecture. Transformers were introduced in the Attention is all you need paper by Vaswani, et al. from 2017.
