Tag:"ai agents" | Microsoft Community Hub

Webinar Series for Microsoft AI Agents
Join us for an exciting and insightful webinar series where we delve into the revolutionary world of Microsoft Copilot Agents in SharePoint, Agent builder, Copilot Studio and Azure AI Foundry! Discover how the integration of AI and intelligent agents is set to transform the future of business processes, making them more efficient, intelligent, and adaptive. In this webinar series, we will explore: The Power of Microsoft Copilot Agents: Learn how these advanced AI-driven agents can assist you in automating routine tasks, providing intelligent insights, and enhancing collaboration within your organization. Seamless Integration with Microsoft Graph: See how Copilot Agents work seamlessly with Microsoft Graph data to improve information retrieval, boost productivity, and automate mundane tasks. Real-World Applications: See real-world examples of how businesses are leveraging Copilot Agents to drive innovation and achieve their goals. Future Trends and Innovations: Get a glimpse into the future of AI in business processes and how it will continue to evolve and shape the way we work. Join us for the Webinars every week, at 11:30am PST/1:30pm CST/2:30 EST: (Click on the webinar name to join the live meeting on the actual date/time or use the .ics file at the bottom of the page to save the date on your calendar) April 2nd: Agents with SharePoint - Watch this Webinar recording for an overview of SharePoint Agents and its key capabilities to enable your organization with powerful Agents helping you search for information within seconds in large SharePoint libraries with 100's of documents. April 9th: Agents with Agent Builder - Watch this Webinar recording for an overview of Agent Builder and its key capabilities to enable organization with "No code" Agents that can be created by any business user within minutes. April 16th: Agents with Copilot Studio- Join us for an overview of Copilot Studio and its key capabilities to enable organization with "Low code" Agents that can help create efficiency with existing business processes. We will feature a few real-life demo examples and answer any questions. April 24th: Agents with Azure AI Foundry - Join us for an overview of Azure AI Foundry and its key capabilities to enable your organization with AI Agents. We will feature a demo of AI agents for prior authorization and provide resources to accelerate your next project. Don't miss this opportunity to stay ahead of the curve and unlock the full potential of AI and Copilot Agents in your organization. Register now and be part of the future of business transformation! Speakers: Jaspreet Dhamija, Sr. MW Copilot Specialist - Linkedin Michael Gannotti, Principal MW Copilot Specialist - LinkedIn Melissa Nelli, Sr. Biz Apps Technical Specialist - LinkedIn Matthew Anderson, Director Azure Apps - LinkedIn Marcin Jimenez, Sr. Cloud Solution Architect - LinkedIn Thank you!
JaspreetDhamija
Apr 24, 2025 Place Healthcare and Life Sciences Blog
2.9KViews
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Integrate Custom Azure AI Agents with CoPilot Studio and M365 CoPilot
Integrating Custom Agents with Copilot Studio and M365 Copilot In today's fast-paced digital world, integrating custom agents with Copilot Studio and M365 Copilot can significantly enhance your company's digital presence and extend your CoPilot platform to your enterprise applications and data. This blog will guide you through the integration steps of bringing your custom Azure AI Agent Service within an Azure Function App, into a Copilot Studio solution and publishing it to M365 and Teams Applications. When Might This Be Necessary: Integrating custom agents with Copilot Studio and M365 Copilot is necessary when you want to extend customization to automate tasks, streamline processes, and provide better user experience for your end-users. This integration is particularly useful for organizations looking to streamline their AI Platform, extend out-of-the-box functionality, and leverage existing enterprise data and applications to optimize their operations. Custom agents built on Azure allow you to achieve greater customization and flexibility than using Copilot Studio agents alone. What You Will Need: To get started, you will need the following: Azure AI Foundry Azure OpenAI Service Copilot Studio Developer License Microsoft Teams Enterprise License M365 Copilot License Steps to Integrate Custom Agents: Create a Project in Azure AI Foundry: Navigate to Azure AI Foundry and create a project. Select 'Agents' from the 'Build and Customize' menu pane on the left side of the screen and click the blue button to create a new agent. Customize Your Agent: Your agent will automatically be assigned an Agent ID. Give your agent a name and assign the model your agent will use. Customize your agent with instructions: Add your knowledge source: You can connect to Azure AI Search, load files directly to your agent, link to Microsoft Fabric, or connect to third-party sources like Tripadvisor. In our example, we are only testing the CoPilot integration steps of the AI Agent, so we did not build out additional options of providing grounding knowledge or function calling here. Test Your Agent: Once you have created your agent, test it in the playground. If you are happy with it, you are ready to call the agent in an Azure Function. Create and Publish an Azure Function: Use the sample function code from the GitHub repository to call the Azure AI Project and Agent. Publish your Azure Function to make it available for integration. azure-ai-foundry-agent/function_app.py at main · azure-data-ai-hub/azure-ai-foundry-agent Connect your AI Agent to your Function: update the "AIProjectConnString" value to include your Project connection string from the project overview page of in the AI Foundry. Role Based Access Controls: We have to add a role for the function app on OpenAI service. Role-based access control for Azure OpenAI - Azure AI services | Microsoft Learn Enable Managed Identity on the Function App Grant "Cognitive Services OpenAI Contributor" role to the System-assigned managed identity to the Function App in the Azure OpenAI resource Grant "Azure AI Developer" role to the System-assigned managed identity for your Function App in the Azure AI Project resource from the AI Foundry Build a Flow in Power Platform: Move into the Power Platform (https://make.powerapps.com) to build out a flow that connects your Copilot Studio solution to your Azure Function App. When creating a new flow, select 'Build an instant cloud flow' and trigger the flow using 'Run a flow from Copilot'. Add an HTTP action to call the Function using the URL and pass the message prompt from the end user with your URL. The output of your function is plain text, so you can pass the response from your Azure AI Agent directly to your Copilot Studio solution. Create Your Copilot Studio Agent: Navigate to Microsoft Copilot Studio and select 'Agents', then 'New Agent'. Now select ‘Create’ button at the top of the screen From the top menu, navigate to ‘Topics’ and ‘System’. We will open up the ‘Conversation boosting’ topic. When you first open the Conversation boosting topic, you will see a template of connected nodes. Delete all but the initial ‘Trigger’ node. Now we will rebuild the conversation boosting agent to call the Flow you built in the previous step. Select 'Add an Action' and then select the option for existing Power Automate flow. Pass the response from your Custom Agent to the end user and end the current topic. My existing Cloud Flow: Add action to connect to existing Cloud Flow: When this menu pops up, you should see the option to Run the flow you created. Here, mine does not have a very unique name, but you see my flow 'Run a flow from Copilot' as a Basic action menu item. Now complete building out the conversation boosting topic: Make Agent Available in M365 Copilot: Navigate to the 'Channels' menu and select 'Teams + Microsoft 365'. Be sure to select the box to 'Make agent available in M365 Copilot'. Save and re-publish your Copilot Agent. It may take up to 24 hours for the Copilot Agent to appear in M365 Teams agents list. Once it has loaded, select the 'Get Agents' option from the side menu of Copilot and pin your Copilot Studio Agent to your featured agent list Now, you can chat with your custom Azure AI Agent, directly from M365 Copilot! Conclusion: By following these steps, you can successfully integrate custom Azure AI Agents with Copilot Studio and M365 Copilot, enhancing you’re the utility of your existing platform and improving operational efficiency. This integration allows you to automate tasks, streamline processes, and provide better user experience for your end-users. Give it a try! Curious of how to bring custom models from your AI Foundry to your CoPilot Studio solutions? Check out this blog
hannahabbott
Apr 24, 2025 Place AI - AI Platform Blog
1.6KViews
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AI Agents: The Multi-Agent Design Pattern - Part 8
This blog post, Part 8 in a series on AI agents, explores the Multi-Agent Design Pattern, outlining the benefits and key components of building systems with multiple interacting agents. It details the scenarios where multi-agent systems excel (large workloads, complex tasks, diverse expertise), highlights their advantages over single-agent approaches (specialization, scalability, fault tolerance), and discusses the fundamental building blocks for implementation, including agent communication, coordination mechanisms, and architectural considerations. The post introduces common multi-agent patterns (group chat, hand-off, collaborative filtering) and illustrates these concepts with a refund process example. Finally, it includes a practical assignment and provides links to further resources and previous posts in the series.
ShivamGoyal03
Apr 21, 2025 Place Educator Developer Blog
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Unlock Multi-Modal Embed 4 and Multilingual Agentic RAG with Command A on Azure
Developers and enterprises now have immediate access to state-of-the-art generative and semantic models purpose-built for RAG (Retrieval-Augmented Generation) and agentic AI workflows on Azure AI Foundry to: Deploy high-performance LLMs and semantic search engines directly into production Build faster, more scalable, and multilingual RAG pipelines Leverage models that are optimized for enterprise workloads in finance, healthcare, government, and manufacturing Cohere Embed 4: High-Performance Embeddings for Search & RAG Accompanying Command A is Cohere’s Embed 4, a cutting-edge embedding model ideal for retrieval-augmented generation pipelines and semantic search. Embed 4 (the latest evolution of Cohere’s Embed series) converts text – and even images – into high-dimensional vector representations that capture semantic meaning. It’s a multi-modal, multilingual embedding model designed to provide recall and relevance in vector search, text classification, and clustering tasks. What makes Embed 4 stand out? 100+ Language Support: This model is truly global – it supports well over 100 languages for text embeddings. You can encode queries and documents in many languages (Arabic, Chinese, French, Hindi, etc.) into the same vector space, enabling cross-lingual search out of the box. For example, a question in Spanish can retrieve a relevant document originally in English if their ideas align semantically. Multi-Modal Embeddings: Embed 4 is capable of embedding not only text but also images. This means you can use it for multimodal search scenarios – e.g. indexing both textual content and images and allowing queries across them. Under the hood, the model has an image encoder; the Azure AI Foundry SDK provides an ImageEmbeddingsClient to generate embeddings from images. With this, you could embed a diagram or a screenshot and find text documents that are semantically related to that image’s content. Matryoshka Embeddings (Scalable Dimensions): A novel feature in Cohere’s Embed 4 is Matryoshka Representation Learning, which produces embeddings that can be truncated to smaller sizes with minimal loss in fidelity. In practice, the model can output a high-dimensional vector (e.g. 768 or 1024 dims) but you have the flexibility to use just the first 64, 128, 256, etc. dimensions if needed. These “nested” embeddings mean you can choose a vector size that balances accuracy vs. storage/query speed – smaller vectors save memory and compute while still preserving most of the semantic signal. This is great for enterprise deployments where vector database size and latency are critical. Enterprise Optimizations: Cohere has optimized Embed 4 for production use. It supports int8 quantization and binary embedding output natively, which can drastically reduce storage footprint and speed up similarity search with only minor impact on accuracy (useful for very large indexes). The model is also trained on massive datasets (including domain-specific data) to ensure robust performance on noisy enterprise text. It achieves state-of-the-art results on benchmark evaluations like MTEB, meaning you get retrieval quality on par with or better than other leading embeddings models (OpenAI, Google, etc.). For instance, Cohere’s previous embed model was top-tier on cross-language retrieval tasks and Embed4 further improves on that foundation. Cohere Command A: Generative Model for Enterprise AI Command A is Cohere’s latest flagship large language model, designed for high-performance text generation in demanding enterprise scenarios. It’s an instruction-tuned, conversational LLM that excels at complex tasks like multi-step reasoning, tool use (function calling), and retrieval-augmented generation. Command A features a massive 111B parameter Transformer architecture with 256K token context length – enabling it to handle extremely large inputs (hundreds of pages of text) in a single prompt without losing coherence. Source for above benchmarks : Introducing Command A: Max performance, minimal compute Some key capabilities of Command A include: Long Context (256K tokens): Using an innovative attention architecture (sliding window + global attention), Command A can ingest up to 256,000 tokens of text in one go. This enables use cases like analyzing lengthy financial reports or entire knowledge bases in a single prompt. Enterprise-Tuned Generation: Command A is optimized for business applications – it’s excellent at instructions, summarization, and especially RAG workflows where it integrates retrieved context and even cites sources to mitigate hallucinations. It supports tool calling (function calling) out-of-the-box so it can interact with external APIs or data sources as part of an Azure AI Agent. Multilingual Proficiency: Command A is good at multilingual use cases (covering all major business languages, with near leading performance in Japanese, Korean, and German). Efficient Deployment: Despite its size, Command A is engineered for efficiency – it delivers 150% higher throughput than its predecessor (Command R+ 08-2024) and requires only 2× A100/H100 GPUs to run. In practice this means lower latency. It also supports streaming token output, so applications can start receiving the response as it’s generated, keeping interaction latency low. Real-World Use Cases for Command A + Embed 4 With both a powerful generative model and a state-of-the-art embedding model at your fingertips, developers can build advanced AI solutions. Here are some real-world use cases unlocked by Command A and Embed 4 on Azure: Financial Report Summarization (RAG): Imagine ingesting thousands of pages of financial filings, earnings call transcripts, and market research into a vector store. Using Embed 4, you can embed and index all this text. When an analyst asks “What were the key revenue drivers mentioned in ACME Corp’s Q1 2025 report?”, you use the query embedding to retrieve the most relevant passages. Command A (with its 256K context) can then take those passages and generate a concise summary or answer with cited evidence. The model’s long context window means it can consider all retrieved chunks at once, and its enterprise tuning ensures factual, business-appropriate summaries. Legal Research Agent (Tool Use + Multilingual): For example a multinational law firm handling cross-border mergers and acquisitions. They have a vast repository of legal documents in multiple languages. Using Embed 4, they index these documents, creating multilingual embeddings. When a lawyer researches a specific legal precedent related to a merger in Germany, they can query in English. Embed 4 retrieves relevant German documents, and Command A summarizes key points, translates excerpts, and compares legal arguments across jurisdictions. Furthermore, Command A leverages tool calling (utilizing agentic capabilities) to retrieve additional information from external databases, such as company registration details and regulatory filings, integrating this data into its analysis to provide a comprehensive report. Technician Knowledge Assistant (RAG + Multilingual): Think of a utilities company committed to operational excellence, managing a vast network of critical equipment, including power generators, transformers, and distribution lines. They can leverage Command A, integrated with Embed 4, to index a comprehensive repository of equipment manuals, maintenance records, and sensor data in multiple languages. This enables technicians and engineers to access critical knowledge instantly. Technicians can ask questions in their native language about specific equipment issues, and Command A retrieves relevant manuals, troubleshooting guides, and past repair reports. It also guides technicians through complex maintenance procedures step-by-step, ensuring consistency and adherence to best practices. This empowers the company to optimize maintenance processes, improve overall equipment reliability, and enhance communication, ultimately achieving operational excellence. Multimodal Search & Indexing: With Embed 4’s image embedding capability, you can build search systems that go beyond text. For instance, a media company could index their image library by generating embeddings for each image (using Azure’s Image Embeddings client) and also index captions/descriptions. A user could then supply a query image (or a textual description) and retrieve both images and articles that are semantically similar to the query. This is useful for scenarios like finding slides similar to a given diagram, searching scanned invoices by content, or matching user-uploaded photos to reference documents. Getting Started: Deploying via Azure AI Foundry In Azure AI Foundry, Embed 4 can be used via the Embeddings API to encode text or images into vectors. Each text input is turned into a numeric vector (e.g. 1024-dimension float array) that you can store in a vector database or use for similarity comparisons. The embeddings are normalized for cosine similarity by default. You can also take advantage of Azure’s vector index or Azure Cognitive Search to directly build vector search on top of these model outputs. Image Source : Introducing Embed 4: Multimodal search for business One of the biggest benefits of using Azure AI Foundry is the ease of deployment for these models. Cohere’s Command A and Embed 4 are available in the model catalog – you can find their model cards and deploy them in just a few clicks. Azure Foundry supports serverless API endpoints for these models, meaning Microsoft hosts the inference infrastructure and scales it for you (with pay-as-you-go billing). Integration with Azure AI Agent Service: If you’re building an AI agent (a system that can orchestrate models and tools to perform tasks), Azure AI Agent Service makes it easy to incorporate these models. In the Agent Service, you can simply reference the deployed model by name as the agent’s reasoning LLM. For example, you could specify an agent that uses CohereCommandA as its model, and add tools like Azure Cognitive Search. The agent can then handle user requests by, say, using a Search tool (powered by Embed 4 vector index) and then passing the results to Command A for answer formulation – all managed by the Azure Agent framework. This lets you build production-grade agentic AI workflows that leverage Cohere’s models with minimal plumbing. In short, Azure provides the glue to connect Command A + Embed 4 + Tools into a coherent solution. Try Command A and Embed 4 today on Azure AI Foundry The availability of Cohere’s Command A and Embed 4 on Azure AI Foundry empowers developers to build the next generation of intelligent apps on a fully managed platform. You can now easily deploy a 256K-context LLM that rivals the best in the industry, alongside a high-performance embedding model that plugs into your search and retrieval pipelines. Whether it’s summarizing lengthy documents with cited facts, powering a multilingual enterprise assistant, enabling multimodal search experiences, or orchestrating complex tool-using agents – these models open up a world of possibilities. Azure AI Foundry makes it simple to integrate these capabilities into your solutions, with the security, compliance, and scalability of Azure’s cloud. We encourage you to try out Command A and Embed 4 in your own projects. Spin them up from the Azure model catalog, use the provided SDK examples to get started, and explore how they can elevate your applications’ intelligence. With Cohere’s models on Azure, you have cutting-edge AI at your fingertips, ready to deploy in production. We’re excited to see what you build with them!
Naomi Moneypenny
Apr 15, 2025 Place AI - AI Platform Blog
1.3KViews
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AI Agents: Planning and Orchestration with the Planning Design Pattern - Part 7
This blog post, Part 7 in a series on AI agents, focuses on the Planning Design Pattern for effective task orchestration. It explains how to define clear goals, decompose complex tasks into manageable subtasks, and leverage structured output (e.g., JSON) for seamless communication between agents. The post includes code snippets demonstrating how to create a planning agent, orchestrate multi-agent workflows, and implement iterative planning for dynamic adaptation. It also links to a practical example notebook (07-autogen.ipynb) and further resources like AutoGen Magnetic One, encouraging readers to explore advanced planning concepts. Links to the previous posts in the series are provided for easy access to foundational AI agent concepts.
ShivamGoyal03
Apr 14, 2025 Place Educator Developer Blog
324Views
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March 2025 Recap: Azure Database for PostgreSQL Flexible Server
March 2025 Feature Recap: Azure PostgreSQL Flexible Server Updates - New Features and Enhancements
varun-dhawan
Apr 10, 2025 Place Azure Database for PostgreSQL Blog
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AI Avatars: Redefining Human-Digital Interaction in the Enterprise Era
In today’s AI-driven world, businesses are constantly seeking innovative ways to humanize digital experiences. AI Avatars are emerging as a powerful solution—bridging the gap between intelligent automation and authentic, human-like engagement. With advancements in speech synthesis, large language models, and avatar rendering technologies, organizations can now deploy AI-powered digital assistants that not only understand and respond but also interact with a lifelike presence. The Rise of AI Avatars in Enterprise Applications AI Avatars go beyond traditional chatbots or voice assistants. These virtual beings offer multimodal interaction—combining voice, visual cues, and conversational intelligence into a seamless user experience. Built on enterprise-grade platforms like Azure AI, these avatars can be integrated into customer support portals, digital kiosks, internal knowledge hubs, and more. Their utility spans a range of industries: Retail: Personalized shopping assistants that guide consumers through products. Healthcare: Virtual health concierges that help patients navigate care. Education: Interactive tutors that deliver lessons with empathy and responsiveness. HR and Training: Onboarding avatars that answer employee questions, onboard new hires, or provide compliance updates. One of our key partners, Cloudforce, has integrated AI Avatar technology directly into their flagship platform nebulaONE®. This integration enables enterprises to deploy digital assistants that are deeply embedded in business processes, offering contextualized support and real-time engagement. From training and onboarding to employee self-service, nebulaONE's agentic AI Avatars act as a digital bridge between users and systems—driving efficiency, engagement, and satisfaction. Partner Spotlight: Cloudforce’s Avatar Initiative To operationalize and productize AI Avatars, Microsoft collaborates with a growing ecosystem of partners. Cloudforce is one of the early pioneers in this space. Their work in embedding avatars into nebulaONE demonstrates what’s possible when advanced AI meets real-world enterprise needs. With a vision to transform user interaction across industries, Cloudforce built a production-grade AI Avatar module designed to support customer Q&A, knowledge discovery, and live guided walkthroughs. Leveraging Azure OpenAI, Azure AI Speech, and privately-deployed secure cloud infrastructure, they have brought conversational intelligence to life—with both a face and a voice. Looking ahead, Cloudforce’s broader vision is to bring AI Avatar capabilities to millions of students—delivering immersive learning experiences that blend interactivity, personalization, and scale. Their education-focused roadmap enhancements highlight the potential of avatars not just as productivity agents, but as accessible and empathetic digital educators, delivering equitable access to knowledge previously reserved for a fortunate few. This kind of partner innovation illustrates how AI Avatars can be customized and scaled to deliver tangible business value across multiple domains. Partner Contribution "Students are already embracing generative AI at a pace and proficiency that far exceeds many professional audiences. With Azure's AI Avatar technology, educators and institutions can tailor unique GenAI interactions that promote reasoning and learning over simply receiving answers the way they would with common public bots." says Husein Sharaf, Founder and CEO at Cloudforce. "We understand the concerns and hesitation that our education partners are currently grappling with, however we believe they can and should take an active role in shaping how this transformative technology is leveraged across their campuses, or risk being left behind as students choose their own adventure." "Microsoft's enterprise AI capabilities are enabling partners like us to deliver secure, cost-efficient, and responsible AI experiences at scale. With the Azure AI Foundry and key innovations like AI Avatars as our building blocks, the nebulaONE platform is poised to serve as the GenAI gateway to tens of thousands of business users, and millions of students at leading educational institutions globally. Our customers are seeking unique differentiators that will enable them to compete and win in the age of AI, and our collaboration with Microsoft is empowering us to deliver just that." Summary AI Avatars represent the next frontier in digital interaction. By combining conversational AI, expressive voice synthesis, and realistic visual rendering, these intelligent agents deliver truly human-like experiences—at scale. They are not just tools, but digital extensions of your brand. Partners like Cloudforce are leading the way with innovative platforms like nebulaONE, showing how this technology can be embedded into enterprise solutions and educational experiences to drive efficiency with a human touch. While Cloudforce is among the first to productize AI Avatars using Azure AI, they are part of a growing movement—helping to shape the future of AI-powered experiences across industries. As AI continues to evolve, avatars will become a standard interface—transforming the way we learn, work, and engage with digital systems.
Gana_Chandrasekaran
Apr 08, 2025 Place AI - AI Platform Blog
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The Future of AI: Computer Use Agents Have Arrived
Discover the groundbreaking advancements in AI with Computer Use Agents (CUAs). In this blog, Marco Casalaina shares how to use the Responses API from Azure OpenAI Service, showcasing how CUAs can launch apps, navigate websites, and reason through tasks. Learn how CUAs utilize multimodal models for computer vision and AI frameworks to enhance automation. Explore the differences between CUAs and traditional Robotic Process Automation (RPA), and understand how CUAs can complement RPA systems. Dive into the future of automation and see how CUAs are set to revolutionize the way we interact with technology.
Marco_Casalaina
Apr 07, 2025 Place AI - AI Platform Blog
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AI Agents: Building Trustworthy Agents- Part 6
This blog post, Part 6 in a series on AI agents, focuses on building trustworthy AI agents. It emphasizes the importance of safety and security in agent design and deployment. The post details a system message framework for creating robust and scalable prompts, outlining a four-step process from meta prompt to iterative refinement. It then explores various threats to AI agents, including task manipulation, unauthorized access, resource overloading, knowledge base poisoning, and cascading errors, providing mitigation strategies for each. The post also highlights the human-in-the-loop approach for enhanced trust and control, providing a code example using AutoGen. Finally, it links to further resources on responsible AI, model evaluation, and risk assessment, along with the previous posts in the series.
ShivamGoyal03
Apr 07, 2025 Place Educator Developer Blog
304Views
3likes
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The Future of AI: Harnessing AI for E-commerce - personalized shopping agents
Explore the development of personalized shopping agents that enhance user experience by providing tailored product recommendations based on uploaded images. Leveraging Azure AI Foundry, these agents analyze images for apparel recognition and generate intelligent product recommendations, creating a seamless and intuitive shopping experience for retail customers.
manniarora
Apr 01, 2025 Place AI - AI Platform Blog
681Views
5likes
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