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Pablo Castro

CVP & Distinguished Engineer · Azure AI Search

4 activities

Farzad Sunavala

Azure AI Search

2 activities

Stuart Moncada

Head of Outbound Product Management, Cloud AI Search and Agent Builder · Google Cloud

2 activities

Activities & events

Title & Speakers	Event
Event Microsoft Ignite 2025 2025-11-21
Build Agentic Knowledge Bases: Next-Level RAG with Azure AI Search 2025-11-21 · 17:00 Ayca Bas – Senior Cloud Developer Advocate @ Microsoft , Pamela Fox – Principal Cloud Advocate @ Microsoft In this hands-on lab, you’ll build a Knowledge Base using agentic RAG, the next evolution of retrieval in Azure AI Search. Connect your agentic retrieval engine to your data through smart source selection across multiple indexes and storage systems. Learn how to enhance planning using natural language guidance and generate grounded responses with citations or extractive answers tailored to your use case. By the end, you’ll have a fully functional Knowledge Base that responds over enterprise data. Please RSVP and arrive at least 5 minutes before the start time, at which point remaining spaces are open to standby attendees. AI/ML Azure RAG
Build agents with knowledge, agentic RAG and Azure AI Search 2025-11-21 · 00:45 Matthew Gotteiner @ Microsoft , Pamela Fox – Principal Cloud Advocate @ Microsoft Start building your next agent with the latest knowledge features from Azure AI Search. In this session, we will demo how to connect your agentic retrieval engine to new knowledge sources like Sharepoint, web and blob. We will also walk through new controls available to improve your RAG performance, across query planning, retrieval and answer generation. Join this code-focused breakout for samples and step-by-step guidance on connecting knowledge to your next agent. Delivered in a silent stage breakout. AI/ML Azure RAG	Video
Build Agentic Knowledge Bases: Next-Level RAG with Azure AI Search 2025-11-20 · 22:45 Ayca Bas – Senior Cloud Developer Advocate @ Microsoft , Pamela Fox – Principal Cloud Advocate @ Microsoft In this hands-on lab, you’ll build a Knowledge Base using agentic RAG, the next evolution of retrieval in Azure AI Search. Connect your agentic retrieval engine to your data through smart source selection across multiple indexes and storage systems. Learn how to enhance planning using natural language guidance and generate grounded responses with citations or extractive answers tailored to your use case. By the end, you’ll have a fully functional Knowledge Base that responds over enterprise data. Please RSVP and arrive at least 5 minutes before the start time, at which point remaining spaces are open to standby attendees. AI/ML Azure RAG
The Missing Link in Enterprise AI with Azure AI Search 2025-11-20 · 21:00 Akanksha Malik @ IRIS by Argon and Co. Most enterprises sit on mountains of unstructured content that is hard to search and even harder to use in AI applications. In this session, join me in transforming raw content into enriched, searchable data using Azure AI Search with text extraction, entity recognition, and image analysis. Combined with vector search and Retrieval Augmented Generation (RAG) see how to build more relevant, trustworthy responses. Integrate and leverage the platforms to unlock entirely new AI-driven solutions. AI/ML Azure RAG
Build Agentic Knowledge Bases: Next-Level RAG with Azure AI Search 2025-11-19 · 23:45 Ayca Bas – Senior Cloud Developer Advocate @ Microsoft , Pamela Fox – Principal Cloud Advocate @ Microsoft In this hands-on lab, you’ll build a Knowledge Base using agentic RAG, the next evolution of retrieval in Azure AI Search. Connect your agentic retrieval engine to your data through smart source selection across multiple indexes and storage systems. Learn how to enhance planning using natural language guidance and generate grounded responses with citations or extractive answers tailored to your use case. By the end, you’ll have a fully functional Knowledge Base that responds over enterprise data. Please RSVP and arrive at least 5 minutes before the start time, at which point remaining spaces are open to standby attendees. AI/ML Azure RAG
Foundry IQ: the future of RAG with knowledge retrieval and AI Search 2025-11-19 · 18:15 Pablo Castro @ Microsoft Agents need context. How should we connect data to our agents for optimal context? In this session we will introduce Foundry IQ, the knowledge layer for agents, and the latest developments from Azure AI Search and Microsoft Foundry. Learn about multi-source RAG orchestration, retrieval steering, dynamic security controls and agentic RAG. AI/ML Azure Microsoft RAG Cyber Security	Video
Build scalable AI apps with Azure SQL Database Hyperscale 2025-11-19 · 01:00 Ravi Mantena , Anna Hoffman @ Microsoft , Ross Jenkins @ Hexagon ALI / Octave , Aditya Badramraju – Product Manager @ Microsoft , Britt Ewen @ BlackRock , Dmitry Borodin @ Hexagon Asset Lifecycle Intelligence Build AI apps that run securely and scale with your needs with Azure SQL Database Hyperscale. We’ll cover native vector indexes for semantic search, read scale out for low latency RAG, and secure model invocation with Microsoft Foundry, using the model of your choice, from T SQL. Hear directly from global technology company Hexagon and investment firm BlackRock who will join us to share their experience along with best practices, demos and more! AI/ML Azure Microsoft RAG SQL	Video
Build Agentic Knowledge Bases: Next-Level RAG with Azure AI Search 2025-11-18 · 22:45 Ayca Bas – Senior Cloud Developer Advocate @ Microsoft , Pamela Fox – Principal Cloud Advocate @ Microsoft In this hands-on lab, you’ll build a Knowledge Base using agentic RAG, the next evolution of retrieval in Azure AI Search. Connect your agentic retrieval engine to your data through smart source selection across multiple indexes and storage systems. Learn how to enhance planning using natural language guidance and generate grounded responses with citations or extractive answers tailored to your use case. By the end, you’ll have a fully functional Knowledge Base that responds over enterprise data. Please RSVP and arrive at least 5 minutes before the start time, at which point remaining spaces are open to standby attendees. AI/ML Azure RAG

Python + AI: Retrieval Augmented Generation 2025-10-09 · 17:00 In our fourth Python + AI session, we'll explore one of the most popular techniques used with LLMs: Retrieval Augmented Generation. RAG is an approach that sends context to the LLM so that it can provide well-grounded answers for a particular domain. The RAG approach can be used with many kinds of data sources like CSVs, webpages, documents, databases. In this session, we'll walk through RAG flows in Python, starting with a simple flow and culminating in a full-stack RAG application based on Azure AI Search. Pre-requisites: Habla español? Tendremos una serie para hispanohablantes!	Python + AI: Retrieval Augmented Generation
Python + AI: Retrieval Augmented Generation 2025-10-09 · 17:00 In our fourth Python + AI session, we'll explore one of the most popular techniques used with LLMs: Retrieval Augmented Generation. RAG is an approach that sends context to the LLM so that it can provide well-grounded answers for a particular domain. The RAG approach can be used with many kinds of data sources like CSVs, webpages, documents, databases. In this session, we'll walk through RAG flows in Python, starting with a simple flow and culminating in a full-stack RAG application based on Azure AI Search. Pre-requisites: Habla español? Tendremos una serie para hispanohablantes!	Python + AI: Retrieval Augmented Generation
Python + AI: Retrieval Augmented Generation 2025-10-09 · 17:00 In our fourth Python + AI session, we'll explore one of the most popular techniques used with LLMs: Retrieval Augmented Generation. RAG is an approach that sends context to the LLM so that it can provide well-grounded answers for a particular domain. The RAG approach can be used with many kinds of data sources like CSVs, webpages, documents, databases. In this session, we'll walk through RAG flows in Python, starting with a simple flow and culminating in a full-stack RAG application based on Azure AI Search. Pre-requisites: Habla español? Tendremos una serie para hispanohablantes!	Python + AI: Retrieval Augmented Generation
Generative Feedback Loops w Neon serverless Postgres, Azure Functions, & Azure O 2025-05-08 · 16:00 As vector search and RAG become mainstream for GenAI use cases, we’re looking ahead to what’s next. Generative Feedback Loops (GFL) represent the next evolution in AI where the AI outputs are improved over time through a cycle of feedback and learning based on the production data. In this talk, we’ll break down how this works, and you will learn how to build a sample AI app with feedback loops using Neon serverless Postgres, Azure Functions, and Azure OpenAI.	Generative Feedback Loops w Neon serverless Postgres, Azure Functions, & Azure O
Multi-source data patterns for modern RAG apps 2025-04-23 · 22:00 This session explores patterns for modern RAG applications to bring together diverse data sources such as Blob Storage, SQL, Cosmos DB, and PostgreSQL. We will work through three primary patterns: indexing all data to conform to same schema (with Azure AI Search), consolidating into a single store (with Fabric), and finally, using multi-agent orchestrations. Follow along: - Create agentic AI solutions by using Azure AI Foundry 📌 Learn more about the series here Pre-requisites: * Join the Hackathon * Learning Resources	Multi-source data patterns for modern RAG apps
Build custom agent with vectorize text in Azure AI Search 2025-04-23 · 12:30 Explore how to enhance your AI agents by integrating them with Azure AI Search. This session will guide you through the process of vectorizing text data, enabling your agents to retrieve and deliver more accurate and contextually relevant information. Whether you're new to AI development or looking to deepen your expertise, you'll gain insights into leveraging Azure's advanced search capabilities to build more intelligent and responsive agents Suggest Microsoft Learn Modules: Build a RAG-based agent with your own data using Azure AI Foundry Configure a vectorizer in a search index	Build custom agent with vectorize text in Azure AI Search
Build custom agent with vectorize text in Azure AI Search 2025-04-23 · 12:30 Explore how to enhance your AI agents by integrating them with Azure AI Search. This session will guide you through the process of vectorizing text data, enabling your agents to retrieve and deliver more accurate and contextually relevant information. Whether you're new to AI development or looking to deepen your expertise, you'll gain insights into leveraging Azure's advanced search capabilities to build more intelligent and responsive agents Suggest Microsoft Learn Modules: Build a RAG-based agent with your own data using Azure AI Foundry Configure a vectorizer in a search index	Build custom agent with vectorize text in Azure AI Search
AI That Talks Like Us 2025-04-22 · 17:00 In this session, internationally acclaimed speaker Chander Dhall will demonstrate how Azure OpenAI and Azure Cosmos DB for NoSQL (and also MongoDB), empowers AI to sound just like a human, enabling natural, conversational interactions with your data. Imagine chatting with an AI that doesn’t just retrieve facts but speaks as if it were human, seamlessly responding in real time. Chander will showcase how these technologies work together to deliver cutting-edge conversational AI, turning complex data into accessible insights. You’ll see live examples of AI transforming how we interact with data—using voice-based Retrieval-Augmented Generation (RAG) to enhance performance, multilingual capabilities to connect globally, and Azure Cosmos DB for MongoDB to deliver real-time, scalable solutions. AI will answer in multiple languages, including Japanese, Mandarin, Hindi, French, and Arabic, demonstrating its ability to break language barriers effortlessly. You will learn: - How to use Fabric, Azure OpenAI, and Azure Cosmos DB, DiskANN, full text and hybrid search to create AI that sounds human. - How to enable conversational access to your data using advanced voice RAG patterns. - How to implement scalable, multilingual AI solutions to meet global demands. Don’t miss this chance to explore the future of AI-powered, human-like conversations and discover practical tools to revolutionize your data interactions!	AI That Talks Like Us
Mastering Agentic RAG 2025-04-16 · 22:00 Retrieval Augmented Generation (RAG) is a popular technique to get LLMs to answer questions based off your own knowledge base. "Agentic RAG" adds planning, tool use, and reflection to a RAG flow. In this session, we'll demonstrate an Agentic RAG workflow using OpenAI Function Calling with NL2SQL for structured data, Azure AI Search for unstructured data, and Bing Search API for live web search. 📌 Learn more about the series here Follow along: - Create agentic AI solutions by using Azure AI Foundry - Practical Foundation for AI Agents: A Developer's Guide on Azure AI Foundry, Apps, and Data 📌 Learn more about the series here Pre-requisites: * Join the Hackathon * Learning Resources	Mastering Agentic RAG
Multi-Agent API with LangGraph and Azure Cosmos DB 2025-04-16 · 19:00 The rise of multi-agent AI applications is transforming how we build intelligent systems - but how do you architect them for real-world scalability and performance? In this session, we’ll take a deep dive into a production-grade multi-agent application built with LangGraph for agent orchestration, FastAPI for an API layer, and Azure Cosmos DB as the backbone for state management, vector storage, and transactional data. Through a detailed code walkthrough, you’ll see how to design and implement an agent-driven workflow that seamlessly integrates retrieval-augmented generation (RAG), memory persistence, and dynamic state transitions. We’ll cover: Agent collaboration with LangGraph for structured reasoning Real-time chat history storage using Azure Cosmos DB - the same database that powers the chat history in ChatGPT, the fastest-growing AI agent application in history Vector search for knowledge retrieval with Cosmos DB's native embeddings support FastAPI’s async capabilities to keep interactions responsive and scalable By the end of this session, you’ll have a clear blueprint for building and deploying your own scalable, cloud-native multi-agent applications that harness the power of modern AI and cloud infrastructure. Whether you're an AI engineer, cloud architect, or Python developer, this talk will equip you with practical insights and battle-tested patterns to build the next generation of AI-powered applications 📌 Learn more about the series here Pre-requisites: * Join the Hackathon * Learning Resources	Multi-Agent API with LangGraph and Azure Cosmos DB
Multi-Agent API with LangGraph and Azure Cosmos DB 2025-04-16 · 19:00 The rise of multi-agent AI applications is transforming how we build intelligent systems - but how do you architect them for real-world scalability and performance? In this session, we’ll take a deep dive into a production-grade multi-agent application built with LangGraph for agent orchestration, FastAPI for an API layer, and Azure Cosmos DB as the backbone for state management, vector storage, and transactional data. Through a detailed code walkthrough, you’ll see how to design and implement an agent-driven workflow that seamlessly integrates retrieval-augmented generation (RAG), memory persistence, and dynamic state transitions. We’ll cover: Agent collaboration with LangGraph for structured reasoning Real-time chat history storage using Azure Cosmos DB - the same database that powers the chat history in ChatGPT, the fastest-growing AI agent application in history Vector search for knowledge retrieval with Cosmos DB's native embeddings support FastAPI’s async capabilities to keep interactions responsive and scalable By the end of this session, you’ll have a clear blueprint for building and deploying your own scalable, cloud-native multi-agent applications that harness the power of modern AI and cloud infrastructure. Whether you're an AI engineer, cloud architect, or Python developer, this talk will equip you with practical insights and battle-tested patterns to build the next generation of AI-powered applications 📌 Learn more about the series here Pre-requisites: * Join the Hackathon * Learning Resources	Multi-Agent API with LangGraph and Azure Cosmos DB
Multi-Agent API with LangGraph and Azure Cosmos DB 2025-04-16 · 19:00 The rise of multi-agent AI applications is transforming how we build intelligent systems - but how do you architect them for real-world scalability and performance? In this session, we’ll take a deep dive into a production-grade multi-agent application built with LangGraph for agent orchestration, FastAPI for an API layer, and Azure Cosmos DB as the backbone for state management, vector storage, and transactional data. Through a detailed code walkthrough, you’ll see how to design and implement an agent-driven workflow that seamlessly integrates retrieval-augmented generation (RAG), memory persistence, and dynamic state transitions. We’ll cover: Agent collaboration with LangGraph for structured reasoning Real-time chat history storage using Azure Cosmos DB - the same database that powers the chat history in ChatGPT, the fastest-growing AI agent application in history Vector search for knowledge retrieval with Cosmos DB's native embeddings support FastAPI’s async capabilities to keep interactions responsive and scalable By the end of this session, you’ll have a clear blueprint for building and deploying your own scalable, cloud-native multi-agent applications that harness the power of modern AI and cloud infrastructure. Whether you're an AI engineer, cloud architect, or Python developer, this talk will equip you with practical insights and battle-tested patterns to build the next generation of AI-powered applications 📌 Learn more about the series here Pre-requisites: * Join the Hackathon * Learning Resources	Multi-Agent API with LangGraph and Azure Cosmos DB