Unlock the potential of intelligent autonomous agents that combine real-time streaming data with Retrieval Augmented Generation (RAG) for dynamic decision-making. You will learn how to use streaming technologies like Amazon Kinesis, Amazon MSK, and Managed Service for Apache Flink create a robust pipeline to transform raw events into actionable insights. This session will show you how autonomous agents leverage these real-time insights with RAG architecture powered by OpenSearch, enabling immediate, context-aware responses to changing conditions. This practical architecture drives real-world value in critical scenarios like predictive maintenance, automated incident response, and intelligent customer service automation, with improved accuracy and reduced latency.

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ABOUT AWS: Amazon Web Services (AWS) hosts events, both online and in-person, bringing the cloud computing community together to connect, collaborate, and learn from AWS experts. AWS is the world's most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster.

AWSreInvent #AWSreInvent2025 #AWS

This talk explores AI agents as the next step beyond prompt‑by‑prompt assistants. Modern AI agents use large language models plus planning, tool‑calling, and memory to execute multi‑step workflows, not just answer isolated questions. The session explains, in accessible terms, what makes something an “agent” rather than a simple chatbot: the ability to decompose tasks, call APIs or tools, and maintain context over time. It then surveys real use cases, from automating repetitive knowledge‑work tasks to orchestrating complex enterprise workflows that blend human decisions with autonomous actions. For the technical audience, the talk briefly outlines typical agent architectures and how they integrate with RAG, vector search, and existing backend services. For everyone else, it focuses on capabilities, limitations, and where AI agents are realistically being used in 2025. Attendees will understand what AI agents can and cannot do today, and how they differ from the hype.

Learn to build a production-grade agentic RAG system using Amazon Bedrock Knowledge Bases, Strands, and AgentCore in this expert-level code talk. Through live coding and detailed walkthroughs, learn how to build an intelligent event assistant agent that integrates knowledge retrieval, long-term memory, and user authentication. This hands-on session covers the complete journey from knowledge base setup through agent creation, memory integration (short-term and long-term), runtime deployment, and identity management. Prerequisites: strong experience with Python and familiarity with RAG concepts.

Learn more: More AWS events: https://go.aws/3kss9CP

Subscribe: More AWS videos: http://bit.ly/2O3zS75 More AWS events videos: http://bit.ly/316g9t4

ABOUT AWS: Amazon Web Services (AWS) hosts events, both online and in-person, bringing the cloud computing community together to connect, collaborate, and learn from AWS experts. AWS is the world's most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster.

AWSreInvent #AWSreInvent2025 #AWS

A carreira de AI Engineer se consolidou como uma das mais disputadas do mercado de tecnologia. Mas afinal, o que realmente é esperado desse profissional na prática? Neste episódio do Data Hackers, discutimos em profundidade o caminho para se tornar um AI Engineer, analisando as principais habilidades técnicas, as diferenças em relação a outros cargos da área de dados e engenharia, a formação acadêmica versus experiência prática, a rotina nas empresas e o impacto da IA Generativa, RAG e AI Agents no dia a dia da função. Para enriquecer o debate, utilizamos dados da pesquisa State of Data Brazil como base para entender o cenário atual do mercado brasileiro, identificar tendências de demanda por habilidades, perfis profissionais mais buscados e os principais desafios enfrentados por quem deseja ingressar ou evoluir nessa carreira. Se você quer migrar para IA, se preparar para oportunidades reais ou entender se esse é o próximo passo profissional em dados, este episódio é para você. Não se esqueça de preencher a pesquisa State of Data Brazil: https://www.stateofdata.com.br/

Nossa Bancada Data Hackers: Paulo Vasconcellos — Co-founder da Data Hackers e Principal Data Scientist na Hotmart.Gabriel Lages — Co-founder da Data Hacker e Diretor de Dados & AI da Hotmart

Unstructured data often holds untapped value, and Amazon SageMaker makes it possible to turn that data into insights and AI-ready assets. In this session, you'll learn how to bring unstructured data from Amazon S3 into SageMaker, create searchable assets, and build knowledge bases for Amazon Bedrock to improve retrieval-augmented generation (RAG) accuracy. Discover how teams can collaborate across roles, data users can self-serve to find and understand the right data, and governance ensures that the right people get the right access. Bayer will share how they use these capabilities to unlock unstructured data and accelerate research and innovation.

Learn more: More AWS events: https://go.aws/3kss9CP

Subscribe: More AWS videos: http://bit.ly/2O3zS75 More AWS events videos: http://bit.ly/316g9t4

ABOUT AWS: Amazon Web Services (AWS) hosts events, both online and in-person, bringing the cloud computing community together to connect, collaborate, and learn from AWS experts. AWS is the world's most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster.

AWSreInvent #AWSreInvent2025 #AWS

Comment interroger la base de connaissances et récupérer rapidement les informations pertinentes.

Send us a text We go inside Mediahuis to see how a small GenAI team is transforming newsroom workflows without losing editorial judgment. From RAG search to headline suggestions and text‑to‑video assists, this episode shares what works, what doesn’t, and how adoption spreads across brands. You’ll hear about: Ten priority use cases shipped across the groupHeadline and summary suggestions that boost clarity and speedRAG‑powered search turning archives into instant contextText‑to‑video tools that free up local video teamsThe hurdles of adoption, quality, and scaling prototypes into productionTheir playbook blends engineering discipline with editorial empathy: use rules where you can, prompt carefully when you must, and always keep journalists in the loop. We also cover policies, guardrails, AI literacy, and how to survive model churn with reusable templates and grounded tests. The result: a practical path to AI in media — protecting judgment, raising quality, and scaling tools without losing each brand’s voice. 🎧 If this sparks ideas for your newsroom or product team, follow the show, share with a colleague, and leave a quick review with your favorite takeaway.

In the era of information overload, organizations struggle to harness the vast amount of unstructured data stored across presentations, reports, images, and text documents. That's why we created the "Autocurator", an AI-powered tool designed to automatically extract, structure, and curate knowledge from heterogeneous document repositories to support Retrieval-Augmented Generation (RAG) systems. Autocurator integrates advanced document parsing pipelines, multimodal AI models, and semantic structuring techniques to convert diverse content - including text, slides, tables, and diagrams - into machine-readable knowledge. This enables downstream RAG systems to query not only text-based insights but also visual and conceptual knowledge that traditionally remained inaccessible. Our system employs a multi-stage pipeline: (1) document ingestion and format normalization, (2) de-duplication of redundant and conflicting information (3) multimodal content understanding using large language and vision models, (4) entity and relationship extraction with human-in-the-loop validation, and (5) generation of structured outputs optimized for retrieval. We will showcase Autocurator’s effectiveness on large enterprise document corpora, showcasing significant gains in retrieval precision and generative quality across several applied AI use cases. By bridging unstructured data and structured knowledge, Autocurator provides a scalable and transparent foundation for next-generation knowledge management and reasoning systems.

Learn how to build an advanced AI agent using Azure Database for PostgreSQL and the new Microsoft Agent Framework. This hands-on lab walks you through integrating Retrieval-Augmented Generation (RAG), semantic re-ranking, Semantic Operators, and GraphRAG (using Apache AGE) to enable intelligent legal question-answering using real case data. Gain practical AI implementation skills with your own PostgreSQL-backed applications.

Please RSVP and arrive at least 5 minutes before the start time, at which point remaining spaces are open to standby attendees.

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.

In this hands-on workshop, you’ll learn to build domain-specific AI agents with Foundry Agent Service. Starting from a simple agent, you’ll add system prompts, custom instructions, and knowledge with RAG. You’ll extend it with tool calling (like a pizza calculator) and connect external services via MCP for live menu and order handling. By the end, you’ll have a working Contoso PizzaBot that can answer questions, recommend pizzas, and manage orders.

Please RSVP and arrive at least 5 minutes before the start time, at which point remaining spaces are open to standby attendees.

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.

Looking to add on-device AI to your apps? Not sure how to get started? Join our lab to learn how to integrate local AI capabilities into your Windows apps using Windows AI APIs. Discover how to implement Semantic Search and Retrieval-Augmented Generation (RAG) to power intelligent information retrieval, and use Phi Silica for on-device text processing. This lab will walk you through key APIs, and best practices to build on-device AI solutions for Copilot+ PCs. Developers of all levels welcome.

Please RSVP and arrive at least 5 minutes before the start time, at which point remaining spaces are open to standby attendees.

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.

Learn how to implement agentic RAG in real-world enterprise workflows. Transform you enterprise through:

• Retrieval: How does a RAG agent decide when to search and which sources matter most for your specific question? • Generation: Using large language models, the system generates responses based on the information it has retrieved. • Autonomy: The system makes decisions about when to look for information, what actions to take, and how to handle multi-step tasks.

Learn how to build an advanced AI agent using Azure Database for PostgreSQL and Semantic Kernel. This hands-on lab walks you through integrating Retrieval-Augmented Generation (RAG), semantic re-ranking, Semantic Operators, and GraphRAG (using Apache AGE) to enable intelligent legal question answering using real case data. Gain practical AI implementation skills with your own PostgreSQL-backed applications.

Please RSVP and arrive at least 5 minutes before the start time, at which point remaining spaces are open to standby attendees.

Join Microsoft’s product team for a hands-on lab where you'll design and deploy an AI-powered application using SQL Database in Microsoft Fabric. This session dives into HTAP capabilities, enabling seamless transactional and analytical processing. You'll provision a SaaS-native SQL Database, use Copilot to generate schema and queries, and implement advanced patterns like RAG with vector search. Walk away with practical skills and a working solution you can apply immediately.

Please RSVP and arrive at least 5 minutes before the start time, at which point remaining spaces are open to standby attendees.

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.

In this hands-on workshop, you’ll learn to build domain-specific AI agents with Foundry Agent Service. Starting from a simple agent, you’ll add system prompts, custom instructions, and knowledge with RAG. You’ll extend it with tool calling (like a pizza calculator) and connect external services via MCP for live menu and order handling. By the end, you’ll have a working Contoso PizzaBot that can answer questions, recommend pizzas, and manage orders.

Please RSVP and arrive at least 5 minutes before the start time, at which point remaining spaces are open to standby attendees.

Dataverse holds rich, contextual business data that powers digital workers—intelligent agents that collaborate with humans in the flow of work. Learn how Model Context Protocol (MCP) enables secure, structured access to enterprise data, enhanced by semantic indexing, vector search, and retrieval-augmented generation (RAG). We’ll explore how to prepare data for agent-readiness and connect Dataverse and business files to Agent 365 for scalable, intelligent automation.