Sean Falconer

Your agents can't act on yesterday's data. In this session, see how to connect streaming events, governed data, and agent orchestration into one production-ready streamhouse architecture on AWS.

Learn how a Streamhouse data architecture powers agents, applications, and lakehouses with the always current state of your business. We'll dive into real-world examples from Together AI, Accenture, and Indeed, along with demos and new product launches that leave you with a practical approach to solving the toughest data challenges.

Business data is constantly changing, but most AI agents make decisions based on a version of it that's already out of date. To run key parts of your operations, not just query the past, agents need continual access to that change at machine speed, not a periodic copy. An agent that approves a refund based on an hour-old order status, or routes a support ticket using stale customer state, isn't reasoning about the business, it's reasoning about a memory of it. Executing a complex workflow correctly requires real-time context, low-latency decisioning, and coordination with other agents, not just a bigger model. This session looks at how Gemini platform and Confluent close that gap together. Confluent's Real-Time Context Engine exposes live business state to Gemini agents over MCP, so an agent queries current data directly instead of working from a copy, runs inference inline as data moves, and coordinates with other agents on what just happened. Tableflow keeps that same data available for analytics by continuously converting Kafka streams into Iceberg tables registered in the BigQuery Data Catalog, so the operational and analytical views never drift apart. We'll walk through…

Fresh off the keynote? Join us for an interactive follow-up discussion and open Q&A on the Streamhouse framework. We'll dive deeper into how this open, vendor-neutral data architecture keeps real-time context continuously available for modern applications and AI agents.

Most AI applications work with a fixed dataset. They analyze yesterday's reports, last week's exports, or a database copy from when it was made. But when used in real life, data keeps changing, and the demo effect fades. Your agents can miss changes in data structure and never see the events your business depends on. The Model Context Protocol (MCP) changes how we solve this problem. MCP gives AI a common way to connect to tools and data, including systems such as Kafka. In this session, we'll create agents that listen to live events, understand changing data structures, and make decisions that affect the whole system. We'll explain how MCP's request/response style works with streaming's continuous updates, how to share changing information without confusing the AI, and the common problems that are rarely shown. You'll leave seeing AI as part of your streaming system, connected to the events!

Just like we don’t put crude oil in a car, we refine it into gasoline. AI can’t run on raw data either. It needs continuous, refined context. Serving fresh context is the missing architectural layer for reliable AI. In this session, learn how Confluent Intelligence and Databricks turn operational data into low-latency context for AI agents and applications. See how Confluent’s fully managed streaming platform feeds trusted data into the Databricks lakehouse for Agent Bricks and AI/BI. We’ll demo real-time architectures for anomaly detection, product personalization, and AI-powered data enrichment, showing how to keep ML models and LLMs grounded in the live state of your business, not stale batch snapshots. You’ll leave with practical, streaming-native patterns to move from brittle pipelines and demos to production-grade AI on Databricks while reducing TCO.

Join Jay Kreps, Confluent leadership, our customer - Getir, and industry thought leaders to learn how you can build intelligent systems with real-time data. We’ll show you why streaming is becoming ubiquitous across the business—and how that unlocks a shift-left approach: process and govern at the source, then reuse everywhere. Expect live demos and candid customer stories that make it concrete. Whether you’re a data leader, architect, or builder, you’ll leave with practical playbooks for bringing real-time AI to production. The future is here—let’s ignite it together!

AI systems don’t fail because models are weak. They fail because context is wrong. As agentic AI moves from experimentation to production, teams are discovering a new class of data problems. Context is scattered across operational databases, event streams, APIs, and vector stores. It’s stale by the time it reaches the model, inconsistent across tools, and expensive to recompute. Most architectures were never designed to continuously assemble and serve context at runtime. This talk introduces context engineering as a practical, systems-level discipline focused on solving these data challenges. Rather than treating context as static input, context engineering treats it as a continuously computed product, derived from live business signals, enriched in real time, governed, and served with low latency to AI systems. We’ll focus on the role of streaming and event-driven architectures as the foundation for this approach. You’ll see why batch pipelines and warehouse-centric designs struggle with agent workloads, and how stream processing enables data enrichment and reprocessing of context as data evolves. In the second half, we’ll build this live. Using Kafka and Flink, we’ll construct a…

Most enterprises are stuck in AI pilot purgatory—impressive demos, no ROI. Garther predicts that by 2028 80% of successful GenAI apps will run on existing data platforms, yet many still treat AI as “magic” exempt from existing data challenges.

Learn how to use a native framework for building long-running, event-driven AI agents directly within Flink’s runtime where agents can observe, reason, and act using live data. This project is grounded in a simple worldview: Agents are software, not prompts. To succeed, they must behave like real services; autonomous, reactive, and resilient. We’ve built Flink Agents around four key pillars: 1) Event-native by design 2) context is everything 3) replay is a superpower 4) every engineer is an AI engineer. Join us for a deep dive, see the architecture, roadmap, and live demo on how to manage a real-time workflow with streaming inputs and autonomous decisions.

Join Jay Kreps, Confluent leadership, our customers, and industry thought leaders to learn how you can build intelligent systems with real-time data. We’ll show you why streaming is becoming ubiquitous across the business—and how that unlocks a shift-left approach: process and govern at the source, then reuse everywhere. Expect live demos and candid customer stories that make it concrete. Whether you’re a data leader, architect, or builder, you’ll leave with practical playbooks for bringing real-time AI to production. The future is here-let's ignite it together!

At their core, AI agents are microservices with a brain. They're powered by large language models (LLMs) and are independent, specialized, and designed to operate autonomously. But agents need more than LLMs to scale -- they need real-time data access, context, and the ability to collaborate across tools, services, and even other agents. As timely data becomes crucial for modern AI systems, agents must operate within distributed, event-driven environments. This session focuses on how to bridge the gap between streaming infrastructure and agentic architectures, by enabling developers to build, test, and operate agents natively on Flink. Through architecture diagrams, use cases and a demo, we'll show practical steps for getting started with streaming agents to power new automation workflows.

Life as a father, tech leader, and fitness enthusiast demands efficiency. To reclaim my time, I’ve built AI-driven solutions that automate everyday tasks—from research agents that prep for podcasts to multi-agent systems that plan meals—all powered by real-time data and automation. This session dives into the technical foundations of these solutions, focusing on event-driven agent design and scalable patterns for robust AI systems. You’ll discover how Databricks technologies like Delta Lake, for reliable and scalable data management, and DSPy, for streamlining the development of generative AI workflows, empower seamless decision-making and deliver actionable insights. Through detailed architecture diagrams and a live demo, I’ll showcase how to design systems that process data in motion to tackle complex, real-world problems. Whether you’re an engineer, architect, or data scientist, you’ll leave with practical strategies to integrate AI-driven automation into your workflows.

Learn how Confluent simplifies real-time streaming of your SAP data into AI-ready Delta tables on Databricks. In this session, you'll see how Confluent’s fully managed data streaming platform—with unified Apache Kafka® and Apache Flink®—connects data from SAP S/4HANA, ECC, and 120+ other sources to enable easy development of trusted, real-time data products that fuel highly contextualized AI and analytics. With Tableflow, you can represent Kafka topics as Delta tables in just a few clicks—eliminating brittle batch jobs and custom pipelines. You’ll see a product demo showcasing how Confluent unites your SAP and Databricks environments to unlock ERP-fueled AI, all while reducing the total cost of ownership (TCO) for data streaming by up to 60%.

This session unveils Google Cloud's Agent2Agent (A2A) protocol, ushering in a new era of AI interoperability where diverse agents collaborate seamlessly to solve complex enterprise challenges. Join our panel of experts to discover how A2A empowers you to deeply integrate these collaborative AI systems with your existing enterprise data, custom APIs, and critical workflows. Ultimately, learn to build more powerful, versatile, and securely managed agentic ecosystems by combining specialized Google-built agents with your own custom solutions (Vertex AI or no-code). Extend this ecosystem further by serving these agents with Databricks Model Serving and governing them with Unity Catalog for consistent security and management across your enterprise.

Get together at the U.S. & Canada–West meetup, where attendees from the region gather to build new connections. This is your chance to meet local community leaders, exchange ideas and expand your professional network. Join us!

Autonomous agents are reshaping enterprise operations, but scaling them isn’t just about smarter AI—it’s about better infrastructure. Agents need real-time data, seamless tool integration, and shared outputs across systems. Rigid request/response models create bottlenecks, while event-driven architecture (EDA) unlocks the flexibility and scalability agents require. This session will show how EDA enables autonomous agents to thrive. Key takeaways include: - How EDA enables real-time, adaptive agent workflows and multi-agent problem solving. - Key design patterns like Orchestrator-Worker, Multi-Agent Collaboration, and Market-Based Competition. - Strategies for leveraging Kafka to handle scalability, fault tolerance, and low latency. - Lessons from microservices evolution to solve interoperability and context-sharing challenges. This talk is for engineers and architects building scalable AI systems. You’ll leave with actionable insights to design resilient, event-driven agents and future-proof your infrastructure for enterprise-scale AI.