Confluent makes it easier to build and secure real-time AI at scale
New capabilities remove barriers to production-ready AI applications with agent-powered workflows, automated data protection, and private cloud connectivity.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
Agentic Fleet Management Architecture for Real-Time Operations
Agentic fleet management is a real-time, event-driven architecture where distributed AI agents continuously process streaming data to make autonomous operational decisions and execute them through closed-loop feedback systems. At its core, agentic systems enable: Unlike traditional systems that react to events after the fact, agentic architectures operate as adaptive, self-optimizing systems.
AI Tools for Builders - Confluent's MCP Server & Agent Skills
Your AI coding assistant just learned to speak Confluent. Developers live in their editors. The best platform tools meet them there—and increasingly, that means their AI assistants meet them there too. AI coding tools are already reshaping how developers build, debug, and operate software, but most of them are generalists. They can write an Apache Kafka producer, but they won't know your Schema Registry subjects.
Keynote: Building Intelligent Systems on Real-time Data
Confluent CEO Jay Kreps takes the stage alongside industry leaders at data streaming’s biggest event. Together, they’ll show why free-flowing, real-time data has become the key to unleashing the full potential of intelligent systems across every business. From live demos to real-world use cases to industry-changing product announcements, this year’s keynote is essential viewing for anyone looking to maximize the potential of their AI. Which is pretty much everyone. Don’t miss it.
Designing Sovereignty in Real-Time Data Streaming
As regulatory frameworks such as the General Data Protection Regulation (GDPR), Digital Operational Resilience Act (DORA), and Network and Information Security Directive 2 (NIS2) converge with the US Clarifying Lawful Overseas Use of Data Act (CLOUD Act), contractual assurances are no longer a sufficient defense. For senior leadership, digital sovereignty has evolved from a compliance checkbox into a core architectural requirement.
InfiniteWatch + Confluent: Turning Customer Interaction Data into Real-Time Intelligence
Every customer interaction generates signals that matter—a failed checkout, repeated form errors, a frustrated support call, a confusing AI agent exchange, or an unresolved email thread. Individually, these are isolated events. Connected, they reveal customer intent, friction points, operational risk, and opportunities for action.
The Magic of Confluent Freight Clusters
Wix rewired 85% of its data volume onto Confluent Freight Clusters—and the result was lower costs and elastic scalability that handles Black Friday–scale spikes without manual intervention. Josef Goldstein explains why it felt like a magical solution.
More Signal, Less Guesswork: New Kafka Observability Updates in Confluent Cloud
We’re introducing enhanced visibility for streaming workload performance on Confluent Cloud, making it easier for developers and operators to understand, troubleshoot, and optimize real-time applications. As Apache Kafka has become the backbone of data streaming, many teams rely on Confluent Cloud for its scale, elasticity, and reduced operational burden.
Feed Your Data Lake With Real-Time, Analytics-Ready Tables for 30-50% Lower Cost Using Tableflow
Organizations are under pressure to feed data lakes and lakehouses with fresher data while keeping a tight lid on cloud spend. The problem is that most ingestion stacks weren’t designed for the real-time, high-volume workloads that power modern analytics and artificial intelligence (AI). They rely on layers of connectors, ETL jobs, and maintenance processes that quietly inflate both infrastructure and operational costs. Confluent’s Tableflow was built to change that equation.
Data Needs to Be as Fast as Your Fastest Use Case
Wix processes 40 billion events a day across use cases that range from minutes to milliseconds. Josef Goldstein explains why the entire upstream architecture has to be built around your most latency-sensitive lane—or none of it works.