New in Confluent Cloud: Making Data & Pipelines Accessible for AI-Ready Streaming | Learn More
Confluent announces the General Availability of Queues for Kafka on Confluent Cloud and Confluent Platform with Apache Kafka 4.2. This production-ready feature brings native queue semantics to Kafka through KIP-932, enabling organizations to consolidate streaming and queuing infrastructure while...
Confluent's AI developer tools are now GA: an open-source local MCP server, a managed MCP server, and Agent Skills. Together they give AI coding assistants direct access to your streaming platform — the tools to act on it and the domain knowledge to build correctly.
Explore new Confluent Intelligence features: enhanced querying with Real-Time Context Engine, PII detection, sentiment analysis, and support for TimesFM, Anthropic, and Fireworks AI models.
Before deploying agentic AI, enterprises should be prepared to address several issues that could impact the trustworthiness and security of the system.
Confluent’s Create Embeddings Action for Flink helps you generate vector embeddings from real-time data to create a live semantic layer for your AI workflows.
We built an AI-powered tool to automate LinkedIn post creation for podcasts, using Kafka, Flink, and OpenAI models. With an event-driven design, it’s scalable, modular, and future-proof. Learn how this system works and explore the code on GitHub in our latest blog.
FLIP 304 lets you customize and enrich your Flink failure messaging: Assign types to failures, emit custom metrics per type, and expose your failure data to other tools.
Learn how Flink enables developers to connect real-time data to external models through remote inference, enabling seamless coordination between data processing and AI/ML workflows.
The rise of agentic AI has fueled excitement around agents that autonomously perform tasks, make recommendations, and execute complex workflows. This blog post details the design and architecture of PodPrep AI, an AI-powered research assistant that helps the author prepare for podcast interviews.
Confluent Cloud 2024 Q4 adds private networking and mTLS authentication, follower fetching, Flink updates, WarpStream features to support migration and governance, and more!
GenAI thrives on real-time contextual data: In a modern system, LLMs should be designed to engage, synthesize, and contribute, rather than to simply serve as queryable data stores.
Continuing issues with hallucinations, the increasing independence of agentic AI systems, and the greater usage of dynamic data sources, are three AI trends you may want to monitor in 2025.
In this final part of the blog series, we bring it all together by exploring data streaming platforms (DSPs), event-driven architecture (EDA), and real-time data processing to scale AI-powered solutions across your organization.
In Part 2 of the series, we take things a step further by enhancing GenAI with the tools it needs to deliver smarter, more relevant responses. We introduce retrieval-augmented generation (RAG) and vector databases (VectorDBs), key technologies that provide LLMs with the context they need.
Discover how predictive analytics, powered by generative AI and data streaming, transforms business decisions with real-time insights, accurate forecasts, and innovation.
Transform your ad campaigns with generative AI + Confluent. Optimize performance, automate tasks, and deliver personalized content—all in real time.
Break language barriers with multi-language data streams. From real-time translation to scalable localization, global communication has never been easier. Explore the best practices and tools to expand your reach.