リアルタイムでコンテキストを認識する AI システムの開発

Confluent Intelligence is a fully managed service on Confluent Cloud, designed for building real-time, context-rich AI systems. It unifies the essential components for production AI—stream processing, state management, and context delivery—into a single stack. This allows developers to build and deploy event-driven, context-aware AI applications powered by Apache Kafka® and Flink® without the heavy operational burden of managing complex infrastructure.

Streaming Agents is a framework for building, testing, and deploying event-driven AI agents directly on your data streams using fully managed Flink and Kafka. Because these agents run as Flink jobs within the stream processing pipeline, they have access to the freshest, most accurate view of your business. This enables them to monitor events and take instant, informed actions as they happen, making them ideal for enterprise automation and real-time decision-making.

Confluent Intelligence is uniquely suited for GenAI because it solves the critical challenge of providing real-time, trustworthy context. It combines AI agents that can live in your data streams, a real-time context engine and native ML functions, allowing GenAI applications to access live, indexed data and building event-driven AI without requiring developers to write custom Kafka code or build complex data polling mechanisms.

Yes. Confluent Intelligence allows you to execute sophisticated AI and machine learning pipelines. In addition to native Flink functions for anomaly detection and forecasting, it provides the capability to invoke remote AI/ML models. This simplifies the development and deployment of your AI applications by providing a unified platform for both data processing and your custom AI/ML tasks.

Confluent Intelligence streamlines the process of creating vector embeddings for your AI applications. You can choose your preferred embedding model and transform a text column within a Kafka topic into a continuous stream of vector embeddings. This real-time embedding creation is essential for powering use cases like Retrieval-Augmented Generation (RAG) and semantic search in your GenAI applications.

The platform is designed for a variety of enterprise use cases, including:

• Real-time Copilots: Building intelligent assistants that use current, contextual data from over 120 sources to provide more accurate decision-making.
• Workflow Automation: Orchestrating complex business processes with AI Agents that run continuously, with humans kept in the loop for critical decisions.
• Real-time Fraud Detection: Using integrated anomaly detection on live transaction streams to identify and block fraudulent activity in milliseconds.

Model Context Protocol (MCP) is an open standard that provides a universal way for AI models to connect to data sources and tools. MCP is transformative for agentic AI because it enables you to provide agents with improved context and give them access to a rich toolkit of capabilities by simply connecting it to relevant MCP servers.