Confluent Intelligence
Confluent Intelligence enables you to seamlessly integrate large-language models (LLMs), machine learning (ML), retrieval-augmented generation (RAG), and agentic AI into your streaming data workflows.
Confluent Intelligence is a suite of the following features.
- Streaming Agents: Use Streaming Agents to build AI workflows that can invoke tools to interact with external systems, perform actions, or retrieve information as part of an AI workflow.
- Real-time Context Engine: The Real-time Context Engine continuously materializes enriched enterprise data sets into a fast, in-memory cache and serves them to AI systems by using the Model Context Protocol (MCP), all fully managed within Confluent Cloud.
- Built-in machine learning (ML) functions: Confluent Cloud for Apache Flink provides built-in functions for building ML workflows, like ML_DETECT_ANOMALIES and ML_FORECAST.
Streaming Agents
The key to agentic AI isn’t building better LLMs – it’s data readiness.
Streaming Agents bridge the gap between enterprise data and AI capabilities by providing:
- Real-time data access: Fresh, contextualized data for AI decision-making
- Unified data processing: Seamless integration of streaming and batch data
- Enterprise data utilization: Effective use of existing enterprise data assets
- Context-aware automation: Agents that understand and act on current business context
With Streaming Agents, you can:
- Unify stream processing and agentic AI workflows using familiar Flink APIs, simplifying development and enabling every engineer to be an AI engineer.
- Integrate seamlessly with any tool, model, and data system.
- Access real-time context to enable agents to operate dynamically on live operational events and effectively use LLMs as reasoning engines to plan, decide, and act.
- Ensure agents are secure and trustworthy with full visibility, control, and secure, governed event flows.
Real-time Context Engine
The Real-time Context Engine enables AI agents to query the most up-to-date context, grounding their responses in real-time data. It supports structured data with lookup by primary key. The Real-time Context Engine is available to AI agents by using MCP and works with any agent, hosted anywhere, as long as it supports MCP.
Real-time Context Engine tables are always loaded in memory, so they provide low-latency response times for agent queries. AI agents require fast access to relevant data to make informed decisions and provide accurate responses. The Real-time Context Engine provides the low-latency data access needed for real-time AI agent context serving.
Built-in machine learning (ML) functions
Simplify complex data science tasks into Flink SQL statements. Built-in ML functions enable forecasting and anomaly detection with Flink SQL functions to derive real-time insights, with no ML expertise or model building needed.
- Do continuous forecasting on time-series streaming data, with out-of-the-box configuration (Auto-ARIMA) or custom user configuration, like training size, seasonality, and forecast horizon.
- Perform anomaly detection for each new event.
- See real-time visualizations, like time-series charts and graphs showing forecasted values and anomalies.
Built-in ML Functions provide time-series Forecasting and Anomaly Detection SQL functions for streaming data, enabling you to derive real-time insights. These functions simplify complex data science tasks into Flink SQL, providing a familiar yet powerful way to apply AI to streaming data. Built on top of popular ML algorithms like ARIMA optimized for real-time performance, the functions deliver accurate forecasts and reliable anomaly detection.
With built-in ML functions, you can:
- Eliminate the need for batch processes
- Bridge the gap between data analysis and machine learning
- Gain real-time, actionable insights
Built-in ML functions make it easier for you to harness the full potential of AI-driven analytics. SQL functions enable real-time analysis, reduce complexity, and speed up decision-making by delivering insights immediately as the data is ingested. Built-in forecasting and anomaly detection make real-time AI accessible to everyone, enabling agents and teams to make smarter decisions faster.
Common use cases include:
- Operational monitoring: Detect system failures or performance issues in real time, minimizing downtime.
- Financial forecasting: Predict trends and identify irregular transactions in streaming financial data.
- IoT analytics: Monitor sensor data in industrial settings to detect equipment malfunctions or predict maintenance needs.
- Retail analytics: Forecast demand and optimize inventory by identifying purchasing trends in real time.
- Marketing: Monitor marketing campaign performance in real-time.
Build AI with Confluent Intelligence in Confluent Cloud | Confluent Documentation
Introducing Real-Time Context Engine for AI
Confluent Intelligence: Real-Time AI With Apache Kafka® and Apache Flink®