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CONFLUENT INTELLIGENCE
Develop Real-Time, Context-Aware AI Systems
Confluent Intelligence enables AI systems to continuously learn from historical data and act in real time. Use it to build AI agents, RAG (Retrieval Augmented Generation), and ML pipelines for AI, or to serve context with Apache Kafka® and Apache Flink®.
Build event-driven agents designed for speed and scale
Empower agents with real-time context for optimal decisions
Accelerate accurate decision-making with native ML functions
What is Confluent Intelligence?
Confluent Intelligence is a fully managed service on Confluent Cloud for building real-time, replayable, context-rich AI systems powered by Kafka and Flink. AI systems built on LLMs must be grounded in real data and serve live decisions in production. Confluent Intelligence closes that loop, combining historical evaluation, continuous processing, and real-time serving with:
- Built-in ML functions for anomaly detection, fraud prevention, and forecasting
- Streaming Agents which runs natively on Kafka and Flink to automate workflows
- Real-time Context Engine to serve trustworthy, structured context to any AI app or agent via MCP
Because Confluent Intelligence is powered by the data streaming platform, it uses the same real-time, governed data that feeds your business to power your AI—delivering secure, contextual, and production-ready intelligence at any scale.
Introducing Real-Time Context Engine
Confluent’s vision for streaming + AI, powered by Real-Time Context Engine
A Real-Time Backbone for AI With Confluent + Google
Bringing MCP and A2A together to power multi-agent systems
How to Power AI Agents With MCP
An agentic AI tutorial with Anthropic’s MCP, Claude LLM, and Confluent
Always-On Agents Built Into the Stream
Streaming Agents brings together data processing and AI workflows, making it easy to build and run event-driven agents directly on Flink. With access to real-time contextualized data, they’re uniquely suited for enterprise workflows—monitoring and taking instant, informed action the moment operational events occur.
Seamless integrations
Securely and scalably connect to any model, tool, or data system via a Model Context Protocol (MCP) server for complete, real-time context
Context-aware automation
Enable agents to operate dynamically with Flink jobs on enriched, governed data streams—with zero polling or delay
Replay and debugging
Solve the biggest gaps in AI systems with end-to-end observability to test, debug, evaluate, audit, and reliably recover from failure—iterating faster, safely
Real-time RAG
Continuously supply up-to-date context for RAG and semantic search by converting any Kafka topic into a stream of vector embeddings
Fresh Context to Power Every AI Decision
Deliver live, structured context in real -time from your enriched, enterprise data to any AI system—LangChain, Bedrock, Agentforce, Claude, and more—via MCP with our Real-Time Context Engine. Fully managed on Confluent Cloud, it delivers governed, real-time context.
Serve any AI app or agent at low latency
Provide any AI agent, copilot, and model anywhere with structured, live relevant data instead of stale snapshots
Continuously synced with upstream data and systems
Unify across historical and streaming data, automatically updated as upstream logic or features change, ensuring decisions reflect current context
Zero infrastructure to manage
Eliminate complexity and focus on building AI with fully managed MCP, while gaining built-in authentication, RBAC, audit logging, and observability. No Kakfa or Flink expertise needed
Real-Time Machine Learning
Execute sophisticated ML pipelines for fraud and anomaly detection, forecasting, sentiment analysis, and more to trigger AI workflows. Utilize native Apache Flink functions to embed ML capabilities directly within data streams, simplifying workflows and unlocking accurate, actionable insights.
Anomaly detection
Identify unexpected deviations in real-time, improving data quality and enabling faster decision-making
Forecasting
Perform real-time analysis and gain actionable insights from your streaming data without needing in-depth data science expertise
ML preprocessing
Transform features into representations more suitable for downstream processors
Invoke remote AI/ML models
Simplify developing and deploying AI by providing a unified platform for both data processing and AI/ML tasks
Confluent Intelligence integrates seamlessly with your critical AI infrastructure. Deploy across major cloud providers and connect to the leading providers of vector search, databases, and AI frameworks. Leverage strategic partnerships and system integrators to accelerate your real-time AI initiatives.
Build Real-Time RAG Pipelines
Power Retrieval-Augmented Generation (RAG) pipelines with live, governed enterprise data through External Tables. Stream fresh context from Kafka and Flink directly into LangChain, Bedrock, or Claude - no manual ETL or index rebuilds required—so every response reflects your most current business data.
Detect Anomalies, Fraud, and Forecast Trends
Run real-time machine learning pipelines for anomaly detection, fraud prevention, and demand forecasting. Stream-based ML functions on Kafka and Flink continuously analyze stock trades, network events, and customer behavior—so your systems can identify risks and predict outcomes before they happen.
"Confluent's platform allows us to stream chagnes as they happen, ensuring that our AI tools always provide the most relevant and timely information."
"Data streaming is going to be critical for financial services organizations in the future—especially when it comes to AI/ML use cases where the ability to stream data, implement workflow automation, and make real-time predictions are the key to success."
"Everyone wants AI—but the hard part is getting high-quality data moving in real time. The Confluent data streaming platform makes that possible for us. It's the foundation that gets our data moving and gets it where it needs to be."
“Good AI needs good data. Confluent is our trusted source of truth… the data streaming platform provides context and orchestration for our AI agents to automate workflows and accelerate our smart city transformation.”
New developers get $400 in credits during their first 30 days—no sales rep required.
Confluent provides everything you need to:
- Build with client libraries for languages like Java and Python, sample code, 120+ pre-built connectors, and a Visual Studio Code extension.
- Learn from on-demand courses, certifications, and a global community of experts.
- Operate with a CLI, IaC support for Terraform and Pulumi, and OpenTelemetry observability.
You can sign up with your cloud marketplace account below, or directly with us.
Confluent Cloud
A fully managed, cloud-native service for Apache Kafka®
Confluent Intelligence | FAQs
What is Confluent Intelligence and how does it help build AI applications?
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.
How do "Streaming Agents" work within Confluent Intelligence?
Streaming Agents is a framework for 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.
What makes Confluent intelligence uniquely suited for real-time generative AI (GenAI) applications?
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.
Can I use my own machine learning models with Confluent Intelligence?
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.
How does Confluent Intelligence help create and use embeddings for AI?
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.
What are some real-world use cases for AI Agents built on Confluent?
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.
What is the Model Context Protocol (MCP) and why is it important for AI Agents?
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.