Confluent Introduces New Platform to Bring Real-Time Context to AI

Confluent has introduced Confluent Intelligence, a new unified platform built on Confluent Cloud to help enterprises build and scale context-rich, real-time AI systems. The launch aims to address what the company calls the “AI context gap”, the lack of continuous, trustworthy data that prevents AI from reasoning effectively in production.

This comes right after Confluent launched streaming agents for real-time agentic AI this month.

“AI is only as good as its context,” said Sean Falconer, head of AI at Confluent, in a statement. “Enterprises have the data, but it’s often stale, fragmented, or locked in formats that AI can’t use effectively.”

He added, “Real-Time Context Engine solves this by unifying data processing, reprocessing, and serving, turning continuous data streams into live context for smarter, faster, and more reliable AI decisions.”

Jay Kreps, co-founder and CEO of Confluent, said the company’s data streaming foundation is uniquely positioned to bridge this gap. “Off-the-shelf models are powerful, but without continuous data flow, they can’t deliver timely, business-specific decisions. That’s where data streaming becomes essential,” he said.

Confluent Intelligence integrates Apache Kafka and Apache Flink into a fully managed stack for event-driven AI systems. It includes the Real-Time Context Engine, which streams structured, trustworthy data directly to AI applications via the Model Context Protocol, and Streaming Agents that can observe, decide, and act in real time without manual input. 

The platform also introduces built-in machine learning functions in Flink SQL for anomaly detection, forecasting, and model inference, enabling teams to move from proof of concept to production faster.

“Confluent fuels our models with real-time streaming data and eliminates the fear of data loss,” said Nithin Prasad, senior engineering manager at GEP.

Confluent is also deepening its partnership with Anthropic by integrating Claude as the default large language model into Streaming Agents. The collaboration will allow enterprises to build adaptive, context-rich AI systems for real-time decision-making, anomaly detection, and personalised customer experiences.

With Confluent Intelligence, the company aims to provide the missing foundation for enterprise AI, a continuous, real-time flow of data that helps models move beyond experimentation and into reliable production use.