Confluent, Inc., a leader in data streaming, has introduced Streaming Agents, a new capability within Confluent Cloud for Apache Flink®. The solution is designed to help enterprises move rapidly from AI prototypes to production-ready agentic systems by combining real-time data processing with intelligent AI workflows.
Streaming Agents enable organizations to build and scale AI agents that can monitor, reason, and act in real time. By securely connecting to large language models (LLMs), embedding models, APIs, and enterprise systems, the platform addresses one of the biggest barriers to AI adoption: unifying fragmented data and tools. Confluent says this advancement will speed up enterprise AI initiatives, enhance operational efficiency, and open the door to new business models.
“Agentic AI is on every organization’s roadmap. But most companies are stuck in prototype purgatory, falling behind as others race toward measurable outcomes,” said Shaun Clowes, Chief Product Officer at Confluent. “Even your smartest AI agents are flying blind if they don’t have fresh business context. Streaming Agents simplifies the messy work of integrating the tools and data that create real intelligence, giving organizations a solid foundation to deploy AI agents that drive meaningful change across the business.”
Research from IDC highlights the challenge: enterprises averaged 23 generative AI proofs of concept between 2023 and 2024, but only three progressed to production, and just 62% of those met expectations. Complex, costly workflows and the absence of real-time data remain key obstacles.
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Streaming Agents embed agentic AI directly into stream processing pipelines using Apache Kafka® and Apache Flink®. These always-on, event-driven agents can dynamically process vast data streams, exchange context with other systems, and respond instantly as business conditions evolve—similar to how human operators make real-time decisions.
Key features include:
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Context-aware automation through Model Context Protocol (MCP) for intelligent tool selection.
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Secure integrations with models, vector databases, and SaaS applications.
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Enhanced accuracy via External Tables and real-time search for Retrieval-Augmented Generation (RAG).
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Replayability for safe testing, dark launches, and faster iteration cycles.
From adjusting e-commerce pricing in real time to orchestrating complex enterprise workflows, Streaming Agents offer a pathway for organizations to unlock the full potential of agentic AI at scale.