Streaming Agents cuts complexity with new capabilities to launch faster, iterate smarter, and scale securely
NEW ORLEANS–(BUSINESS WIRE)–Confluent, Inc. (Nasdaq:CFLT), the data streaming pioneer, today introduced new Streaming Agents advancements that make it easier to build and scale event-driven artificial intelligence (AI) agents. With the new Agent Definition, teams can create production-ready agents in just a few lines of code. Built-in observability and debugging give teams confidence to quickly move from projects to real-world use cases with replayability, testability, and safe recovery. Confluent’s Real-Time Context Engine provides fresh context with enterprise-grade governance so organizations can bring trustworthy AI agents to market faster.
Building agentic AI is hard. AI agents power scalable generative AI (GenAI) adoption that drives business transformation, but many organizations face challenges with governance and data complexity. Connecting data is hard, failures are tough to troubleshoot, and brittle monoliths are unscalable. For enterprises, the stakes are even higher. They need systems that can respond in real time, yet most AI today can’t act on critical events without human intervention.
“With advancements in agentic AI and data processing automation, real-time data processing and streaming analytics capabilities are essential,” states the IDC MarketScape: Worldwide Data Platform Software 2025 Vendor Assessment. “The ability to process data as it enters the system is crucial for time-sensitive applications such as fraud detection, personalization, and operational monitoring, where delays can result in lost opportunities or increased risks.”
Streaming Agents makes building agentic AI easier. Streaming Agents brings the stream processing strengths of Apache Flink®—scale, low latency, and fault tolerance—together with agent capabilities like large language models (LLMs), tools, memory, and orchestration. Because Streaming Agents lives directly in event streams, it monitors the state of a business with the latest real-time data. This produces enterprise AI agents that can observe, decide, and act in real time, without stitching together disparate systems. Streaming Agents brings data processing and AI together in one place so teams can finally launch AI agents that are always-on and ready to act instantly.
“Today, most enterprise AI systems can’t respond automatically to important events happening in a business without someone prompting them first,” said Sean Falconer, Head of AI at Confluent. “This leads to lost revenue, unhappy customers, or added risk when a payment fails or a network malfunctions. Streaming Agents brings real-time data and agent reasoning together so teams can quickly launch AI agents that observe and act in real time with the freshest, most accurate data.”
Simplify and Accelerate Agentic AI Adoption With Streaming Agents
Streaming Agents (available in Open Preview) enables organizations to build, deploy, and orchestrate event-driven AI agents directly on Confluent Cloud, unifying data processing and AI workflows. Streaming Agents takes this further with three new capabilities:
- Build agents faster. With Agent Definition, teams can build agent workflows using only a few lines of code that can be reused and tested to save time. Agents can take on complex tasks with iterative tool calling that can be optimized for better outputs.
- See what’s happening. The lack of visibility and testability is one of the biggest gaps in AI systems. Streaming Agents makes it easier for teams to jump in whenever testing, oversight, or approvals are needed. With built-in observability and debugging, developers can easily trace and replay every agent interaction and compare outcomes, enabling fast iteration, reliable testing, and debugging all in the same data infrastructure. This also creates a tamper-proof decision log that’s essential for security, trust, and compliance when deploying agents.
- Get contextualized, trustworthy data. Real-Time Context Engine, a fully managed service, delivers fresh context to Streaming Agents as well as to any other AI agent or application. Teams can reprocess their contextual data to improve and evolve their AI agents. Additionally, Real-Time Context Engine ensures that data meets compliance and security requirements with built-in authentication, role-based access control (RBAC), and audit logging.
Confluent Partners Solve AI’s Toughest Data Challenges
Streaming Agents can be built on major cloud providers Amazon Web Services (AWS), Microsoft Azure, and Google Cloud and communicate with external agents built on frameworks such as LlamaIndex. They’re fueled with real-time context and can trigger event-driven workflows across the AI stack, including Clickhouse, MongoDB, Snowflake, and more. Customers can also team up with industry experts like Infosys to quickly implement Streaming Agents and scale AI with confidence.
An open source version called Flink Agents is an Apache Flink project created from a joint collaboration between Alibaba Cloud, Confluent, LinkedIn, and Ververica. It’s designed with Flink as a native framework for building long-running, event-driven AI agents directly within Flink’s runtime. To learn more, contribute, and join the community, visit the GitHub page.
Additional Info and Other Confluent News
To learn more about new Streaming Agents features, check out this launch blog. Confluent also announced its Real-Time Context Engine and Confluent Private Cloud. Read the press releases to learn more.
About Confluent
Confluent is the data streaming platform that is pioneering a fundamentally new category of data infrastructure that sets data in motion. Confluent’s cloud-native offering is the foundational platform for data in motion—designed to be the intelligent connective tissue enabling real-time data from multiple sources to constantly stream across an organization. With Confluent, organizations can meet the new business imperative of delivering rich, digital frontend customer experiences and transitioning to sophisticated, real-time, software-driven backend operations. To learn more, please visit www.confluent.io.
As our roadmap may change in the future, the features referred to here may change, may not be delivered on time, or may not be delivered at all. This information is not a commitment to deliver any functionality, and customers should make their purchasing decisions based on features that are currently available.
Confluent® and associated marks are trademarks or registered trademarks of Confluent, Inc.
Apache®, Apache Flink®, and Flink® are registered trademarks of the Apache Software Foundation in the United States and/or other countries. No endorsement by the Apache Software Foundation is implied by the use of these marks. All other trademarks are the property of their respective owners.
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