alnzng commented on code in PR #809: URL: https://github.com/apache/flink-web/pull/809#discussion_r2404991060
########## docs/content/posts/2025-10-01-release-flink-agents-0.1.0.md: ########## @@ -0,0 +1,57 @@ +--- +title: "Apache Flink Agents 0.1.0 Release Announcement" +date: "2025-10-01T08:00:00.000Z" +authors: +- xtsong: + name: "Xintong Song" +aliases: +- /news/2025/10/01/release-flink-agents-0.1.0.html +--- +\<todo: update release date in: filename, header-date, header-aliases\> + +The Apache Flink Community is excited to announce the first preview release of Apache Flink Agents (0.1.0). + +## What is Apache Flink Agents + +Apache Flink Agents is a brand-new sub-project from the Apache Flink community. It's an open-source framework for building event-driven streaming agents that can operate with scalability, reliability, and real-time responsiveness. + +### Why Apache Flink Agents Matters + +While AI agents have made rapid progress in interactive applications like chatbots, most still operate outside the high-throughput, low-latency world of real-time data processing. Yet in industrial settings -- from e-commerce and finance to IoT and logistics -- critical decisions must be made instantly in response to live events: a payment failure, a sensor anomaly, a user click. These workloads demand more than just intelligence -- they require massive scale, millisecond latency, fault tolerance, and stateful coordination, all of which are strengths of Apache Flink. But until now, there’s been no unified framework to bring agentic AI patterns into this proven streaming ecosystem. Apache Flink Agents bridges this gap. + +### Key Features + +Building on Flink's battle-tested streaming engine, Apache Flink Agents inherits distributed, at-scale, fault-tolerant structured data processing and mature state management, and adds first-class abstractions for Agentic AI building blocks and functionalities -- large language models (LLMs), prompts, tools memory, dynamic orchestration, observability, and more. + +The key features of Apache Flink Agents include: Review Comment: @xintongsong I understand and agree with your point about focusing on WHAT rather than HOW. Let me clarify my original thoughts: - I thought the `Exactly-Once Action Consistency` section mainly focuses on ensuring unique (exactly-once) action execution, rather than deterministic action behavior (i.e., deterministic recovery despite the inherently non-deterministic nature of LLMs). - The non-deterministic LLM output is actually one of the core challenges addressed by the `replay-based per-action state consistency` design. Making LLM actions deterministic is also a fundamental requirement for features like regression testing and debugging. - With that in mind, I was thinking this aspect might deserve to be highlighted separately. That said, your `side effects` statement may already implicitly cover this, since non-deterministic LLM behaviors can be viewed as side effects of agent actions. If that’s the case, perhaps we could make it explicit (e.g., “side effects such as non-deterministic LLM outputs”) to make the intention clearer. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
