AI Agents can be effectively developed using streaming SQL queries, particularly with platforms like Apache Flink, which enhance consistency, scalability, and developer experience. By treating AI Agents as event-driven systems that interact with large language models (LLMs), developers can create more efficient and responsive applications that process data in real-time. The article discusses the potential advantages of this approach and provides examples of how to implement it using SQL queries.

AI agents can be effectively built as streaming SQL queries, leveraging platforms like Apache Flink for enhanced consistency, scalability, and developer experience. By transforming the traditional approach of creating microservices into a model that utilizes SQL, developers can create intelligent systems that interact with large language models (LLMs) for tasks such as summarizing documents or responding to real-time events. This method opens new avenues for developing autonomous, event-driven AI agents that can operate efficiently in various applications.

Apache Flink has announced the preview release of Apache Flink Agents 0.1.0, a new sub-project designed to integrate event-driven AI agents with Flink's streaming runtime. This framework aims to enhance real-time processing of high-volume event streams, ensuring reliability and scalability while providing first-class abstractions for agent functionalities such as large language models and dynamic orchestration.