Abstract
Agentic AI is rapidly transitioning from research prototypes to enterprise deployments, where requirements extend to meet the software quality attributes of reliability, scalability, and observability beyond plausible text generation. We present Agentics 2.0, a lightweight, Python-native framework for building high-quality, structured, explainable, and type-safe agentic data workflows. At the core of Agentics 2.0, the logical transduction algebra formalizes a large language model inference call as a typed semantic transformation, which we call a transducible function that enforces schema validity and the locality of evidence. The transducible functions compose into larger programs via algebraically grounded operators and execute as stateless asynchronous calls in parallel in asynchronous Map-Reduce programs. The proposed framework provides semantic reliability through strong typing, semantic observability through evidence tracing between slots of the input and output types, and scalability through stateless parallel execution. We instantiate reusable design patterns and evaluate the programs in Agentics 2.0 on challenging benchmarks, including DiscoveryBench for data-driven discovery and Archer for NL-to-SQL semantic parsing, demonstrating state-of-the-art performance.
Abstract (translated)
代理型AI正在迅速从研究原型转向企业部署，其需求不仅限于可能的文字生成，还要求满足软件质量属性，如可靠性、可扩展性和可观测性。我们介绍Agentics 2.0，这是一个轻量级的Python原生框架，用于构建高质量、结构化、解释性强和类型安全的代理数据工作流。 在Agentics 2.0的核心中，逻辑变换代数将大型语言模型的推理调用形式化为一个有类型的语义转换，我们将其称为可变函数。这种可变函数强制执行模式的有效性和证据的地方性。通过基于代数的操作符，这些可变函数可以组合成更大的程序，并在异步Map-Reduce程序中以无状态的方式并行执行。 该框架提供了通过强类型提供的语义可靠性、通过输入和输出类型的槽之间证据跟踪的语义可观测性以及通过无状态并行执行的扩展性。我们实现了可重用的设计模式并在Agentics 2.0的程序上进行了评估，包括用于数据驱动发现的DiscoveryBench和用于自然语言到SQL语义解析的Archer等具有挑战性的基准测试，展示了最先进的性能。
URL
https://arxiv.org/abs/2603.04241
