用代数方法构建可解释的智能数据流程，提升可靠性与可扩展性。
Agentics 2.0: Logical Transduction Algebra for Agentic Data Workflows
- 将大模型调用视为带类型的语义变换，确保数据结构正确
- 支持并行异步执行，在两个基准上达到顶尖性能
- 适合需要可解释性和高可靠性的企业级AI应用
智能体式AI正从研究原型快速转向企业部署，对可靠性、可扩展性和可观测性等软件质量属性提出更高要求。我们提出Agentics 2.0，一个轻量级、原生Python的框架，用于构建高质量、结构化、可解释且类型安全的智能体数据工作流。其核心是逻辑转导代数，将大语言模型推理调用形式化为带类型的语义转换，称为可转导函数，强制保证模式有效性与证据局部性。这些函数通过代数基操作组合成更大程序，并以无状态异步调用方式在异步Map-Reduce程序中并行执行。该框架通过强类型实现语义可靠性，通过输入输出槽位间的证据追踪实现语义可观测性，并通过无状态并行执行实现可扩展性。我们实例化了可复用的设计模式，并在多个挑战性基准上评估，包括用于数据驱动发现的DiscoveryBench和用于自然语言到SQL解析的Archer，均展现出最先进性能。
原文摘要 · Abstract (English)
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.
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