[2603.04241] Agentics 2.0: Logical Transduction Algebra for Agentic Data Workflows

Computer Science > Artificial Intelligence arXiv:2603.04241 (cs) [Submitted on 4 Mar 2026] Title:Agentics 2.0: Logical Transduction Algebra for Agentic Data Workflows Authors:Alfio Massimiliano Gliozzo, Junkyu Lee, Nahuel Defosse View a PDF of the paper titled Agentics 2.0: Logical Transduction Algebra for Agentic Data Workflows, by Alfio Massimiliano Gliozzo and 2 other authors View PDF HTML (experimental) 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 pat...