Researchers have introduced a formal theory for agent harness engineering using categorical architecture, specifically the (G, Know, Phi) triple from the ArchAgents framework. This formalization provides a structured approach to designing, composing, and comparing LLM-based agent frameworks. The proposed method maps key agent components like memory and skills to the triple's elements and ensures structural guarantees through a compiler that checks identity and verifier replay, rather than output correctness. A reference implementation demonstrates the preservation of these guarantees across multiple popular agent frameworks, including LangGraph, Swarms, DeerFlow, and Ralph. AI
影响 Provides a formal theory for building and comparing LLM agent frameworks, potentially improving reliability and interoperability.
排序理由 Academic paper introducing a formal theory for LLM agent harness engineering. [lever_c_demoted from research: ic=1 ai=1.0]
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