SemaClaw: A Step Towards General-Purpose Personal AI Agents through Harness Engineering

Fuente: arXiv
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Autori principali: Zhu, Ningyan, Wang, Huacan, Zhou, Jie, Chen, Feiyu, Zhang, Shuo, Chen, Ge, Liu, Chen, Wu, Jiarou, Chen, Wangyi, Mou, Xiaofeng, Xu, Yi
Natura: Preprint
Pubblicazione: 2026
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author Zhu, Ningyan
Wang, Huacan
Zhou, Jie
Chen, Feiyu
Zhang, Shuo
Chen, Ge
Liu, Chen
Wu, Jiarou
Chen, Wangyi
Mou, Xiaofeng
Xu, Yi
author_facet Zhu, Ningyan
Wang, Huacan
Zhou, Jie
Chen, Feiyu
Zhang, Shuo
Chen, Ge
Liu, Chen
Wu, Jiarou
Chen, Wangyi
Mou, Xiaofeng
Xu, Yi
contents The rise of OpenClaw in early 2026 marks the moment when millions of users began deploying personal AI agents into their daily lives, delegating tasks ranging from travel planning to multi-step research. This scale of adoption signals that two parallel arcs of development have reached an inflection point. First is a paradigm shift in AI engineering, evolving from prompt and context engineering to harness engineering-designing the complete infrastructure necessary to transform unconstrained agents into controllable, auditable, and production-reliable systems. As model capabilities converge, this harness layer is becoming the primary site of architectural differentiation. Second is the evolution of human-agent interaction from discrete tasks toward a persistent, contextually aware collaborative relationship, which demands open, trustworthy and extensible harness infrastructure. We present SemaClaw, an open-source multi-agent application framework that addresses these shifts by taking a step towards general-purpose personal AI agents through harness engineering. Our primary contributions include a DAG-based two-phase hybrid agent team orchestration method, a PermissionBridge behavioral safety system, a three-tier context management architecture, and an agentic wiki skill for automated personal knowledge base construction.
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id arxiv_https___arxiv_org_abs_2604_11548
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle SemaClaw: A Step Towards General-Purpose Personal AI Agents through Harness Engineering
Zhu, Ningyan
Wang, Huacan
Zhou, Jie
Chen, Feiyu
Zhang, Shuo
Chen, Ge
Liu, Chen
Wu, Jiarou
Chen, Wangyi
Mou, Xiaofeng
Xu, Yi
Artificial Intelligence
The rise of OpenClaw in early 2026 marks the moment when millions of users began deploying personal AI agents into their daily lives, delegating tasks ranging from travel planning to multi-step research. This scale of adoption signals that two parallel arcs of development have reached an inflection point. First is a paradigm shift in AI engineering, evolving from prompt and context engineering to harness engineering-designing the complete infrastructure necessary to transform unconstrained agents into controllable, auditable, and production-reliable systems. As model capabilities converge, this harness layer is becoming the primary site of architectural differentiation. Second is the evolution of human-agent interaction from discrete tasks toward a persistent, contextually aware collaborative relationship, which demands open, trustworthy and extensible harness infrastructure. We present SemaClaw, an open-source multi-agent application framework that addresses these shifts by taking a step towards general-purpose personal AI agents through harness engineering. Our primary contributions include a DAG-based two-phase hybrid agent team orchestration method, a PermissionBridge behavioral safety system, a three-tier context management architecture, and an agentic wiki skill for automated personal knowledge base construction.
title SemaClaw: A Step Towards General-Purpose Personal AI Agents through Harness Engineering
topic Artificial Intelligence
url https://arxiv.org/abs/2604.11548