TL;DR
SemaClaw introduces a comprehensive framework for developing general-purpose personal AI agents, emphasizing harness engineering, safety, and collaborative multi-agent orchestration.
Contribution
It presents novel infrastructure components like DAG-based orchestration, PermissionBridge safety, and a multi-tier context architecture for personal AI agents.
Findings
Effective multi-agent orchestration with DAG-based method
Enhanced safety through PermissionBridge system
Automated personal knowledge base construction
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
