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

Key points

The paper formalizes a paradigm shift in AI engineering from prompt and context engineering to harness engineering. As model capabilities converge, the harness layer becomes the main site of architectural differentiation.
DAG Teams proposes a two-phase hybrid orchestration approach in which the LLM dynamically generates a task graph (DAG) and a deterministic scheduler executes it, achieving both flexibility and execution traceability.
PermissionBridge is a behavioral safety system that performs runtime authorization checkpoints before high-risk actions such as file edits, external API calls, and code execution. It is a native runtime primitive, not a tool-level wrapper.
We introduce a hierarchical memory architecture for three-tier context management, consisting of compressed working memory, retrieval-based external memory, and SOUL.md anchor persona partitions.
Agentic Wiki externalizes task-derived knowledge into a Markdown-file-based user-owned knowledge base, creating a personal knowledge infrastructure that future agent sessions can retrieve and use.
In LangChain experiments, keeping the model fixed and improving only the harness increased task completion from 52.8% to 66.5%, a gain of 13.7 percentage points, showing that harness design is a major determinant of agent performance.

External research summaries. These are not HDATF publications or measured product results.