Ruiteng Huang creates LoopX, a local control plane for long-running agent work. He focuses on keeping goals, decisions and evidence usable across sessions and runtimes so a continuing agent workflow can be inspected and redirected.[1]
Start with LoopX →
Professional X account Contributions
Keep ownership and evidence with the goal
LoopX stores objectives, acceptance criteria, decisions, ownership and evidence while an existing agent runtime supplies models and tools. Claims, leases and task boundaries determine who acts next. A board displays that state, but durable records remain the source of truth. This separates continuing coordination from any one provider’s execution loop.[2]
Inspect progress before continuing
Its research workspace exposes delivered artifacts, supporting and refuting evidence, costs and decisions. Protected actions still depend on human authorization. The project describes early long-duration trajectories as owner-run examples spanning work, waits and recovery, rather than proof of uninterrupted autonomous execution.[2]
Ideas
Huang argues that finite model context calls for external state in long-running work. His LoopX introduction emphasizes state management, supervision and planning, with human feedback able to change the continuing process. The goal is to preserve the task and its decisions beyond any one conversation.[1]
Tools, writing & talks
Tools
Start here
LoopX
github.comA local-first control plane preserving goals, ownership, decisions and evidence across agent sessions.
Read the explanation of how LoopX fits around an agent runtime, then choose a small goal with an observable result. Setup requires the documented Python and Node versions plus a supported agent host.
- LoopXgithub.com
Agent-agnostic state kernel tracking objectives, gates, todos, and evidence for long-running agent work across runtimes.
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