Overview

LoopX is a provider-neutral, stateful control plane designed for loop engineering – the orchestration of long-horizon AI agent tasks. It acts as a robust intermediary, running on top of various agent harnesses (like Codex, Claude Code, Cursor, and others) rather than replacing them. LoopX ensures that complex, multi-stage AI operations remain durable, governed, and auditable, providing essential capabilities like state management, semantic decision-making, error recovery, and seamless human-agent collaboration.

Key Features

• Long-Horizon State Management: Preserves critical context, objectives, evidence, and progress across extended agent sessions, restarts, and harness changes. Your work doesn't get lost in chat history.
• Provider-Neutrality: Works with a variety of agent harnesses and backends, offering flexibility and avoiding vendor lock-in.
• Local-First Control Plane: Maintains durable state in a local workspace, ensuring data privacy and offline resilience.
• Governed Execution: Implements objectives, gates, todos, evidence tracking, and quota management to ensure work is performed predictably and safely.
• Human-Agent Collaboration: Facilitates clear handoffs, reviews, and confirmations, keeping humans in the loop for critical decisions.
• Recovery & Resilience: Enables restarting complex tasks from where they left off, even after failures or interruptions.
• Personal Agent Workspace: Provides a unified dashboard to manage goals, tasks, conversations, and file-based work across different agent sessions.

Typical Use Cases

LoopX is ideal for scenarios requiring sustained, multi-step AI agent activity where state persistence, governance, and auditability are paramount:

• Multi-Day Engineering, Research, and Benchmarking: Orchestrate complex experiments, code refactors, or benchmark runs that span days and involve multiple agent turns.
• Issue and Pull Request (PR) Lifecycles: Manage the entire process of addressing bugs or implementing features, preserving scope, review status, and accumulated evidence.
• Recurring Monitoring and Maintenance Tasks: Automate and govern long-running observational or upkeep workflows.
• Projects with Strict Gating and Review: Implement workflows with owner approvals, safety checks, publication gates, or sensitive data handling requirements.
• Peer Agent Team Coordination: Facilitate collaboration between multiple AI agents, managing ownership, task leases, and handoffs effectively.
• Legible Creator, Research, and Operations Workflows: Ensure progress on complex projects remains transparent and understandable to non-technical operators.