Martin-loop is a control plane for AI coding agents that enforces safety, budget, and policy constraints at scale.
The tool addresses the challenge of running autonomous AI coding agents across multiple repositories without losing visibility or control. It works by intercepting agent work assignments, applying spend caps and policy rules before execution, monitoring outputs for correctness, and providing rollback capabilities when failures occur. The system maintains an audit trail of agent activity and learns from execution loops to improve future decisions. This approach lets teams deploy AI agents to handle coding tasks while maintaining governance over cost, quality, and compliance.
Teams should adopt this tool if they plan to run multiple AI coding agents autonomously across their codebase and need to enforce spending limits, policy compliance, and output verification. It suits organizations that want to prove return on investment from AI agents by tracking their performance across repositories. The tool integrates with GitHub Actions and supports agents built on Claude Code and similar frameworks. It is designed specifically for the problem of scaling AI agents safely rather than for single-agent or manually-supervised workflows.
The project shows active development with regular commits addressing core functionality. Work is distributed across multiple areas including agent runtime improvements, policy enforcement mechanisms, and observability features. The codebase demonstrates ongoing refinement of the control plane's core capabilities rather than exploration of tangential features.
