backlog-loop

To stop AI agents from marking incomplete tasks as done, use a verification loop that requires independent execution and Judge-based approval before updating backlog state. This enforces a skeptical review gate, preventing unsupported completion claims during automated code workflows.

A judge loop in AI-assisted software development is an iteration cycle where a skeptical sub-agent verifies implementation work before task completion. It evaluates execution results, provides feedback on failures, and gates task approval to ensure attributed writes meet defined verification standards.

To automate a single backlog task from implementation to completion, configure an AI agent to execute a prioritized item, run verification checks, and iterate based on Judge feedback. This bounded loop handles failures and maintains task history until completion criteria are met.

Yes, automated task verification loops require backlog task management commands, attributed writes, and verification execution capabilities. These commands allow the AI agent to pick prioritized tasks, apply changes, execute checks, and maintain recovery paths throughout the workflow.

When a verification check fails during an AI agent task loop, the system iterates based on Judge feedback to correct the implementation. This cycle continues until the task meets completion criteria or reaches a bounded failure state, preserving task history and recovery paths.

Backlog-loop handles task attribution and history by enforcing attributed writes and maintaining backlog state throughout the implementation and verification cycle. This ensures autonomous AI coding workflows preserve recovery paths and task lineage while clearing well-defined backlog items.