This framework provides an operational blueprint and patterns for high-quality, AI-assisted software development, offering a canonical guide and a lightweight workflow for repeatable, auditable, and architecture-first delivery. It targets teams using AI coding tools, enabling safer delivery of real software through a shared contract, state machine, and documentation patterns that prioritize reuse, explicit approval gates, and architectural alignment.

How It Works

AGENT-ZERO employs a structured state machine workflow: PLAN → BUILD → DIFF → QA → APPROVAL → APPLY → DOCS. Its core principles emphasize "Reuse over creation," "Approval gates" (no changes without consent), "Architecture-aligned changes" (following patterns, citing sources), and "High signal context" managed via a Memory Bank. The Memory Bank is a curated, persistent set of documentation (project brief, patterns, context, decisions) that agents read from and write to sparingly, citing it in plans and diffs. A Compaction Protocol allows context compression at any time, with automatic state persistence to the Memory Bank for seamless recovery.

Quick Start & Requirements

The workflow involves a sequence of commands and interactions with compatible AI agents (Claude, Cursor, Copilot, Cline, Aider). Key commands include startup, BUILD, QA, Document it. Update the memory bank., and /compact. The primary resource is the AGENTS.md specification, which details the workflow, Memory Bank structure, quality standards, and practical boot sequences. Compatibility with AI agents that can follow the AGENTS.md specification is required.

Highlighted Details

• Compatibility: Works with Claude, Cursor, Copilot, Cline, Aider, and other AGENTS.md-compatible tools.
• Core Principles: Adheres to "Four Sacred Rules": no new files without reuse analysis, no rewrites when refactoring is possible, no generic advice (cite file:line/MB), and no ignoring existing architecture.
• Workflow: Features explicit approval gates at multiple stages, including human approval before applying changes.
• Task Management: Utilizes a "Task Contract" to define context, outcomes, constraints, and budgets (cycles, tokens, minutes).
• Quality Gates: Mandates tests passing, linter clean, coverage thresholds met, and security review before proceeding past the APPROVAL state.

Maintenance & Community

The project is sponsored via GitHub Sponsors. No explicit community channels (e.g., Discord, Slack) or detailed roadmap information are provided in the README. Contributions are welcome, following the established state machine and approval gates.

Licensing & Compatibility

No specific open-source license is mentioned in the provided README content. This absence of a stated license may pose compatibility concerns for commercial use or integration into closed-source projects.

Limitations & Caveats

AGENT-ZERO functions as a methodology and a guide (AGENTS.md) rather than a standalone executable tool. Its effectiveness is contingent upon the AI agent's adherence to the specification and the quality of user-provided input. The workflow involves manual interaction and explicit user approval at multiple stages, requiring active user engagement throughout the development process.