AI Agent Workstation: A Local Workflow Framework for Multi-Agent Collaboration

AI Agent Workstation is a local multi-agent collaboration workspace designed to address core pain points in multi-agent collaboration, such as context management, task handover, quality control, and memory management. Through a hierarchical directory structure and Markdown file transfer mechanism, it enables the collaboration between Hermes (coordination and review) and Codex (execution and implementation), supports quality gates (MCP auxiliary layer) and knowledge precipitation, and provides a structured collaboration solution for complex development scenarios.

With the improvement of large model capabilities, multi-agent collaboration to complete complex tasks has become a trend, but there are four major pain points:

1. Context Management: Ensure each agent gets necessary information without overload;
2. Task Handover: Efficiently transfer results to ensure execution consistency;
3. Quality Control: Establish an effective review mechanism;
4. Memory Management: Extract reusable experience. AI Agent Workstation is designed for this purpose, providing a structured local workspace to support multi-agent collaboration according to protocols.

Hierarchical Directory Structure

• templates/: General reusable templates;
• tasks/: Codex task handover files;
• results/: Codex execution outputs;
• reviews/: Hermes review files;
• memory/: Knowledge precipitation;
• workflows/: Multi-agent workflow definitions.

Core Collaboration Roles

• Hermes: Planning, coordination, review, context and memory management;
• Codex: Code editing, testing, building, and result reporting;
• Human: Product direction, approval, final decision-making (AI-assisted rather than replacement).

Markdown File Transfer Flow

Agents hand over via structured Markdown files, with the flow: AGENT_CONTEXT.md → CODEX_TASK.md → AGENT_RESULT.md → AGENT_REVIEW.md → MEMORY_CANDIDATES.md Each file carries context, task definition, execution result, review comments, and knowledge candidates respectively.

MCP Auxiliary Layer (Optional)

When my-agents-mcp is available, it provides artifact template verification and quality gates:

• Verify CODEX_TASK.md before execution;
• Verify AGENT_RESULT.md before review;
• Verify AGENT_REVIEW.md before response;
• Verify MEMORY_CANDIDATES.md before memory precipitation. MCP does not replace the core Markdown workflow; it is only an auxiliary tool.

AI Agent Workstation适用于以下场景：

1. Complex Feature Development: Hermes is responsible for architecture design, Codex implements modules;
2. Code Refactoring: Hermes formulates strategies, Codex executes refactoring, Hermes reviews results;
3. Test-Driven Development: Hermes defines test cases, Codex implements code and runs tests;
4. Knowledge Precipitation: Systematically record best practices and lessons learned via MEMORY_CANDIDATES.md.

The project follows strict security and privacy principles:

1. Do not store sensitive information in memory candidates, templates, or other files;
2. Pause before executing destructive commands or external integrations, waiting for human confirmation;
3. Keep workflow artifacts transparent and avoid hidden automation logic. These principles ensure the security and controllability of AI collaboration.

AI Agent Workstation represents a structured multi-agent collaboration methodology, not just simple agent chat. It achieves effective collaboration through clear role division, standardized file transfer mechanisms, and strict quality gates. It provides a well-thought-out reference framework for teams looking to integrate AI agents into their development workflows, proving that AI collaboration requires strong model capabilities plus clear protocols and governance mechanisms. Project address: https://github.com/agenticnoob/ai-agent-workstation