11-Agent System: Claude-Based Multi-Agent Workflow Orchestration and MiniMax M2.7 Auto-Assignment System

The 11-Agent System is a v2.0 workflow orchestration system with 11 Claude agents, integrated with the MiniMax M2.7 model to achieve intelligent automatic task assignment, exploring the application paradigm of multi-agent collaboration in complex workflow processing. The system addresses the limitations of single agents through role division, combining Claude's general capabilities with MiniMax's assignment intelligence to improve the efficiency of complex task processing.

With the breakthroughs in large language model capabilities, single agents face challenges such as context window limitations, scattered professional capabilities, high task switching costs, and risk of error propagation. The 11-Agent System is built based on this insight, enabling efficient completion of tasks in complex business scenarios through multi-agent collaboration.

The multi-agent architecture ensures professional depth and overall processing capability through role division. The 11 Claude agents are presumed to cover key links such as requirement analysis, planning, research, coding, review, documentation, coordination, execution, verification, optimization, and reporting, addressing the limitations of single agents.

MiniMax M2.7 (developed by Xiyu Technology) acts as an intelligent scheduler, addressing the core challenges of multi-agent task assignment. Its functions include task understanding, capability matching, dynamic assignment, and exception handling; an example of the assignment process: coordination agent receives the request → MiniMax analyzes the task → planning agent formulates a plan → sub-agents execute → review → report and integrate results.

Key technical points of the system: 1. Inter-agent communication protocol (clear message format, state synchronization, error handling); 2. State management and persistence (tracking execution status, intermediate results, supporting resumption from breakpoints); 3. Concurrency and resource scheduling (reasonable allocation of computing resources, optimizing API costs and response speed).

Application scenarios of the system: 1. Complex software development (covering the full lifecycle from requirement analysis to document writing); 2. Research task processing (multi-step information collection and synthesis); 3. Content production pipeline (complete process from topic selection to review and publication).

The 11-Agent System demonstrates the potential of multi-agent architecture, combining Claude and MiniMax to achieve stronger task processing capabilities. Future outlook: Agents learn from each other and evolve dynamically, forming an intelligent collaboration ecosystem, providing a reference for exploring the application boundaries of AI agents.