The rapid advancement of large language models (LLMs) has empowered
intelligent agents to leverage diverse external tools for solving complex
real-world problems. However, as agents increasingly depend on multiple tools,
they encounter new challenges: extended contexts from disparate sources and
noisy or irrelevant tool outputs can undermine system reliability and accuracy.
These challenges underscore the necessity for enhanced stability in agent-based
systems. To address this, we introduce dynamic supervision and maneuvering
mechanisms, constructing a robust and dynamic Multi-Agent System (MAS)
architecture within the AWorld framework. In our approach, the Execution Agent
invokes the Guard Agent at critical steps to verify and correct the reasoning
process, effectively reducing errors arising from noise and bolstering
problem-solving robustness. Extensive experiments on the GAIA test dataset
reveal that our dynamic maneuvering mechanism significantly improves both the
effectiveness and stability of solutions, outperforming single-agent system
(SAS) and standard tool-augmented systems. As a result, our dynamic MAS system
achieved first place among open-source projects on the prestigious GAIA
leaderboard. These findings highlight the practical value of collaborative
agent roles in developing more reliable and trustworthy intelligent systems.