Glossary

Multi-agent System

A multi-agent system is a computational framework composed of multiple autonomous AI agents that interact within a shared environment to achieve complex objectives. These agents operate independently, utilizing specialized capabilities to process information, negotiate tasks, and coordinate actions, enabling the system to solve problems that exceed the capacity of a single monolithic model.

Multi-agent systems represent a shift from monolithic AI applications toward modular, decentralized architectures. By decomposing large, multifaceted workflows into smaller, domain-specific tasks, enterprises can improve system maintainability, scalability, and performance. This approach is increasingly relevant as organizations move beyond simple chatbots toward autonomous workflows that require cross-functional reasoning. The modular nature of these systems allows for the integration of diverse models and tools, providing a robust foundation for managing complex, long-running business processes that demand high levels of reliability and specialized expertise.

In practice, these systems rely on defined communication protocols and orchestration layers to manage agent interactions. Practitioners must design clear interfaces, state management strategies, and conflict resolution mechanisms to ensure coherent output. Key considerations include defining agent roles, establishing boundaries for autonomy, and implementing rigorous governance to monitor inter-agent dependencies. Successful implementation requires a focus on message passing, shared context management, and the ability to audit decision-making paths, ensuring that the collective behavior of the agents remains aligned with the intended operational goals.

Last updated: 2026-08-25