Multi-agent systems: Frameworks & step-by-step tutorial

Multi-agent systems, comprised of specialized AI agents, can outperform single-agent systems by efficiently handling tasks across multiple domains, although they require more tokens and complex coordination. These systems, which often use communication protocols, shared memory, and orchestration logic, allow for parallel processing and modular updates, reducing deployment risks and improving reliability. However, they also present challenges such as increased coordination overhead, higher token costs, potential quality drift, and security vulnerabilities, particularly in client-facing applications. Practical applications include customer support, deep research, software development, data analytics, and content creation, utilizing various coordination patterns like handoff, parallel execution, and sequential refinement. Building these systems can be facilitated through visual builders like n8n, which combines visual workflow design with code capabilities, and code-first frameworks that offer detailed control, depending on the specific needs and expertise of the development team.

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