Multi-Agent System

Some complex tasks are difficult for a single AI agent to handle. For example, assigning code generation, testing, and review to one agent in software development causes context bloat and quality degradation. Multi-agent systems address this through specialization and division of labor.

In a typical architecture, an orchestrator creates the overall plan and delegates subtasks to specialized agents such as a coder, reviewer, and tester. Each agent acts on its own judgment and queries other agents as needed. Standards like the A2A (Agent-to-Agent) protocol are being developed, making it practical to connect agents built on different frameworks.

The key design consideration is the communication pattern between agents. Serial pipeline patterns are easier to debug, while parallel fan-out patterns offer better throughput. In practice, starting with a small configuration of 2-3 agents and expanding once bottlenecks become visible is less likely to fail.

As a caveat, adding more agents increases latency and API costs. The principle of "just enough" agents applies.