Building a Multi-Agent AI System — Part 1: Architecture Decisions

This is Part 1 of a series on building production multi-agent AI systems. Each part covers a critical aspect: architecture, coordination, testing, and deployment. Why Multi-Agent? Single AI agents hit a ceiling. They can't: Specialize in multiple domains simultaneously Process tasks in parallel efficiently Self-correct through peer review Multi-agent systems solve this by assigning specialized roles to different agents and letting them collaborate. Architecture Patterns Pattern 1: Hub and Spoke ┌───────────┐ │ Supervisor│ └─────┬─────┘ ┌────────┼────────┐ ▼ ▼ ▼ ┌────────┐┌────────┐┌────────┐ │Research││Analyst ││Writer │ └────────┘└────────┘└────────┘ A supervisor routes tasks and aggregates results. Pros : Simple, controlled. Cons : Single point of failure, bottleneck. Pattern 2: Peer-to-Peer ┌────────┐ ←→ ┌────────┐ │Agent A │ │Agent B │ └────┬───┘ └───┬────┘ │ ←→ │ └──┬───────────┘ ▼ ┌────────┐ │Agent C │ └────────┘ Agents communicate directly. Pros : No bottleneck, resilient. Cons