Multi-Agent AI Systems: Architecture & Design Patterns

Why Multi-Agent?

Single agents hit capability limits on complex tasks. Multi-agent systems distribute work across specialized agents: a researcher agent finds information, a writer agent creates content, a critic agent reviews quality, and a coordinator agent manages the workflow.

This mirrors how human teams work — specialization and collaboration produce better results than any individual.

Communication Patterns

Hub-and-Spoke: A coordinator agent assigns tasks and collects results. Simple, reliable, but bottlenecked by coordinator.

Peer-to-Peer: Agents communicate directly. More flexible but harder to debug.

Blackboard: Agents read/write to a shared knowledge base. Best for iterative refinement.

Hierarchical: Agents organized in teams with team leads. Best for large-scale systems.

Task Delegation

Effective delegation requires: clear role definitions (what each agent can and should do), capability awareness (coordinator knows agent strengths), context passing (agents share relevant context without overwhelming), and result validation (output quality checking before accepting).

Anti-pattern: Having all agents use the same prompt template. Each agent should have distinct instructions optimized for its role.

Conflict Resolution

When agents disagree (e.g., critic rejects writer's output), you need resolution strategies: majority voting (multiple agents evaluate), hierarchical override (senior agent decides), iterative refinement (agents negotiate through revisions), and human escalation.

Set maximum iteration limits to prevent infinite loops of revision and criticism.

Production Tips

Log every inter-agent message for debugging. Set budgets per agent (token limits, time limits). Use different models for different roles — GPT-5 for creative agents, Claude 4 for analytical agents, fast models for simple routing agents.

Explore the best models for multi-agent systems on Vincony.com.