Multi-Agent collaboration patterns with Strands Agents and Amazon Nova

This article explores multi-agent collaboration patterns using Amazon Nova and the open-source Strands Agents SDK, demonstrating how multiple specialized AI agents can work together to solve complex tasks more effectively than single agents.

• Multi-agent systems can improve success rates up to 70% higher than single-agent approaches on complex goals
• Amazon Nova provides ideal foundation for multi-agent systems: 200+ tokens/second throughput, ultra-low costs, and consistent structured outputs
• Four key collaboration patterns: Agents as Tools (hierarchical manager-expert structure), Swarms (decentralized peer agents), Agent Graphs (structured network with directed connections), and Workflows (predefined sequential task execution)
• Agents as Tools: Orchestrator delegates to specialized sub-agents; best for distinct subtasks requiring different expertise
• Swarms: Peer agents exchange ideas iteratively with no central controller; ideal for brainstorming and complex reasoning
• Agent Graphs: Explicitly defined network topology with controlled information flow; suits complex multi-stage processes and organizational hierarchies
• Workflows: Directed acyclic graph of tasks with clear dependencies; excellent for content generation pipelines and monitored processes
• Strands SDK provides lightweight Python-based orchestration framework with @tool decorator, GraphBuilder, and built-in Bedrock integration

Combining Amazon Nova's cost-efficiency and throughput with Strands' orchestration capabilities enables production-grade multi-agent systems that scale from prototypes to enterprise applications while maintaining speed, accuracy, and cost control.