Multi-Agent collaboration patterns with Strands Agents and Amazon Nova

Multi-agent generative AI systems use multiple specialized AI agents working together to handle complex, multi-faceted tasks that exceed the capabilities of any single model. By combining agents with different skills or modalities (for example, language, vision, audio, video), these systems can tackle tasks in parallel or sequence, yielding more robust results. Recent research shows that multi-agent collaboration can significantly improve success rates on complex goals (up to 70% higher vs. single-agent approaches). There are different patterns for such multi-agent collaborations. Whether it’s a manager-agent delegating specialized tasks (Agent as tools), a swarm of brainstormers (Swarms), a carefully wired graph of expert agents (Agent Graph), or a structured pipeline (agent workflow), the right design pattern combined with the right tooling will significantly enhance the system’s effectiveness.

The challenge with multi-agent systems, however, lies in their computational demands. Modern multi-agent applications can issue thousands of prompts per user request as ...