Hello everyone, today I continue to share Google expert Antonio Gulli’s “Agentic Design Patterns”. Today is Chapter 7: “Multi-Agent Collaboration”.
When a single AI cannot solve complex problems (such as needing market analysis, copywriting, and design layout simultaneously), a multi-agent system can decompose tasks and have multiple specialized agents collaborate to complete them.
There are three main modes of agent collaboration:
Sequential handoff: like an assembly line, where the researcher → writer → editor agents relay work;
Parallel processing: multiple agents simultaneously gather data such as weather and news;
Hierarchical management: a manager agent dynamically allocates tasks, coordinating the team like a “project manager”.
This model has been applied in:
Software development: collaboration among requirement analysis, coding, and testing agents to speed up the process;
Financial analysis: joint decision-making by data scraping, sentiment analysis, and investment recommendation agents;
Customer support: basic issues handled by foundational agents, while complex issues are automatically referred to expert agents.
Frameworks like Crew AI and Google ADK support the rapid construction of multi-agent systems. For example, by simply defining agent dependencies in code, an automated blog creation pipeline can be established.
Multi-agent collaboration upgrades AI from “going solo” to an efficient team, becoming a powerful tool for solving complex problems!