How to Build Multi-Agent Architectures with Google ADK

Why Single Agents Don't Scale Well Whenever we’re prototyping an AI application, we begin by creating a single agent that’s in charge of everything, from understanding the request, planning, calling tools, and generating the final response. This is fine for a simple use case, as this is easy to implement. However, as complexity grows, the agent must manage a lot of different tasks, like validation and multiple tool calls in the same iteration loop. This can make context difficult to debug and understand because everything is happening inside a single model call. This would mean adding more instructions and tools to our agent that don’t actually fix the structural issue. This increases the complexity and unpredictability of our agent. While single-agent systems are excellent for prototypes, we require multi-step workflows that actually separate responsibilities and are more reliable. This is where multi-agent systems shine and become a more scalable solution in the long term. What are M