How to build a simple multi-agentic system using Google’s ADK

In this foundational architecture guide published on the Google Cloud Blog, I break down why scaling enterprise AI requires moving away from massive, single-prompt monolithic scripts and moving toward granular networks of specialized agents.

Using the Google Agent Development Kit (ADK), developers can build complex AI systems that model human organizational structures—breaking down massive problems into isolated, high-fidelity tasks.

Core Concepts and the Travel Orchestration Blueprint:

• The Problem with Monoliths: Brittle system designs try to force a single model instance to execute entirely different logic branches simultaneously, leading to high token latency, context confusion, and frequent silent tool parsing failures.
• Building Isolated Specialists: Using the ADK LlmAgent class running on gemini-2.0-flash, the guide demonstrates how to instantiate dedicated specialist nodes with hard boundaries. In a practical travel scenario, this involves defining an isolated FlightAgent (focused strictly on valid flight JSON returns), a HotelAgent (an expert in accommodations), and a SightseeingAgent (acting as a local tour guide).
• The Coordinator Architecture (TripPlanner): To manage these specialists efficiently, the framework establishes a top-level root agent acting as a centralized coordinator. By defining the sub_agents=[flight_agent, hotel_agent, sightseeing_agent] parameter list, the TripPlanner root node handles initial intent routing.
• Overcoming Delegation Transfer: The article addresses a critical engineering caveat: when responsibility is fully transferred to a sub-agent, the top-level orchestrator can fall out of the execution loop for complex multi-step user prompts (e.g., “Book a flight and then find a hotel”). I explore how to implement advanced orchestration frameworks over simple sub-agent routing to ensure long-term, multi-step conversation context tracking remains stable.