Building a Multi-Agent System Using Google’s Agent Development Kit
In modern AI system design, we’re moving away from monolithic, single-purpose models and toward modular, multi-agent architectures — systems composed of specialized components that can reason, collaborate, and delegate.
Recently, I explored this concept hands-on using Google’s latest Agent Development Kit, and built a multi-agent architecture consisting of four agents — one Parent Agent and three specialized sub-agents:
- Recommendation Agent
- Search Agent
- Completion Agent
Each agent is equipped with its own tools for retrieving and updating data in a connected database.
In this post, I’ll walk through the concept, architecture, and data flow — using the analogy of an intelligent control tower system to explain how this architecture functions in real-world scenarios.
The Analogy: An Intelligent Control Tower
Think of the Parent Agent as the control tower of an airport.
Every incoming request is like an aircraft asking for landing instructions.
The tower (Parent Agent) doesn’t fly the planes itself — it analyzes the situation, determines the type of aircraft, and assigns it to the appropriate runway or terminal (sub-agent).
If an unexpected event occurs — say, the assigned runway can’t handle that aircraft — the control tower steps back in, reassesses, and redirects it appropriately.
This analogy reflects how modular agent orchestration enables adaptive and context-aware decision-making.
Architecture Overview
The system is composed of four main agents:
Parent Agent — The Orchestrator
- Acts as the entry point for all incoming user requests.
- Performs intent classification and task routing.
- Delegates work to specialized sub-agents.
- Handles re-delegation or fallback responses when needed.
The Parent Agent’s logic acts like a router combined with a context analyzer, determining which sub-agent should take ownership of the request.
Recommendation Agent — Contextual Insights
This agent specializes in generating context-based recommendations — such as suggesting items, actions, or next steps.
It uses internal reasoning plus a custom-built tool that retrieves relevant data from the database to make informed suggestions.
Example use cases:
- “Show me trending items.”
- “What are the top-rated restaurants nearby?”
Search Agent — Information Retrieval
This agent functions as a retrieval pipeline, focusing purely on fetching structured data with high precision, using database-integrated search tools.
Example queries:
- “Find restaurant details by city.”
- “Get all entries with a 5-star rating.”
Completion Agent — Task Executor
The Completion Agent is responsible for executing create, update, and completion-based tasks.
Whenever a query involves action rather than retrieval (like inserting or updating data), the Parent Agent delegates it here.
Example tasks:
- “Add this restaurant to the database.”
- “Update the dish price.”
Workflow: Intelligent Delegation
Here’s how a request moves through the system:
Request Entry:
Every request enters via the Parent Agent.Intent Classification:
The Parent Agent analyzes the intent using reasoning or classification logic.Delegation:
It assigns the task to the appropriate sub-agent — Recommendation, Search, or Completion.Execution:
The selected sub-agent performs the task using its specific toolset.Re-routing:
If a sub-agent encounters a query outside its scope, it escalates the request back to the Parent Agent.Fallback:
If no suitable agent exists, the Parent Agent provides a default response.
This process ensures autonomy, modularity, and fault tolerance, enabling the system to adapt dynamically based on the context of the request.
Tooling Layer: Data Access and Actions
Each sub-agent has access to custom tools that interact with the backend database.
Common operations include:
- Data retrieval (
get,findByField, etc.) - Data modification (
insert,update,delete) - Validation and error handling
These tools enable agents to perform real operations, not just generate text.
For example:
“Add a new restaurant to the list.”
The Parent Agent identifies it as a task completion request → delegates to the Completion Agent → which triggers the database insertion tool → and returns a confirmation message.
Why Multi-Agent Design?
The multi-agent approach offers several engineering benefits:
✅ Modularity — Each agent focuses on a specific domain, simplifying debugging and scaling.
✅ Reusability — Agents can be reused or extended for different applications.
✅ Scalability — New agents can be integrated without disturbing existing logic.
✅ Context Isolation — Each agent handles its domain-specific tasks, improving accuracy and reducing confusion.
Compared to a single monolithic agent, this design allows for better adaptability, maintainability, and distributed reasoning.
Takeaways
Implementing this architecture with Google’s Agent Development Kit highlighted how structured delegation transforms conversational AI into a cooperative, context-aware framework.
Instead of one model doing everything, we now design ecosystems of intelligent agents that collaborate effectively — much like distributed microservices in a backend architecture.
The result is a system that’s:
- Autonomous
- Modular
- Scalable
- Context-aware
Final Thoughts
As AI continues to evolve, the distinction between “chatbots” and “autonomous systems” is fading.
The true challenge now lies in orchestrating intelligence — designing agents that can reason, delegate, and cooperate efficiently.
By combining modular architectures with tools like Google’s Agent Development Kit, we’re moving toward the era of composable AI systems — where intelligence is not centralized but distributed, dynamic, and domain-aware.
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