Why Multi-Agent Systems?
AI Agents are powerful. But if a task becomes complex, asking a single agent to keep track of all of the instructions can be problematic. It gets hard to debug and maintain, plus it can produce unreliable results.
Instead of one “do-it-all” agent, we can build a multi-agent system, where the team is comprised of multiple (simpler) specialized agents that work together. In these teams, each agent has a clear job to perform (e.g. Agent A does research, Agent B summarizes the research).
In a way, opting for a multi-agent system is similar to writing modular code. We tend to prefer modular code because it’s easier to test, maintain, and reason about.
Implementation with ADK
See this Kaggle notebook for an example implementation of a multi-agent system using Google Agent Developer Kit. Some selected snippets are below:
Create Individual Agents
# Research Agent: Its job is to use the google_search tool and present findings.
research_agent = Agent(
name="ResearchAgent",
model="gemini-2.5-flash-lite",
instruction="""You are a specialized research agent. Your only job is to use the
google_search tool to find 2-3 pieces of relevant information on the given topic and present the findings with citations.""",
tools=[google_search],
output_key="research_findings", # The result of this agent will be stored in the session state with this key.
)
# Summarizer Agent: Its job is to summarize the text it receives.
summarizer_agent = Agent(
name="SummarizerAgent",
model="gemini-2.5-flash-lite",
# The instruction is modified to request a bulleted list for a clear output format.
instruction="""Read the provided research findings: {research_findings}
Create a concise summary as a bulleted list with 3-5 key points.""",
output_key="final_summary",
)Create a Coordinator Agent
A naive implementation might look like this:
root_agent = Agent(
name="ResearchCoordinator",
model="gemini-2.5-flash-lite",
# This instruction tells the root agent HOW to use its tools (which are the other agents).
instruction="""You are a research coordinator. Your goal is to answer the user's query by orchestrating a workflow.
1. First, you MUST call the `ResearchAgent` tool to find relevant information on the topic provided by the user.
2. Next, after receiving the research findings, you MUST call the `SummarizerAgent` tool to create a concise summary.
3. Finally, present the final summary clearly to the user as your response.""",
# We wrap the sub-agents in `AgentTool` to make them callable tools for the root agent.
tools=[
AgentTool(research_agent),
AgentTool(summarizer_agent)
],
)Types of Multi-Agent Systems
The 3 main types/architectures of multi-agent systems supported by Google Agent Developer Kit are: