Designing Multi-Agent Swarms & Orchestration in Kotlin

Single monolithic prompts fail when tasked with executing complex, multi-layered enterprise workflows. To scale, architectures must evolve into decoupled, collaborative systems: Multi-Agent Swarms.

The Orchestration Design Pattern

In a multi-agent swarm, tasks are broken down into sub-domains managed by tightly constrained micro-agents. A primary **Orchestrator Agent** acts as a dynamic router, determining when to delegate execution flow to specialized worker nodes.

Step-by-Step Implementation: Building a Multi-Agent Cluster

Let's build a clean architecture swarm featuring a PrimaryRouterAgent that delegates specific tasks to a specialized DatabaseQueryAgent using the built-in AgentDelegationTool.

1. Define the Sub-Agent (Worker Node)

First, we configure the specialized worker node responsible exclusively for data validation or extraction.

package com.example.adkdemoapp.agents

import com.example.adkdemoapp.BuildConfig
import com.google.adk.kt.agents.Instruction
import com.google.adk.kt.agents.LlmAgent
import com.google.adk.kt.models.Gemini

object DatabaseQueryAgent {
    @JvmField
    val agent = LlmAgent(
        name = "database_query_agent",
        description = "Specialized worker handling data verification and deep lookup operations.",
        model = Gemini(name = "gemini-2.5-flash", apiKey = BuildConfig.GEMINI_API_KEY),
        instruction = Instruction(
            """
            You are a backend query agent. You only process structured data validation.
            Return results clearly inside Markdown tables. Do not engage in conversational filler.
            """.trimIndent()
        )
    )
}

2. Wire the Router Agent with AgentDelegationTool

Now, we expose the worker node directly to our main orchestrator using ADK's native sub-agent framework mapping.

package com.example.adkdemoapp.agents

import com.example.adkdemoapp.BuildConfig
import com.google.adk.kt.agents.Instruction
import com.google.adk.kt.agents.LlmAgent
import com.google.adk.kt.models.Gemini
import com.google.adk.kt.tools.AgentDelegationTool

object PrimaryRouterAgent {
    @JvmField
    val orchestrator = LlmAgent(
        name = "primary_router_agent",
        description = "Root orchestrator responsible for analyzing user intent and delegating to correct sub-agents.",
        model = Gemini(name = "gemini-2.5-flash", apiKey = BuildConfig.GEMINI_API_KEY),
        instruction = Instruction(
            """
            Analyze the user prompt. If the inquiry requires complex technical lookups,
            delegate the context entirely to the 'database_query_agent' immediately.
            """.trimIndent()
        ),
        tools = listOf(
            // Native orchestration delegation link
            AgentDelegationTool(targetAgent = DatabaseQueryAgent.agent)
        )
    )
}

Key Architectural Takeaways

• Decoupled Tooling: Sub-agents maintain their own independent tools, keeping the primary orchestrator's context window light and fast.
• Predictable State Transitions: Execution ownership passes predictably down the chain, minimizing token overhead and reducing prompt hallucination risks.