As AI-powered applications become increasingly sophisticated, a single AI agent is often insufficient to handle complex business workflows. Modern AI systems frequently require multiple specialized agents, each responsible for a specific task such as research, data analysis, content generation, validation, or decision-making. Subagent orchestration is a design pattern that enables a primary AI agent (orchestrator) to delegate responsibilities to multiple specialized subagents and aggregate their responses into a final result. This approach improves scalability, maintainability, and response quality. Spring AI provides a powerful framework for building agentic workflows using Large Language Models (LLMs), tools, memory, and structured interactions. By leveraging Spring Boot and Spring AI, developers can implement orchestrator-subagent architectures with minimal boilerplate code.
1. Introduction to Subagent Orchestration in Spring AI
In a subagent orchestration architecture, a master agent coordinates several specialized agents. Each subagent performs a focused task and returns its result to the orchestrator, forming a structured workflow pattern often used in Spring AI-based systems to build modular and scalable AI applications. This design improves separation of concerns, where each agent is responsible for a specific capability such as research, summarization, validation, or data transformation, and the orchestrator manages the overall execution flow and result aggregation.
1.1 Architecture
The subagent orchestration architecture in Spring AI is built around a central orchestrator agent that acts as the control layer for all AI-driven workflows, receiving the user request and breaking it into smaller, well-defined tasks that are delegated to specialized subagents such as research, summarization, validation, or domain-specific processing agents. Each subagent is designed to be single-purpose and stateless in nature, focusing only on its assigned responsibility and returning structured output back to the orchestrator. The orchestrator then coordinates the execution flow—either sequentially or in parallel—aggregates the results from all subagents, applies any final reasoning or formatting, and produces a unified response for the user. This layered approach enables modularity, scalability, and easier maintenance because each agent can be independently developed, tested, and replaced without affecting the overall system, while Spring AI provides the underlying LLM integration, prompt management, and tool-calling capabilities that allow seamless communication between the orchestrator and its subagents.
1.2 Benefits
Subagent orchestration in Spring AI provides a clean separation of responsibilities by dividing complex AI tasks into smaller, focused agents, each optimized for a specific function such as reasoning, retrieval, or summarization. This leads to improved response quality because each subagent can be individually tuned with specialized prompts and context, reducing ambiguity and increasing precision. It also significantly improves maintainability and testing, as each agent behaves like an independent service that can be validated in isolation. Additionally, this modular design enables reusable AI components that can be shared across different workflows, reducing duplication of logic and effort. Overall, it supports a scalable enterprise AI architecture where systems can grow organically by adding new agents without disrupting existing workflows.
1.3 Use Cases
Subagent orchestration is widely applicable in real-world AI systems where tasks require multiple reasoning steps or domain specialization. In research and report generation, different agents can gather, validate, and summarize information from multiple sources. In customer support automation, specialized agents can handle intent detection, response generation, and escalation decisions. For software architecture analysis, agents can evaluate code structure, identify issues, and suggest improvements. In document processing pipelines, subagents can extract, classify, and transform unstructured data into structured formats. It is also highly effective in multi-step business workflows where tasks such as planning, decision-making, validation, and reporting need to be coordinated in a controlled and reliable manner.
2. Spring AI Subagent Orchestration Example
We will build a Spring Boot application containing a Research Agent that gathers detailed information about a given topic, a Summary Agent that processes the collected information and generates a concise and meaningful summary, and an Orchestrator Agent that coordinates the workflow between these subagents by delegating tasks, managing execution flow, and producing the final aggregated output for the user.
2.1 Maven dependencies
These dependencies provide Spring Boot core runtime and Spring AI OpenAI integration required for building agentic workflows.
<dependencies>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-starter-model-openai</artifactId>
</dependency>
</dependencies>
The Maven dependencies define the core building blocks required to run the Spring AI application within a Spring Boot ecosystem. The spring-boot-starter provides the foundational framework support including dependency injection, auto-configuration, and application lifecycle management, enabling rapid development of production-ready services. The spring-ai-starter-model-openai dependency integrates Spring AI with OpenAI’s large language models, allowing the application to send prompts, receive structured responses, and build AI-powered agent workflows. Together, these dependencies establish the essential infrastructure needed to create orchestrator and subagent-based architectures with minimal configuration while leveraging Spring’s modular and extensible design principles.
2.2 Application configuration
This configuration connects Spring AI with OpenAI and defines the model used for all agents in the system.
spring:
ai:
openai:
api-key: ${OPENAI_API_KEY}
chat:
options:
model: gpt-4o
The application configuration defines how Spring AI connects to the underlying LLM provider, in this case OpenAI, by supplying the required API key through an environment variable for security and externalized configuration. It also specifies the chat model to be used, such as gpt-4o, which determines the capability, reasoning quality, and response behavior of the AI agents in the system. By externalizing these settings in the Spring Boot configuration file, the application gains flexibility, allowing developers to switch models or providers without changing the core business logic of the agents, thereby supporting better maintainability and deployment portability.
2.3 Research Subagent
This agent is responsible for gathering detailed information about a given topic using LLM-based prompts.
package com.example.agent;
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.stereotype.Service;
@Service
public class ResearchAgent {
private final ChatClient chatClient;
public ResearchAgent(ChatClient.Builder builder) {
this.chatClient = builder.build();
}
public String research(String topic) {
return chatClient.prompt()
.user("""
Act as a research expert.
Provide detailed information about:
%s
""".formatted(topic))
.call()
.content();
}
}
The ResearchAgent class is a Spring-managed service responsible for handling research-related AI tasks by leveraging Spring AI’s ChatClient abstraction. It is annotated with @Service so that Spring can automatically detect and inject it as a singleton bean within the application context. The class receives a ChatClient.Builder through constructor injection, which is then used to build a ChatClient instance that acts as the communication bridge between the application and the underlying LLM provider. The research method takes a topic as input and constructs a structured prompt instructing the model to act as a research expert and generate detailed information about the given topic. This prompt is sent using the fluent ChatClient API, where .prompt() initializes the request, .user() defines the user message, and .call().content() executes the request and retrieves the generated response as a plain string. Overall, this agent encapsulates the responsibility of information gathering, keeping the logic modular, reusable, and aligned with the subagent orchestration pattern in Spring AI.
2.4 Summary Subagent
This agent takes raw research content and converts it into structured, concise key points.
package com.example.agent;
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.stereotype.Service;
@Service
public class SummaryAgent {
private final ChatClient chatClient;
public SummaryAgent(ChatClient.Builder builder) {
this.chatClient = builder.build();
}
public String summarize(String content) {
return chatClient.prompt()
.user("""
Summarize the following content
into key points:
%s
""".formatted(content))
.call()
.content();
}
}
The SummaryAgent class is a Spring service that acts as a specialized subagent responsible for condensing large pieces of text into concise and structured summaries using Spring AI’s ChatClient. It is marked with the @Service annotation so that Spring automatically manages its lifecycle and dependency injection. The class receives a ChatClient.Builder through constructor injection, which is used to build a ChatClient instance that enables communication with the underlying LLM. The summarize method accepts a content string as input and constructs a prompt instructing the model to summarize the provided text into key points. This prompt is executed using the fluent ChatClient API, where the request is initiated with .prompt(), the user instruction is defined with .user(), and the call is executed using .call().content() to retrieve the generated summary as a string. This design keeps the summarization logic isolated, reusable, and aligned with the subagent-based orchestration pattern in Spring AI, where each agent performs a single, well-defined responsibility.
2.5 Orchestrator Agent
This orchestrator coordinates ResearchAgent and SummaryAgent to produce the final response.
package com.example.agent;
import org.springframework.stereotype.Service;
@Service
public class OrchestratorAgent {
private final ResearchAgent researchAgent;
private final SummaryAgent summaryAgent;
public OrchestratorAgent(
ResearchAgent researchAgent,
SummaryAgent summaryAgent) {
this.researchAgent = researchAgent;
this.summaryAgent = summaryAgent;
}
public String process(String topic) {
System.out.println("Invoking Research Agent...");
String researchResult = researchAgent.research(topic);
System.out.println("Invoking Summary Agent...");
String summary = summaryAgent.summarize(researchResult);
return """
===== RESEARCH RESULT =====
%s
===== SUMMARY =====
%s
""".formatted(
researchResult,
summary
);
}
}
The OrchestratorAgent class acts as the central coordination layer in the subagent architecture, responsible for managing the workflow between multiple specialized agents in a Spring AI application. It is annotated with @Service so that Spring can manage it as a singleton bean and inject its dependencies automatically. The class depends on two subagents, ResearchAgent and SummaryAgent, which are injected via constructor injection to promote loose coupling and better testability. The process method takes a topic as input and first invokes the ResearchAgent to gather detailed information about the topic, printing a log statement to indicate execution flow. The resulting research output is then passed to the SummaryAgent, which generates a concise summary of the retrieved content. Finally, the orchestrator combines both the raw research output and the summarized insights into a structured formatted response using a text block, effectively aggregating results from multiple AI agents into a single coherent output. This design demonstrates the core principle of orchestration, where the orchestrator controls execution flow while delegating specialized tasks to subagents, enabling modularity, scalability, and clear separation of concerns.
2.6 REST Controller
This controller exposes the HTTP endpoint that triggers the full orchestration workflow.
package com.example.controller;
import com.example.agent.OrchestratorAgent;
import org.springframework.web.bind.annotation.*;
@RestController
@RequestMapping("/agent")
public class AgentController {
private final OrchestratorAgent orchestrator;
public AgentController(OrchestratorAgent orchestrator) {
this.orchestrator = orchestrator;
}
@GetMapping("/process")
public String process(@RequestParam String topic) {
return orchestrator.process(topic);
}
}
The AgentController class serves as the REST API layer in the Spring Boot application, exposing an endpoint that allows external clients to interact with the underlying subagent orchestration system. It is annotated with @RestController, indicating that it handles HTTP requests and returns responses directly as data, and @RequestMapping(“/agent”), which defines the base URL path for all endpoints within this controller. The class depends on the OrchestratorAgent, which is injected through constructor injection to ensure loose coupling between the web layer and the AI orchestration logic. The process method is mapped to the GET endpoint /process and accepts a topic as a request parameter. When invoked, it delegates the request to the orchestrator’s process method, which internally coordinates the ResearchAgent and SummaryAgent, and returns the final aggregated response. This design cleanly separates the API layer from the AI workflow logic, enabling a scalable and maintainable architecture where the controller focuses solely on request handling while the orchestrator manages all AI-driven processing.
2.7 Spring Boot Main Class
This is the application bootstrap class that starts the Spring Boot runtime and initializes all agents.
package com.example;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
@SpringBootApplication
public class SpringAiApplication {
public static void main(String[] args) {
SpringApplication.run(SpringAiApplication.class, args
);
}
}
The SpringAiApplication class is the main entry point of the Spring Boot application and is responsible for bootstrapping the entire system. It is annotated with @SpringBootApplication, which is a composite annotation that enables component scanning, auto-configuration, and configuration class registration, allowing Spring to automatically detect and wire all agents, controllers, and services defined in the application. The main method serves as the standard Java entry point and invokes SpringApplication.run(), which launches the embedded server, initializes the Spring context, and sets up all beans including the orchestrator and subagents. Once the application starts, it becomes capable of handling incoming HTTP requests, routing them through the controller layer, and executing the full subagent orchestration workflow. This class essentially acts as the bootstrapper that activates the entire Spring AI-powered system.
2.8 Code Output
When the application is executed and the REST endpoint is invoked with a topic such as “Spring AI”, the system processes the request through the orchestrator-driven subagent pipeline. The request first enters the controller layer, which forwards it to the OrchestratorAgent. The orchestrator then delegates the task to the ResearchAgent to generate detailed contextual information about the topic, and subsequently passes the research output to the SummaryAgent to distill it into structured key points. Finally, the orchestrator aggregates both the raw research output and the summarized insights into a unified response, which is returned to the client as the final output.
GET /agent/process?topic=Spring AI
The execution flow begins when a client sends an HTTP GET request to the /agent/process endpoint with a topic parameter, which is first handled by the AgentController in the Spring Boot application. The controller forwards this request to the OrchestratorAgent, which acts as the central coordination layer of the system. The orchestrator then invokes the ResearchAgent to generate detailed information about the given topic using an LLM-powered prompt, and once the research output is received, it passes this result to the SummaryAgent for further processing. The SummaryAgent condenses the large research output into structured key points, after which control returns back to the orchestrator. Finally, the orchestrator combines both the raw research output and the summarized insights into a single formatted response, which is returned through the controller back to the client as the final API response, completing the full subagent orchestration cycle.
===== RESEARCH RESULT ===== Spring AI is an application framework that integrates Large Language Models into Spring Boot applications. It provides abstractions for chat models, embeddings, vector stores, tools, memory, and agent workflows. ===== SUMMARY ===== 1. Spring AI simplifies AI integration. 2. Supports OpenAI, Anthropic, Azure OpenAI, and other providers. 3. Provides vector store integration. 4. Enables agentic workflows and tool calling. 5. Ideal for enterprise AI applications.
3. Conclusion
Subagent orchestration is a powerful architectural pattern for building enterprise-grade AI systems using Spring AI. Instead of relying on a single monolithic agent, developers can create multiple specialized agents and coordinate them through an orchestrator. By combining Spring Boot, Spring AI, and orchestrator-subagent architectures, organizations can build scalable, maintainable, and intelligent AI applications capable of handling complex multi-step workflows efficiently.
