Sub-Agents in AI Systems: How Multi-Agent Orchestration Works

If you have been following this series, you already know what an AI agent is. An agent observes its environment, decides what to do next, and takes actions to reach a goal. That is powerful on its own. But real-world tasks are rarely that simple — and that is exactly where sub-agents in AI systems come in.

Imagine asking an AI to research a topic, write a detailed report, translate it into three languages, and email it to your team — all in one go. That is too much for one agent. Sub-agents in AI systems solve this by breaking the work into parts and having specialized agents handle each one. The mechanism that coordinates all of them is called orchestration.

In this article, we will explore what sub-agents in AI systems are, how multiple agents work together, and how orchestration keeps it all running smoothly. We will use simple language and real-world examples so that everything makes sense by the end.

• 1. Why One Agent Is Often Not Enough
• 2. What Is a Sub-Agent?
• 3. What Is a Multi-Agent System?
• 4. How Orchestration Works
• 5. Communication Between Agents
• 6. Types of Multi-Agent Architectures
• 7. Practical Example: A Multi-Agent Content Pipeline
• 8. Popular Frameworks for Multi-Agent Systems
• 9. Common Challenges and How to Handle Them
• 10. Single-Agent vs Multi-Agent: A Clear Comparison
• 11. Conclusion

1. Why One Agent Is Often Not Enough

When people first learn about AI agents, they naturally think: can one smart agent just do everything? In theory, yes. But in practice, a single agent trying to do too many things at once runs into several real problems.

The first problem is context limits. Large language models (LLMs) that power most AI agents have a maximum amount of text they can process at one time. This is called the context window. If a task is very long — reading dozens of documents, writing thousands of lines of code — a single agent will hit this limit and lose track of earlier information.

The second problem is specialization. A single generalist agent might be decent at many things but excellent at none. Just like in a company, you would not ask your accountant to also design your website and manage your servers. Different tasks benefit from different tools, different instructions, and sometimes different models entirely.

The third problem is speed. A single agent works sequentially — one step at a time. If multiple parts of a task can happen at the same time, making one agent do them one after another is slow and inefficient. A team of agents can work in parallel, finishing the same job much faster.

These three problems — context limits, specialization, and speed — are exactly what multi-agent systems are designed to solve. Instead of one large, overloaded agent, you build a team of focused agents, each doing what it does best.

Real-World Analogy: Think of a software company. You have a product manager, developers, testers, and a DevOps engineer. Each person is specialised. The project manager coordinates everyone. That is exactly how a multi-agent system works. The orchestrator is the project manager, and each sub-agent is a team member with a specific role.

2. What Is a Sub-Agent?

A sub-agent is simply an AI agent that works under the direction of another agent. The agent giving instructions is called the orchestrator (or the primary agent). The agents who receive those instructions and carry them out are the sub-agents.

A sub-agent is not a lesser or weaker agent. It is just an agent with a focused job within a larger workflow. A sub-agent might be excellent at one specific task — searching the web, writing code, summarising text, calling an API, or generating images — and it gets called exactly when that skill is needed.

2.1 What Does a Sub-Agent Look Like in Code?

In technical terms, a sub-agent is usually a regular AI agent that has been given a specific system prompt, a specific set of tools, and a specific goal. The orchestrator calls it by sending a message with a task description, and the sub-agent responds with the result.

From the sub-agent’s perspective, it just receives a task and completes it. It does not necessarily know about the bigger picture. It does not know it is part of a larger workflow. It simply does its job and returns the output. This isolation is a good design principle — it keeps agents simple, focused, and easy to test independently.

// Conceptual example — how an orchestrator calls a sub-agent

// Step 1: Orchestrator defines the task for a sub-agent
String taskForResearchAgent = "Search the web and find the top 5 AI frameworks in 2026.";

// Step 2: Orchestrator sends this task to the research sub-agent
String researchResult = researchAgent.run(taskForResearchAgent);

// Step 3: Orchestrator uses the result for the next step
String taskForWriterAgent = "Write a 500-word summary based on: " + researchResult;
String finalArticle = writerAgent.run(taskForWriterAgent);

// Step 4: Orchestrator returns the final output to the user
System.out.println(finalArticle);

In this simplified example, the orchestrator coordinates two sub-agents: a research agent and a writer agent. It calls them in sequence — first research, then writing. This is the core idea of orchestration, even if real systems are more complex.

2.2 Sub-Agent vs Tool: What Is the Difference?

A very common question is: What is the difference between a sub-agent and a tool? Both are invoked by an orchestrator to perform a specific task. Understanding the distinction matters in practice.

A tool is a fixed function. It does one specific thing when called — like searching Google, running a database query, or converting a file format. A tool has no intelligence on its own. It takes inputs, runs a predefined operation, and returns outputs deterministically.

A sub-agent, on the other hand, is itself an LLM-based agent with reasoning ability. It can receive a complex, open-ended instruction, think about how to approach it, potentially use its own tools, and return a nuanced, context-aware result. A sub-agent can handle ambiguity in ways that a fixed tool simply cannot.

Aspect	Tool vs Sub-Agent
Tool	Fixed function, no reasoning, deterministic output
Sub-Agent	LLM-based, can reason, can use tools, handles ambiguity
Tool Example	search_web(query) — always searches, always returns raw links
Sub-Agent Example	ResearchAgent — reads links, judges relevance, writes a summary
When to use a Tool	Simple, well-defined, repeatable operations with known inputs and outputs
When to use a Sub-Agent	Complex tasks that require judgment, planning, and multi-step reasoning

3. What Is a Multi-Agent System?

A multi-agent system (MAS) is a system where two or more AI agents work together to complete a task or a set of tasks. Each agent in the system has its own role, tools, and instructions. They communicate with each other — directly or through an orchestrator — to produce a combined result.

Multi-agent systems have been studied in computer science and AI research for decades. But until recently, building them required enormous engineering effort. The rise of powerful LLMs and frameworks that support agent-to-agent communication has made multi-agent systems practical for everyday developers.

3.1 Key Components of a Multi-Agent System

Every multi-agent system has a few core components that appear in almost every agentic framework — whether you are using LangChain, CrewAI, AutoGen, or building your own custom system.

• Orchestrator Agent: The central coordinator. Breaks down goals, assigns tasks to sub-agents, and assembles the final result.
• Sub-Agents: Specialized agents that execute specific tasks as directed by the orchestrator.
• Tools: Functions that agents use to interact with external systems — web search, APIs, databases, file systems.
• Shared Memory or Context: The mechanism through which agents share information — either via a central memory store or by passing outputs directly as inputs.
• Communication Protocol: The format and channel through which agents send messages to each other.

When all five components are in place, you have a functioning multi-agent system. The orchestrator sets direction, sub-agents do specialized work, tools interact with external systems, memory keeps things connected across steps, and the communication protocol ties it all together cleanly.

3.2 A Real-World Example: AI Research Assistant

Let us make this concrete. Suppose you are building an AI research assistant that answers deep technical questions. When a user asks a question, here is how a multi-agent system might handle it step by step.

Step 1 – The orchestrator receives the question and breaks it into sub-tasks: search for recent information, read and summarise relevant sources, cross-check facts, and write a final answer.

Step 2 – The orchestrator calls a Search Agent with the query. The Search Agent uses a web search tool to retrieve URLs and returns a list of relevant links with brief descriptions.

Step 3 – The orchestrator passes those links to a Reader Agent. The Reader Agent fetches each page, reads the content, and produces a short, clean summary of each source.

Step 4 – The orchestrator passes all summaries to a Writer Agent. The Writer Agent synthesizes the information into a clear, structured answer with references.

Step 5 – The orchestrator reviews the draft output and, if it meets quality criteria, returns it to the user. If not, it routes back to an editing sub-agent for improvement.

Notice that no single agent had to do everything. Each was focused on what it did best. The orchestrator simply coordinated the flow. This division of responsibility is the power of a multi-agent system.

4. How Orchestration Works

Orchestration is the process of coordinating multiple agents to work together towards a common goal. The orchestrator is the brain of the system. It receives the high-level objective, figures out what needs to happen and in what order, delegates work to sub-agents, and assembles the results into a final output.

Orchestration can be simple or sophisticated. In simple cases, the orchestrator follows a fixed sequence — always calling agents in the same predetermined order. In advanced cases, the orchestrator uses an LLM to dynamically reason about the best plan, adapting its approach based on each sub-agent’s output.

4.1 Static Orchestration vs Dynamic Orchestration

Static orchestration means the workflow is predefined by the developer. You decide in advance which agents run in what order. This approach is predictable, easy to debug, and well-suited for tasks where the steps are always the same. Think of it as a fixed production pipeline.

Dynamic orchestration means the orchestrator itself decides what to do next, based on the results received so far. It uses an LLM to reason about the current state and pick the next best action. This is more flexible and more powerful, but also less predictable and harder to debug. It works best for open-ended, exploratory tasks where you cannot define all the steps in advance.

// Static orchestration — fixed pipeline, steps always the same
public String runStaticPipeline(String userQuery) {
    String searchResults = searchAgent.run(userQuery);
    String summary       = summaryAgent.run(searchResults);
    String finalAnswer   = writerAgent.run(summary);
    return finalAnswer;
}

// Dynamic orchestration — orchestrator decides next step at runtime
public String runDynamicOrchestration(String userQuery) {
    String currentState = userQuery;
    int MAX_STEPS = 10;
    for (int step = 0; step < MAX_STEPS; step++) {
        // The orchestrator LLM decides which agent to call next
        String nextAction = orchestratorLLM.decideNextAction(currentState);
        if (nextAction.equals("DONE")) break;
        String result = agentRegistry.run(nextAction, currentState);
        currentState  = currentState + "\nResult: " + result;
    }
    return currentState;
}

In the static example, the flow is fixed — always search, then summarise, then write. In the dynamic example, the orchestrator LLM evaluates the current state at each iteration and decides on the next action. The loop continues until the orchestrator signals completion or MAX_STEPS is reached.

4.2 Sequential vs Parallel Execution

Another important dimension of orchestration is whether agents run one after another (sequential) or at the same time (parallel). Sequential execution is simpler to reason about — each agent waits for the previous one to finish before it starts. Parallel execution is faster but requires careful design to avoid conflicts and to properly combine results.

For example, if you are building a report that pulls data from three different APIs, you could call three data-fetching agents simultaneously in parallel and then combine their outputs in a final writing step. This is significantly faster than fetching from each API sequentially.

Interview Insight: A common interview question is: ‘When would you choose sequential vs parallel agent execution?’ The right answer depends on data dependencies. If Agent B needs the output of Agent A as its input, they must run sequentially. If Agent B and Agent C are independent of each other and their outputs are combined later by the orchestrator, they can run in parallel. Understanding and mapping these dependency relationships is a key skill when designing multi-agent systems.

5. Communication Between Agents

For a multi-agent system to work, agents need to exchange information reliably. How they do this depends on the architecture you choose. There are two main communication patterns you will encounter: message passing and shared state.

5.1 Message Passing

In message passing, one agent sends a message directly to another agent. The message contains the task description, any relevant context, and often the output of previous steps. The receiving agent processes the message and sends back a response. This is the most common pattern in modern agentic frameworks.

Think of it like email between colleagues. The orchestrator emails the research agent with a task. The research agent replies with findings. The orchestrator then emails the writer agent with those findings. Each agent only sees the messages sent specifically to it — not the full system picture. This keeps agents focused and reduces noise.

// Message passing pattern — each agent receives a clear, structured message

AgentMessage msg1 = new AgentMessage(
    "research-agent",
    "Find the latest trends in Java frameworks for 2026. Return a bullet list."
);
AgentResponse resp1 = messageBus.send(msg1);

// Orchestrator builds the next message using the previous response
AgentMessage msg2 = new AgentMessage(
    "writer-agent",
    "Write a blog introduction using these trends: " + resp1.getContent()
);
AgentResponse resp2 = messageBus.send(msg2);

5.2 Shared State and Shared Memory

In the shared state pattern, all agents read from and write to a common memory store. An agent picks up the current state, does its work, and writes the updated state back. The next agent picks up the updated state and continues from there. This is more like a shared whiteboard in a meeting room — everyone can see what others have written.

Shared state is useful when many agents need to see the full context of what has been done so far. The downside is coordination complexity — agents need to be careful not to overwrite each other’s work, and you need concurrency controls when agents run in parallel.

Most production multi-agent systems use a combination of both patterns. Message passing is used for direct task delegation between agents, while shared memory maintains overall context across the full workflow session.

6. Types of Multi-Agent Architectures

Not all multi-agent systems are structured the same way. Depending on the use case and scale, different architectural patterns make more sense. Here are the three most common architectures you will encounter.

6.1 Hub-and-Spoke (Centralized Orchestrator)

In a hub-and-spoke, there is one central orchestrator that communicates with all sub-agents. Sub-agents do not talk to each other — everything goes through the central hub. This is the simplest and most common architecture for beginners. It is easy to understand, easy to debug, and works well for most real-world use cases.

The downside is that the orchestrator becomes a bottleneck. If the orchestrator makes a bad decision or fails, the whole system is affected. For small to medium systems, this is usually acceptable — the simplicity and debuggability outweigh the risk.

6.2 Hierarchical Agents

In a hierarchical architecture, orchestrators can have sub-orchestrators. A top-level orchestrator might break a very large task into three major sub-tasks and assign each to a mid-level orchestrator. Each mid-level orchestrator then manages its own set of sub-agents to complete that sub-task.

This mirrors how large organizations are structured. A CEO delegates to VPs, who delegate to managers, who delegate to individual contributors. Hierarchical architecture scales well for very complex tasks, but adds layers of coordination complexity and can make debugging harder.

6.3 Peer-to-Peer (Decentralized) Agents

In peer-to-peer architecture, agents can communicate directly with each other without a central orchestrator. Any agent can request help from any other agent. This is the most flexible design but also the hardest to build, reason about, and debug.

Peer-to-peer architectures appear more in research and simulation settings. For most production business applications, centralized or hierarchical orchestration is the right starting point. You can always evolve towards decentralization later if the system demands it.

Architecture	When to Use It
Hub-and-Spoke (Central Orchestrator)	Simple workflows, clear sequence, beginner-friendly, easy to debug and monitor
Hierarchical	Complex tasks, large numbers of agents, enterprise-scale automation workflows
Peer-to-Peer	Research, simulations, highly dynamic and exploratory workflows — uncommon in production

7. Practical Example: A Multi-Agent Content Pipeline

Let us walk through a practical, end-to-end example that ties all these concepts together. We will design a multi-agent content pipeline. The goal is straightforward: given a topic, automatically produce a ready-to-publish blog article.

Here are the agents we need to build the pipeline:

• Orchestrator Agent: Receives the topic, coordinates all other agents, and returns the final polished article.
• Research Agent: Searches the web for recent, accurate, and relevant information about the topic.
• Outline Agent: Takes the research summary and creates a structured blog outline with clear section headings.
• Writer Agent: Takes the outline and writes the full article, section by section, in a readable style.
• Editor Agent: Reviews the article for grammar, clarity, flow, and overall quality before final delivery.

// Multi-agent content pipeline — simplified conceptual Java code

public class ContentPipelineOrchestrator {

    private ResearchAgent researchAgent = new ResearchAgent();
    private OutlineAgent  outlineAgent  = new OutlineAgent();
    private WriterAgent   writerAgent   = new WriterAgent();
    private EditorAgent   editorAgent   = new EditorAgent();

    public String generateArticle(String topic) {

        // Step 1 — Research the topic
        System.out.println("[Orchestrator] Starting research for: " + topic);
        String research = researchAgent.run(
            "Find key facts, recent trends, and key points about: " + topic
        );

        // Step 2 — Create a structured outline
        System.out.println("[Orchestrator] Creating outline...");
        String outline = outlineAgent.run(
            "Create a blog outline with 6 sections using this research: " + research
        );

        // Step 3 — Write the full article from the outline
        System.out.println("[Orchestrator] Writing article...");
        String draft = writerAgent.run(
            "Write a full beginner-friendly article from this outline: " + outline
        );

        // Step 4 — Edit and polish the draft
        System.out.println("[Orchestrator] Editing article...");
        String finalArticle = editorAgent.run(
            "Review and improve this article for clarity and grammar: " + draft
        );

        System.out.println("[Orchestrator] Article complete!");
        return finalArticle;
    }
}

This example uses a static, sequential pipeline. The orchestrator drives the process step by step. Each sub-agent receives a focused task, does its job, and returns its output. The orchestrator threads all the outputs together to produce the final result.

In a real production system, each agent class would internally make an LLM API call using a specific system prompt that defines its role and capabilities. The orchestrator would also include error handling — if one agent fails, it can retry, route to a fallback agent, or gracefully abort with a helpful error message rather than crashing silently.

Interview Insight: Interviewers often ask: ‘How do you handle failures in a multi-agent system?’ Strong answers cover: retry logic at the agent level with exponential backoff, fallback agents for critical steps, circuit breakers to prevent repeatedly calling a failing agent, and structured logging at every step so you can diagnose exactly where a workflow broke. Candidates who mention observability and graceful degradation stand out.

8. Popular Frameworks for Multi-Agent Systems

You do not have to build multi-agent orchestration entirely from scratch. Several popular frameworks already provide the building blocks — agent definitions, tool registration, memory management, and inter-agent communication. Here is a practical overview of the most commonly used ones.

8.1 LangChain and LangGraph

LangChain is one of the most widely used frameworks for building LLM-powered applications in Python. It provides ready-made components for agents, tools, memory, and chains. LangGraph, built on top of LangChain, adds graph-based workflow orchestration. You define agent workflows as directed graphs in which nodes are agents or functions, and edges represent transitions between them.

LangGraph is particularly good for stateful, multi-step workflows. It handles cycles (an agent looping back to a previous step based on results), branching (taking different paths based on conditions), and parallel execution out of the box.

8.2 CrewAI

CrewAI is a framework designed specifically for multi-agent collaboration. It uses the metaphor of a ‘crew’ — you define roles for each agent, assign tasks, and the framework manages how the crew works together to complete the overall goal. CrewAI is developer-friendly, has clean abstractions, and is a good choice for teams new to multi-agent development.

8.3 AutoGen (Microsoft)

AutoGen, from Microsoft Research, is a framework focused on multi-agent conversation. Agents communicate through natural-language messages—much like a group chat. AutoGen supports both fully autonomous agent collaboration and human-in-the-loop workflows where a human can review and approve decisions at any step in the process.

8.4 Custom Java Implementation

If you are a Java developer and already have Spring Boot applications in production, you may prefer to build orchestration logic directly in Java. You can call LLM APIs (such as the Anthropic Claude API or OpenAI API) from Java using HTTP clients, define your own agent classes, and wire them together using Spring dependency injection. This gives you full control over every aspect of the system and integrates naturally with your existing Java infrastructure.

Framework / Approach	Best For
LangChain / LangGraph	Graph-based workflows, stateful agents, large Python ecosystem
CrewAI	Role-based agent teams, easy setup, collaborative multi-agent tasks
AutoGen (Microsoft)	Multi-agent conversation, human-in-the-loop workflows, research use cases
Custom Java / Spring Boot	JVM ecosystem, existing Java codebases, full control over orchestration logic

9. Common Challenges and How to Handle Them

Multi-agent systems are powerful, but they come with real engineering challenges. Understanding these challenges upfront will save you significant time and frustration when you start building production systems.

9.1 Prompt Leakage and Instruction Confusion

When you pass the output of one agent as the input to the next, the text from the first agent becomes part of the prompt for the second. If that output contains confusing, ambiguous, or contradictory content, the second agent can be misled into producing wrong results. This is called prompt leakage.

The practical fix is to structure inter-agent messages carefully. Use explicit delimiters between sections, give the receiving agent clear instructions about what to do with the input, and prefer clean summaries over raw outputs when passing context between agents.

9.2 Runaway Loops and Missing Stopping Conditions

In dynamic orchestration, there is a real risk that the orchestrator keeps calling agents in a loop without making meaningful progress. This happens when the LLM does not recognise a stopping condition, or when agents keep returning ambiguous results the orchestrator cannot interpret as complete.

Always set a hard maximum number of steps (MAX_STEPS) and define a clear, explicit stopping condition in your orchestrator logic. Log every step with timestamps and intermediate results so you can detect when the system is going in circles before it runs up a large API bill.

9.3 Cost Management

Every LLM call has a cost in money or computing resources. In a multi-agent system with five agents handling a single request, you might make 20 to 50 LLM calls per user interaction. Without careful design, costs spiral very quickly at scale.

A few practical strategies help here. Use smaller, cheaper models for simpler sub-agents (like a summarisation agent or a formatting agent) and reserve larger, more capable models for the orchestrator and the agents that require the most reasoning. Cache results aggressively where outputs are deterministic. Set a per-workflow budget and abort if costs exceed the threshold.

9.4 Debugging and Observability

When something goes wrong in a multi-agent system, tracing the issue back to its source is genuinely difficult. Was it the orchestrator’s task decomposition? Is the research agent returning stale data? Is the writer’s agent misinterpreting the outline? Without proper logging and tracing, debugging feels like searching for a needle in a haystack.

Build observability from day one. Log every agent call, every input message, every output response, and every decision the orchestrator makes. Use structured logging with step numbers, agent names, and timestamps so you can replay the full execution trace and pinpoint exactly where things went wrong.

10. Single-Agent vs Multi-Agent: A Clear Comparison

Now that we have covered multi-agent systems in depth, let us directly compare them with single-agent systems. This comparison helps you decide which approach is right for your specific use case.

Factor	Single-Agent vs Multi-Agent
Setup complexity	Single-agent: simple to build. Multi-agent: more upfront design required.
Task complexity	Single-agent: good for focused, short tasks. Multi-agent: better for long, multi-step workflows.
Context window limits	When to choose a single
Specialisation	Single-agent: one prompt handles everything. Multi-agent: each agent is fine-tuned for its specific role.
Speed	Single-agent: sequential by nature. Multi-agent: can run independent steps in parallel.
Cost per request	Single-agent: fewer LLM calls, lower cost. Multi-agent: more calls, potentially higher cost.
Debuggability	Single-agent: easier to trace a single conversation. Multi-agent: requires structured logging across agents.
Long workflows, tasks needing specialization, parallel processing needs, or production automation.	Short tasks, simple goals, tight budgets, quick prototypes, or exploratory projects.
When to choose multi	Long workflows, tasks needing specialisation, parallel processing needs, or production automation.

The key takeaway is that multi-agent systems are not always better. For a simple question-and-answer chatbot or a focused code-generation tool, a single agent is the right, simpler choice. For an autonomous pipeline that researches, plans, codes, tests, and deploys, a well-designed multi-agent system is the only viable approach.

11. Conclusion

Multi-agent systems and orchestration represent a major step forward in how we build AI-powered applications. Instead of asking one agent to do everything and hoping for the best, we build a team. Each agent focuses on what it does best. An orchestrator coordinates the team towards the shared goal.

In this article, we covered why single agents are often not enough, what sub-agents are and how they differ from tools, how multi-agent systems are structured and what components they need, the mechanics of orchestration including static vs dynamic and sequential vs parallel patterns, how agents communicate through message passing and shared state, the three main architectural patterns (hub-and-spoke, hierarchical, and peer-to-peer), a practical Java pipeline example, popular frameworks, and the real challenges you will face in production.

This is one of the most important concepts in modern AI development. As AI moves from simple chatbots to autonomous systems capable of taking complex actions in the real world, multi-agent orchestration is the engineering discipline that makes such systems reliable, scalable, and maintainable.

In the next article, we will dive into memory — how agents remember things across conversations and tasks, what short-term vs long-term memory means in the context of AI agents, and how to build agents that carry context forward effectively.