The thought of having a whole team of specialized AI agents sounds pretty great, doesn’t it? Each one’s an expert, and they all work together to solve your biggest problems. But if you’ve ever tried to build something like this, you know it quickly turns into a technical, expensive mess.
This is where subagent orchestration enters the chat. It’s a smart way to divide big AI tasks among specialized "agents" that are all managed by one central coordinator.
In this guide, we’ll walk through what subagent orchestration is all about and how developers are using it with some pretty complex frameworks. We’ll also get real about the challenges of that approach, from runaway costs to coordination headaches. More importantly, we’ll show you a more practical path for teams who want the power of an AI team without needing a dedicated AI research department to run it.
What is subagent orchestration?
Put simply, subagent orchestration is about making one AI system smarter by turning it into a team. Think of it like a project manager (the orchestrator or supervisor) running a team of specialists (the subagents). When a big request comes in, the manager doesn’t try to do it all. Instead, it breaks the job down and hands each piece to the right expert.
This way of working has a few key benefits that have developers excited:
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Niche experts: You can create agents that are masters of one specific task. In developer communities, you’ll see things like a "quality control" agent that aggressively checks if work was actually finished, a "debugger" agent, or a "documentation writer." Each one is tuned for a single purpose, making it way more effective than a jack-of-all-trades AI.
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Keeps things clean: As one user on Hacker News pointed out, giving each subagent its own separate memory is a huge win. It keeps the main conversation with the orchestrator from getting cluttered, which allows for much bigger, longer-running tasks without the AI getting confused or hitting its context limit.
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Work in parallel: Just like a human team, subagents can work on different tasks at the same time. This means one agent can be analyzing data while another drafts a response and a third runs tests. The whole process gets a lot faster.
This space is moving incredibly fast. What began as developers just telling an AI to "spawn a subagent" has grown into full-blown frameworks designed to build and manage these AI teams.
The developer’s approach to subagent orchestration
Right now, the go-to method for building these multi-agent systems is through frameworks made for developers. These are powerful toolkits, but they are absolutely not plug-and-play. You need serious coding skills and a real grasp of how AI models tick.
How frameworks like AutoGen and LangChain work for subagent orchestration
Frameworks like Microsoft’s AutoGen, LangChain, and Google’s ADK give engineers the raw materials to create multi-agent apps. They aren’t ready-made solutions; think of them more like a box of Lego for AI developers.
Here’s a rough idea of what the process looks like:
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Define your agents: A developer writes code or config files for each subagent. They have to define its name, its purpose, its core instructions (the system prompt), and what tools it can use. It’s similar to how Claude Code’s subagents are set up.
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Create the supervisor: Another agent is set up to be the "supervisor" or "orchestrator." Its only job is to look at the user’s initial request and manage the whole show.
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Code the workflow: The developer then has to program the logic that tells the supervisor how to hand out tasks. This code decides when to call which subagent, how to pass info between them, and how to piece their work together into a final answer.
This gives developers a ton of flexibility, but at the end of the day, it’s a software engineering project.
A screenshot showing how a developer might define a subagent in a JSON configuration file, a key part of manual subagent orchestration.Real-world subagent orchestration examples from developers
Looking at how developers are messing around with these tools shows you both how powerful they can be and how complicated they are.
- The AI dev team: One really ambitious project shared on Reddit was an attempt to build a full AI development team. It had specialized agents for the backend, frontend, API architecture, and even documentation. The idea was to get production-ready code by copying how a real dev team works, with a "Tech Lead" agent calling the shots.
- The quality control agent: A more focused, and honestly hilarious, example is the "quality-control-enforcer" agent, which someone nicknamed "Karen." This subagent’s only job is to "aggressively detect bs" and check if the tasks other agents said were done are actually working. It’s a perfect example of a specialized agent solving a common, frustrating problem.
- Sequential vs. parallel work: A developer building a collaborative API playground explained their orchestration strategy perfectly. Some tasks had to run sequentially (one after the other) because they depended on each other, like building the backend before the frontend. Other tasks, like writing the README and creating API samples, could run in parallel (at the same time). It really shows the complex planning that has to go into making these systems work well.
Key challenges in manual subagent orchestration
As cool as these developer experiments are, they shine a big, bright light on the problems that stop most teams from even trying this stuff out. The Reddit threads are full of people complaining, and you start to see a pattern.
Why it’s a nightmare to set up and maintain
Building and managing a team of AI agents is a full-time engineering job. It’s not something you can just set and forget. As the official Claude Code documentation makes clear, every single agent needs a detailed configuration file defining its name, description, tools, and a long system prompt. Getting all those prompts and the orchestration logic right is a constant cycle of trial and error.
This is a whole different world from what a typical business team needs. While a developer is lost in YAML files and debugging Python scripts, a support manager using a platform like eesel AI can get a similar result, like creating a specialized agent for triaging tickets, through a simple, no-code interface. You can get a new "agent" up and running in minutes, not weeks.
The eesel AI no-code interface, which simplifies the process of subagent orchestration for business users.How performance and cost can get out of control
Two of the biggest complaints you see in community forums are about speed and cost. One user mentioned that subagents are "way more slow," while another was worried about how fast they "burn more tokens."
And it makes total sense. Every time you call a subagent, you’re firing up another AI model, which adds a delay and costs money. Orchestrating a team of five agents can easily be five times slower and five times more expensive than using a single AI. To manage this, you have to get into careful optimization, like using cheaper, faster models for easier tasks, which just adds another layer of technical work. This is baked into the complicated, usage-based pricing of frameworks like LangChain, which charge you per "trace" or "node execution," making it really hard to predict your bill.
Getting agents to work together
Getting a team of humans to collaborate is hard enough; getting a team of AIs to do it is a whole other level of difficulty. One developer shared their "parallel prompt fail" where they asked five subagents to design a UI at the same time. The result was pure chaos. Each agent had its own idea for the design, leading to a clashing mess of colors and fonts. They only got a decent result after creating yet another agent whose only job was to create a shared design plan first.
This really gets to the heart of the coordination problem. Good orchestration isn’t just about splitting up tasks; it’s about making sure every agent is on the same page. Experts also warn about things like "context bleeding," where agents can accidentally influence each other and lose what makes them specialists. Fixing these problems requires a deep, almost intuitive feel for how AI models work, which is way beyond what most business teams have time for.
A simpler path for support teams
Look, having a team of specialized AIs is powerful. But let’s be real: the developer-first approach is just too complex, expensive, and slow for most companies. The good news? There’s a much better way.
Introducing managed subagent orchestration workflows
Instead of handing you a box of parts and a technical manual, a managed platform gives you a system that’s ready to go, where the messy details of subagent orchestration are handled for you. The goal is to give you all the perks of specialization and automation without the engineering headache. You get to focus on what you want the AI to do, not how to build it from the ground up.
How eesel AI delivers the power of subagent orchestration without the code
eesel AI was built to give business users the power of multi-agent AI through a simple, visual interface. Here’s how it delivers on the promise of subagent orchestration:
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Specialized agents, no code needed: Creating multiple bots in eesel AI is like building your team of subagents. You can have one bot for Tier 1 customer questions trained on your help center, another for internal IT support in Slack trained on your Confluence docs, and an AI Triage agent that just tags and escalates tricky tickets. Each bot has its own knowledge, prompt, and permissions, making it a true specialist.
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You are the orchestrator: eesel AI’s workflow engine acts as the supervisor, but you’re the one in charge. Instead of writing code, you set simple rules to decide which "agent" handles which ticket, or when a task should be passed to a human. This gives you complete control over your automation without touching a single line of code.
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Test with confidence: The unpredictable nature of DIY agent systems is a huge risk. eesel AI solves this with its powerful simulation mode. Before you ever let the AI talk to live customers, you can test your entire multi-agent setup on thousands of your own past tickets. You get a clear forecast of how your "agents" will perform, what your automation rate will look like, and where the gaps are in your knowledge base.
eesel AI's simulation mode provides a risk-free way to test your subagent orchestration setup on historical data.Comparing subagent orchestration pricing models: Frameworks vs. platforms
The different approaches really show when you look at the price tags. One is built for developers, the other for business teams.
| Aspect | Developer Frameworks (e.g., LangChain) | Managed Platforms (e.g., eesel AI) |
|---|---|---|
| Model | Usage-based (per trace, per node) | Tiered, based on interaction volume |
| Predictability | Can be difficult to forecast | High, with clear monthly/annual costs |
| Hidden Costs | Potential for high bills during busy periods | No per-resolution fees; cost is fixed |
| Target User | Developers, AI Engineers | Support Managers, Business Teams |
Frameworks like LangChain have developer-focused pricing that can be confusing and unpredictable. On the other hand, eesel AI’s pricing model is transparent and built for businesses. Plans are based on a predictable monthly volume of interactions, and best of all, there are no per-resolution fees. You’ll never get a surprise bill just because your AI was great at its job.
This video shows how to build a team of AI agents using Claude Code, illustrating the developer-focused approach to subagent orchestration.
Choose the right subagent orchestration tool for the job
When it comes to subagent orchestration, you have two clear paths, and each is designed for a different kind of user.
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The DIY/Framework path: This is for teams with deep technical skills, plenty of engineering resources, and the time to manage costs and performance. It’s best for companies building their own AI products from scratch.
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The Managed/Platform path: This approach gives you the core benefits of multi-agent AI, specialization, automation, through a user-friendly interface that anyone can use. It’s the right choice for business teams, like customer support and IT, who need to solve problems today without hiring an AI research team.
For most support and service teams, the goal is pretty straightforward: you need efficient, reliable automation that’s easy to control, measure, and grow with you.
Get the power of subagent orchestration, minus the complexity
Don’t spend months trying to build a complex system of agents that might not even work. With eesel AI, you can deploy a team of specialized AI agents for your helpdesk and internal support channels in a matter of minutes.
You can simulate how it will perform on your own data, customize workflows with a few clicks, and see a return on your investment from day one.
Frequently asked questions
Subagent orchestration is a method where a central AI (orchestrator) divides large tasks among specialized AI agents (subagents). This approach allows for niche expertise, keeps individual agent memories clean, and enables parallel processing, making AI systems smarter and more efficient.
Setting up subagent orchestration with frameworks like AutoGen or LangChain is a significant engineering task, requiring deep coding skills and constant debugging. Each agent needs detailed configuration and prompt engineering, making it a full-time job to get right and maintain.
Implementing subagent orchestration can significantly increase costs and slow down performance, as each subagent call triggers another AI model interaction. Frameworks with usage-based pricing models, charging per "trace" or "node execution," can lead to unpredictable and high bills if not carefully optimized.
Managed platforms abstract away the technical complexities, providing ready-to-use systems for subagent orchestration. Business users can create specialized "agents" through simple, visual interfaces and define workflows with rules, eliminating the need for coding and extensive AI expertise.
In a customer support context, subagent orchestration can involve different AI bots for various tasks. For instance, one bot could triage tickets, another handle Tier 1 FAQs, and a third provide internal IT support, all coordinated to ensure efficient and specialized assistance without human intervention for routine queries.
Getting AI subagents to collaborate effectively without overlapping or losing their specialized focus is a major challenge. Problems like "context bleeding" can occur where agents unintentionally influence each other, leading to chaotic or uncoordinated outputs, requiring complex logic to manage.
To ensure performance and reliability, especially in managed platforms, teams can use simulation modes. This allows testing the entire subagent orchestration setup on past data to forecast performance, automation rates, and identify knowledge gaps before the AI interacts with live customers.
