When building AI-driven workflows, one of the early decisions you'll face is whether to design your system around a single agent or to coordinate a team of multiple specialized agents. It may sound like a philosophical debate, but the choice has very real implications for scalability, maintainability, performance, and even explainability.
In this post, I'll break down the key differences between single-agent and multi-agent systems, when each shines, and how to make the right call depending on your project.
What Is a Single-Agent System?
A single-agent system is structured around one general purpose AI agent responsible for handling the entire task from start to finish. It's often easier to develop and deploy because all logic, memory, and capabilities are centralized.
🟢 Advantages:
- Simpler orchestration: Only one agent to manage.
- Easier debugging: Fewer moving parts mean easier logs and failure tracing.
- Good for narrow tasks: Ideal when the task is linear, clearly scoped, and doesn't require different modes of reasoning.
🔴 Drawbacks:
- Limited specialization: The agent must handle all parts of the workflow, even if some require drastically different expertise.
- Harder to scale: As complexity grows, the single agent's prompt or context may become too large or fragile.
- No collaboration: You lose out on the benefits of independent verification, specialization, or emergent strategies.
What Is a Multi-Agent System?
A multi-agent system consists of multiple AI agents, each with a clearly defined role. Think of it as a virtual team: one agent might classify inputs, another might generate solutions, and a third might review or refine them.
🟢 Advantages:
- Specialization: Agents can be optimized for specific tasks or domains.
- Parallelism: Different parts of the task can be run in parallel.
- Redundancy and validation: One agent can check another's work (maker-checker pattern).
- Improved context management: Each agent only needs to remember its portion of the task.
- Choose the right tool: You can choose the appropriate LLM model for the given task. For less complex tasks, a cheaper model, like gpt-35 could be chosen.
🔴 Drawbacks:
- Orchestration complexity: Requires a coordination layer or orchestration logic.
- Higher overhead: More agents = more API calls, more latency, more cost.
- Harder to trace errors: Issues may arise from interactions, not just individual agents.
A Side By Side Comparison
| Feature / Consideration | Multi-Agent System | Single-Agent System |
|---|---|---|
| Separation of Concerns | High — Each agent focuses on a specific task | Low — One agent handles everything |
| Modularity | Easy to modify or swap individual agents | Changes affect the entire agent logic |
| Reusability | Agents can be reused across projects | Logic is often tightly coupled |
| Scalability | High — Agents can run in parallel and scale independently | Limited — Single thread or context |
| Fault Isolation | Failures can be isolated to a specific agent | One failure can affect the whole system |
| Flexibility | Each agent can use specialized prompts, models, or tools | One-size-fits-all model or prompt |
| Performance | Can optimize each agent for its task | May be slower or inefficient at multitasking |
| Cost | Higher — More context switches, coordination, or models may be involved | Lower — One agent runs everything |
| Explainability | High — Easier to trace logic and responsibilities | Lower — Hard to isolate specific steps |
| Development Overhead | More effort to orchestrate and manage agents | Simpler to build and deploy |
| Best Use Case | Complex workflows, simulations, real-world team dynamics | Simple, linear tasks or MVPs |
A Real World Example: Bug Triage
I'll use the example I built in the previous post titled Building a Multi-Agent AI Bug Triage System with Azure OpenAI
- In a single-agent system, one agent reads the bug report, classifies it, proposes a fix, and decides the severity.
- In a multi-agent system, you could have:
- A Classifier Agent to label the bug (e.g., UI, backend, regression). You could use an agent that specializes in your ticketing system (e.g. Jira, Asana, Github, etc)
- A Fix Recommender Agent to suggest potential resolutions.
- A Reviewer Agent to assess and validate the classification and recommendation before approving the fix.
- A Fixer Agent to implement the fix and create a pull request.
Here, the multi-agent approach lets you chose an agent that specializes in the work being performed. Each AI agent focus on a distinct responsibility, leading to more reliable and explainable outputs. In some situations (e.g. the Fix Recommender Agent and the Fixer Agent), these could be the same agent, since the responsibilities are very similar.
When to Choose What
| Factor | Choose Single-Agent If... | Choose Multi-Agent If... |
|---|---|---|
| Complexity | The task is relatively simple or linear | The task involves multiple stages or types of reasoning |
| Speed | You want lower latency and fewer roundtrips | You can afford extra steps for better quality |
| Cost | You need to minimize API calls or runtime costs | You're okay with higher costs for modularity or robustness |
| Scalability | You don't expect the workflow to grow much | You want to easily extend the system by adding more agents |
| Explainability | One perspective is sufficient | You want to see intermediate reasoning and validations |
| Maintenance | You need minimal ongoing maintenance | You have resources to maintain and update multiple agents |
Final Thoughts
While a single agent is easier to get started with, multi-agent systems unlock the power of collaboration and modular design, just like a real world team. As your AI workflows grow more complex, leaning into a multi-agent design can help keep things manageable, explainable, and scalable.
That said, you don't have to choose one forever. Many teams prototype with a single agent and gradually refactor into multi-agent systems as needs evolve.
Question for you:
Have you worked with single or multi-agent systems in your own projects? What challenges did you run into, or solve along the way? Share your thoughts in the comments!