The Evolution from Microservice to Multi-Agent AI Systems

Over the last decade, we have embraced microservices architecture in software development to enable independent development, scalability, and resilience. This approach ensures that each service focuses on a specific feature or function, avoiding the pitfalls of large, monolithic applications that are difficult to manage, scale, and maintain.

Now, in the AI-driven Agentic world, we reflect these same principles. We do not want AI agents to become too large and complex, resembling monolithic applications but with different challenges. When we overload a single agent with multiple tools and decision-making responsibilities, it can lead to poor performance, lack of explainability, and violation of core software principles like SOLID—particularly the Single Responsibility Principle (SRP). Overburdening an agent with multiple tasks, multiple prompts, or even multiple models leads to inefficiency and unpredictable outcomes.

To tackle these challenges, we must adopt a Multi-Agent System (MAS), where AI agents are structured similarly to distributed systems in software architecture:

1. Single Agent – This is like a basic conversational chatbot that interacts with an LLM (Large Language Model) or invokes specific tools as needed to complete tasks and provide responses.

2.Supervisor Agent – Think of this as the SAGA Orchestration Pattern in microservices. Here, the supervisor agent makes decisions about which agent should be called next. This is also known as Agent Delegation, where one agent manages tools, and those tools, in turn, may invoke another agent to accomplish the task.

3.Hierarchy of Supervisors – Just as organizations have multiple levels of management, a multi-agent system can have a supervisor of supervisors. This enables even more complex control flows and decision-making structures. A real-world analogy is a corporate hierarchy:

The CEO (Chief Executive Officer) represents the top-level supervisor agent.
Below them, department heads (Finance, Marketing, HR, etc.) act as intermediary supervisor agents.
Each department has specialized teams or units (individual agents) working toward a common organizational goal.

By following this structured multi-agent approach, we can build AI systems that scale efficiently, maintain clarity in decision-making, and remain adaptable to complex tasks—just like microservices revolutionized software architecture.