Multi-Agent Design Patterns

As organizations increasingly embed AI into their applications, AI agents are becoming the foundation for building intelligent systems that deliver real business value. When designing agentic applications, one of the first architectural decisions teams encounter is whether to build a single-agent solution or a multi-agent system. This decision closely mirrors a familiar question from traditional system design: Should this be a monolithic application or a microservices based architecture?

While a single (monolithic) agent can certainly handle complex tasks, multi-agent systems introduce clear advantages in reliability, maintainability, specialization, and scalability. Much like microservices, they allow responsibilities to be distributed, optimized, and evolved independently.

In this article, we will deep dive into how to design effective multi-agent solutions, explore commonly used multi-agent design patterns, and discuss when and why each pattern should be applied in real world AI systems.

1. Prompt Chaining Pattern

What it is: A linear sequence where the output of one agent becomes the input to the next agent.

Complexity: Low

When to use:

• Tasks must be executed in a strict order
• Each step depends on the previous result
• Predictability and simplicity are priorities

Key characteristics

• Sequential execution
• Deterministic flow
• Easy to debug and maintain
• Minimal orchestration overhead

Example: Document processing pipeline: Text extraction → Summarization → Translation → Formatting

2. Routing Pattern

What it is: A routing agent analyzes the input and forwards it to the most appropriate specialized agent.

Complexity: Medium

When to use

• Different inputs require different specialized handling
• Conditional logic determines execution paths

Key characteristics

• Initial classification step determines routing
• Specialized agents for different categories
• Can use traditional classification algorithms or LLM based routing

Example: Customer support system routing requests to billing, technical, or sales agents.

3. Parallelization Pattern

What it is: Multiple agents execute tasks concurrently, and results are aggregated at the end.

Complexity: Medium

When to use

• Tasks are independent
• Speed and coverage are critical
• No strict execution order is required

Key characteristics

• Concurrent execution
• Faster response times
• Requires result aggregation logic

Example: Market analysis where agents independently evaluate competitors, trends, and risks.

4. Evaluator–Optimizer Pattern

What it is: One agent generates output, another evaluates it, and feedback is used to iteratively improve results.

Complexity: High

When to use

• When you have clear evaluation criteria and iterative refinement provides measurable value

Key characteristics

• Iterative refinement loop
• Continuous improvement
• Clear evaluation criteria and pre-defined termination condition to avoid infinite loops.

Example: Code generation system where a reviewer agent validates performance, security, and best practices.

5. Orchestrator–Worker Pattern

What it is: A central orchestrator decomposes a complex task into subtasks and assigns them to worker agents.

Complexity: High

When to use

• You need dynamics task decomposition
• Centralized coordination is required

Key characteristics

• Task decomposition
• High scalability and extensibility

Example: Enterprise data pipeline handling ingestion, validation, transformation, enrichment, and publishing.

6. Handoff Pattern

What it is: Responsibility for a task is explicitly transferred from one agent to another.

Complexity: Medium

When to use

• Ownership must shift during execution
• Different agents handle different lifecycle stages
• Context continuity is required

Key characteristics

• Clear handoff criteria
• Context/State preservation
• Reduced cognitive load per agent

Example: AI chatbot handing off from a FAQ agent to a troubleshooting or escalation agent.