👾 Multi-Agent Architectures: 5 Key Designs for AI Systems

When building AI systems, you have two main options: a single agent or multiple agents working together. A single agent uses one LLM as the brain of the operation. It handles all tasks, manages tools, and evaluates results. This works for simple tasks. But complex problems need a different approach.

Multi-agent systems split work among specialized agents. Each handles specific tasks. Often, one agent acts as the lead coordinator. This approach solves a common problem: performance drops when one agent tries to do too much.

By creating agents with smaller, well-defined tasks, each can excel at its role. You can use different models for different agents based on their needs. Some agents can even communicate with agents on other systems (agent-to-agent or A2A communication).

Let's look at six architectures to arrange these agents.

1. Hierarchical (Vertical) Architecture

In this setup, a supervisor agent coordinates several specialized agents beneath it. The supervisor delegates tasks, manages workflow, and synthesizes results.

Example: A content research system where one agent retrieves information from internal databases, another gathers public information from web searches, and a third accesses personal accounts like email or chat histories. The supervisor combines these findings into a comprehensive report.

2. Human-in-the-Loop Architecture

This architecture integrates human verification at critical decision points. The system pauses for human approval before proceeding to the next steps.

Example: A contract drafting system where agents create different sections of legal documents, but a human lawyer reviews each section before the final assembly. This maintains quality control while automating routine aspects.

3. Network (Horizontal) Architecture

Agents communicate directly with each other in a decentralized network. There's no strict hierarchy, allowing for flexible collaboration.

Example: A customer service system where specialized agents handle different aspects of customer inquiries (billing, technical support, shipping) and communicate directly with each other to resolve complex issues without centralized coordination.

4. Sequential Architecture

Agents work in a defined sequence, with each agent's output becoming the next agent's input.

Example: A research assistant with three sequential agents: the first retrieves information from vector search, the second gathers additional details from web searches based on those initial findings, and the final agent synthesizes all information into a comprehensive response.

5. Data Transformation Architecture

This architecture focuses on agents that process and transform data at different stages.

Example: A content system where one agent transforms raw financial data into structured formats, another enriches it with market context, and a third converts the enriched data into natural language reports tailored to different audience needs.

Common Agentic Patterns

Beyond these architectures, several patterns can be applied across different systems:

• Loop Pattern: Agents work iteratively on tasks, refining outputs through multiple passes.

• Parallel Pattern: Multiple agents work simultaneously on different parts of a complex task.

• Router Pattern: A specialized agent evaluates incoming requests and directs them to the most appropriate agent.

• Aggregator/Synthesizer Pattern: A dedicated agent collects and combines outputs from multiple sources into coherent results.

The power of multi-agent systems comes from this flexibility. By combining different architectures and patterns, you can create AI systems that handle complex workflows while maintaining specialized expertise in each component.

As these systems evolve, we're seeing more sophisticated collaboration between agents. The ability to break down complex tasks into specialized roles mirrors how effective human teams operate.

Multi-agent architectures create more capable, reliable, and scalable AI solutions. I covered just a few examples. They show how to think about breaking down complex tasks into separate agents.

The real power comes from how you connect these pieces into a complete system. Start with these building blocks. Then create architectures that fit your specific needs.