5 Proven Multi-Agent Architectures: AI Agent Building Blocks

We are seeing a shift from single-agent workflows to multi-agent architectures. Multi-agents mark a significant advancement in how we build intelligent systems. It addresses a fundamental challenge in AI development: Handling complex tasks without sacrificing accuracy.

Multi-agent architectures provide the answer. By distributing work among several AI agents, these systems can solve complex workflows through intelligent coordination. Each agent focuses on what it does best, creating a more robust and flexible system overall.

We'll explore five proven multi-agent architectures and four common patterns used in enterprise-grade agentic systems. You'll learn how to combine these building blocks to create more capable and reliable AI solutions.

When designing AI systems, you have two main options: a single agent or a multi-agent architecture.

A single agent uses one Large Language Model (LLM) as the brain of the operation. It handles all tasks, manages tools, and evaluates results. This works for simple tasks where the scope is limited and well-defined.

Single-agent systems are:

• Simpler to implement
• Easier to maintain
• Sufficient for straightforward applications

But they face limitations as complexity increases. Performance often drops when one agent tries to do too much.

Multi-agent systems split work among specialized agents. Each handles specific tasks and often, one agent acts as the lead coordinator.

Multi-agent systems offer several advantages:

• Specialization: Each agent excels at a specific role
• Scalability: Systems can grow by adding more specialized agents
• Flexibility: Different models can be used for different agents based on their needs
• Cross-System Communication: Some agents can communicate with agents on other systems through agent-to-agent (A2A) communication

This approach solves a common problem: performance degradation when the number of instructions increases. By creating agents with smaller, well-defined tasks, each can excel at its role without forgetting parts of the instruction.

Let's examine five proven architectures for organizing multiple AI agents into cohesive systems.

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

Hierarchical Multi AI Agent Architecture showing a supervisor at the top connected to multiple task-specific agents below

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.

Business applications: This architecture works well for complex projects requiring coordination between different specialized functions, like market research, product development, or content creation systems.

This architecture integrates human verification at critical decision points. The system pauses for human approval before proceeding to the next steps, creating a collaborative human-AI workflow.

Human-in-the-loop multi AI agent architecture showing AI agents connected to human verification points

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

Business applications: Ideal for high-stakes domains requiring human oversight, such as legal document preparation, medical diagnosis assistance, or financial advisory services.

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

Network multi AI agent architecture showing interconnected agents without hierarchical structure

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

Business applications: Works well in scenarios requiring dynamic collaboration and information sharing, such as incident response systems, design tools, or distributed platforms.

Agents handover work in a sequence, with each agent's output becoming the next agent's input. This creates a pipeline where information flows linearly from start to finish.

Sequential multi AI agent architecture showing agents arranged in a linear chain

Example: A research assistant with three sequential agents: the first retrieves information from a vector database, the second gathers additional details from web searches based on those initial findings, and the last agent aggregates all information into a summary.

Business applications: Effective for workflows with clear stages, such as content creation pipelines, data processing systems, or approval processes.

This architecture focuses on agents that process and transform data at different stages, each adding value or refining the information.

Data transformation multi AI agent architecture showing data flowing through transformative agents

Example: A system where one agent transforms raw financial data into structured output, another enriches it with market insights, and a third converts the enriched data into reports tailored to the audience.

Business applications: Particularly valuable for data-intensive applications like business intelligence tools, reporting systems, or personalized content.

Beyond the overall architectures, several patterns can be applied across different multi-agent systems. These building blocks enhance specific capabilities regardless of the chosen architecture.

Common multi AI agent patterns showing the four patterns described below

Agents work iteratively on tasks, refining outputs through multiple passes. Each iteration improves the result until it meets quality thresholds.

Example scenario: A content editing system where an agent generates text, another reviews and suggests improvements, and the first agent incorporates those suggestions. This loop continues until the content meets quality standards.

Multiple agents work simultaneously on different parts of a complex task, dramatically improving processing speed for suitable workloads.

Example scenario: A data analysis system where multiple agents process different datasets concurrently, then share their findings for synthesis into a comprehensive analysis.

An agent evaluates incoming requests and directs them to the most appropriate agent based on content, intent, or specific requirements.

Example scenario: A platform that analyzes incoming customer queries and routes them to specialized agents.

An agent collects and combines outputs from multiple sources into unified results.

Example scenario: A system where multiple agents gather information from different sources, and an aggregator agent combines these findings into a market analysis report.

These four patterns can be combined with any of the architectures described earlier to create multi-agent AI systems tailored to specific business needs.

The power of multi-agent architectures lies in how closely they mirror effective human team structures. Just as human organizations benefit from specialized roles and clear communication channels, multi-agent AI systems gain efficiency through similar principles.

By breaking down complex tasks into specialized roles, multi-agent systems achieve:

• Scalability: Systems can grow by adding new specialized agents without rebuilding the entire architecture
• Reliability: Multiple agents provide redundancy and error-checking capabilities
• Domain-specific optimization: Each agent can be optimized for its specific task rather than compromising for general capabilities

Agent-to-agent (A2A) communication extends these benefits across systems. AI agents can share information and coordinate actions across different environments, creating near-unlimited possibilities.

Multi-agent architectures create more capable, reliable, and scalable AI solutions. The examples covered here show how to think about breaking down complex tasks into separate, specialized agents.

The true power comes from how you connect these pieces into a complete system. Start with these building blocks - hierarchical, human-in-the-loop, network, sequential, or data transformation architectures. Then layer in patterns like loops, parallel processing, routing, and aggregation.

By combining these elements to fit your specific workflows, you'll create AI systems that handle complexity well and deliver superior results.

This article is based on a recent episode of Agents Made Simple – The weekly newsletter on state-of-the-art AI agent technology. Subscribe here for free.