Multi-Agent Systems: Architecture and Examples

March 1, 2026 · 8 min read

How multi-agent AI systems work — architecture, orchestration patterns, and real examples. When to use several agents instead of one, and how LLM-based multi-agent systems are built.

What is a multi-agent system?

A multi-agent system (MAS) is an architecture where several AI agents work together to solve a complex task. Each agent has its own role, tools, and responsibilities. Instead of one universal agent that does everything, you get a team of specialists that coordinate and delegate.

The key idea: complex problems are easier to solve when broken into subtasks and distributed among specialized agents. One agent researches, another writes code, a third reviews — the result is often better than a single agent trying to do it all.

Architecture of multi-agent systems

There are several common patterns for organizing multi-agent systems:

1. Sequential pipeline

Agents work in a chain: Agent A passes its result to Agent B, B to C, and so on. Example: Research Agent → Summary Agent → Report Agent. Simple to implement, but no feedback loops.

2. Manager–worker (hierarchical)

A coordinator agent receives the task, breaks it into subtasks, assigns them to specialist agents, and aggregates the results. The manager decides who does what and when. This is the most common pattern for LLM-based multi-agent systems.

3. Collaborative (peer-to-peer)

Agents communicate directly with each other, negotiate, and exchange information. No central coordinator. More flexible but harder to control and debug.

4. Debate / critique

Several agents propose solutions, then critique each other's work. The best option is selected or synthesized. Good for creative and analytical tasks where multiple perspectives matter.

Multi-agent systems with LLMs

Modern multi-agent systems are often built on large language models (LLMs). Each agent is an LLM instance with its own system prompt and tools. The orchestration layer (another LLM or a program) manages the flow: who to call, when, and how to combine results.

Popular frameworks: CrewAI, AutoGen (Microsoft), LangGraph — they provide primitives for defining agents, roles, and interaction patterns.

Examples of multi-agent systems

Example 1: Research and report pipeline

Agents: Researcher (web search, databases) → Analyst (structures and analyzes) → Writer (creates the final report).

Flow: The researcher gathers raw data, the analyst extracts insights, the writer formats everything into a readable document. Each agent focuses on one stage.

Example 2: Code development team

Agents: Architect (design) → Developer (implementation) → Reviewer (code review) → Tester (runs tests).

Flow: The architect proposes the structure, the developer writes code, the reviewer suggests improvements, the tester validates. Mimics a real dev team.

Example 3: Customer support escalation

Agents: Triage Agent (classifies requests) → FAQ Agent (answers standard questions) → Escalation Agent (hands complex cases to humans).

Flow: The triage agent routes each request. Simple questions go to the FAQ agent. Complex or sensitive cases go to the escalation agent, which creates a ticket for a human.

Example 4: Competitive intelligence

Agents: Monitor Agent (scrapes competitor sites) → Analyst Agent (compares with previous data) → Reporter Agent (sends digest to the team).

Flow: Daily automation: monitor collects data, analyst identifies changes, reporter formats and delivers the summary.

When to use multi-agent systems

Multi-agent architecture makes sense when:

• ✅ The task has clearly distinct stages (research → analysis → writing)
• ✅ Different subtasks need different tools (one agent needs SQL, another needs web search)
• ✅ You want specialized expertise (one agent for code, another for legal text)
• ✅ A single agent gets confused or produces worse results on complex tasks

Start with one agent. Add more only when you hit its limits — more agents mean more complexity, latency, and cost.

Challenges and pitfalls

• Latency: several agents in sequence = several LLM calls. The total time grows.
• Cost: each agent consumes tokens. A 4-agent pipeline can cost 4x more than a single agent.
• Orchestration complexity: you need to define clear handoffs, handle failures, and avoid infinite loops.
• Debugging: when something goes wrong, it's harder to trace which agent made the mistake.

Conclusion

Multi-agent systems are a powerful pattern for complex tasks that benefit from specialization and division of labor. Start with a simple pipeline (2–3 agents), use a manager–worker pattern for flexibility, and scale only when a single agent is no longer enough.