Hot take: I really liked the "Stranger Things" finale. Yeah, the final fight was formulaic, but I love a good boss fight: everyone has a specialty, and they all contribute to the win.
That's how high-performing teams work everywhere, whether it's a psychic, demon-fighting group of teenagers or an automation system.Â
AI agents can do a lot on their own, but they need support to tackle more complex problems. When they're organized into systems, they can specialize, share information, and delegate. Like Dustin and Steve, they're better together.
In this guide, I'll break down what a multi-agent system is, how it works, where it helps (and where it creates new problems), and a few practical ways teams are using multi-agent setups at work.
Table of contents:
What is a multi-agent system?
A multi-agent system (MAS) is a collection of specialized AI agents that act autonomously according to a set of instructions or rules. These agents can share information, delegate tasks to one another, exchange outputs, and take on different responsibilities.Â
The keyword here is "system." MAS functions as a cohesive unit, not just as independent agents running in parallel. This coordination is achieved through defined roles and intentional handoffs, where one agent's output becomes another's input. And ideally, the whole workflow has a shared source of truth (like a database, a table, or your CRM) so agents aren't inventing context as they go.
For example, on Zapier, you might have four agents, each with a specific job, that communicate with each other to get their tasks done. You set the hierarchy, the scope of work, and the communication pathways. Then, the bots execute. They may not band together as epically as a group of teenagers fighting a giant monster, but they do rise to the occasion together.
Single-agent systems vs. multi-agent systems
A single-agent system is one intelligent AI agent that performs defined tasks. For example, with one Zapier agent, you could generate a meeting transcript and send it to participants, summarize your daily calendar in Slack, or assign to-dos based on overdue tasks.
Multi-agent systems combine multiple autonomous AI agents to achieve more complex workflows. For example, with Zapier, you could create a multi-agent system for meeting follow-ups:
One agent takes a customer call and extracts all the feature requests mentioned during the call. Then it sends the feature requests to a second agent.Â
When the second agent receives the requests, it creates a product requirements document (PRD) based on a specific set of instructions and any desired data integrations. It then sends the document to the first agent.
The first agent delivers the completed PRD to a human.
Multiple specialized agents can be more reliable than a single agent for multi-step workflows because you can narrow the scope at each step. Large language models (LLMs) are probabilistic, not deterministic, meaning they generate likely outputs rather than guaranteed ones. So the more you ask one agent to do in one go, the more chances it has to go off-script.
How do multi-agent systems work?
Multi-agent systems group AI agents together, and you can set rules for how they interact with each other. Some agents execute tasks, others share information, and others act as managers. The agents are individual entities with defined behaviors working within structures.
So let's look at the different components of multi-agent systems.
Agents
AI agents are entities designed to achieve specific goals autonomously. Most computer programs follow "if-this-then-that" rules, which make them deterministic, but agents perceive their environment and make independent decisions to solve problems. They're based on LLMs and other AI models, which produce probabilistic outputs.
Agents can take actions (using APIs, MCP, and other tools), not just generate text. The catch is that an agent is only as trustworthy as its instructions, permissions, and the data you give it.
Agents are versatile, and each one is capable of handling multi-step tasks. For example, you could use a Zapier agent to automate lead capture and follow-up. When someone submits a form, the agent can score the lead, send it to your CRM, and send a personalized welcome email.
Structures
For agents to work together, they need structures that define how they relate to each other.
Common multi-agent structures include:
Hierarchical, where some agents act as managers, telling other agents what to do and setting priorities. (Useful when you want approvals, prioritization, or escalation paths.)
Sequential, where events and responses occur in a chain, with each triggering the next agent. Agents can call other agents to perform tasks. (Useful for pipelines like "extract > draft > QA > send.")
Decentralized, where agents negotiate or compete with each other to determine the best agent for any particular task. (Useful when tasks can be routed to the "best fit" agent based on context.)
Zapier's multi-agent workflows often follow sequential structures, allowing you to set rules such as, "After completing this, call another agent to do the next step."
Behaviors
Each agent in a multi-agent system has a set of defined behaviors for completing tasks and interacting with other agents.
Here are the general categories that behaviors can fall into:
Reactive: An agent waits for an instruction or environmental condition before performing a defined task. Example: An agent kicks off when it detects a new form submission.
Reflex: An agent follows action and response rules but considers current and past environmental states to make decisions. Example: An agent fills information into a form and detects incomplete information, triggering an alert and pausing further progress.
Goals: The agents understand their environment and make autonomous decisions to achieve a specific goal. Example: An agent searches other databases or public information to fill in information about a contact that it wasn't directly given.
Utility: The agent can handle additional complex variables in their tasks, like managing resources, time, or making progress toward a larger goal. Example: An agent classifies and labels tasks based on priority, urgency, or sentiment, and sends them to the appropriate human or agent.
Learning: These agents learn from previous experiences, their environment, and data, enabling them to operate in unfamiliar or unpredictable environments and retain more complex information. Example: A customer service chatbot adjusts its responses to future inquiries based on user feedback and preferences.
In a multi-agent system, different agents might have different levels of behavior. An agent acting as a manager might have advanced awareness of its environment in order to delegate tasks. But an agent that summarizes documents doesn't need that additional scope, and performs better when it's limited to the documents given to it.
Use cases for multi-agent systems
So why not just build one mega-agent and call it a day? Because most business workflows aren't one task. They're a chain of tasks with handoffs, permissions, and constraints baked in.
In reality, agents work best when each has a specific part to play and guardrails defining when and how to perform actions. Here are a few real examples of how companies are using multiple AI agents in the workplace.
Sales
To fuel business growth, NisonCo uses a multi-agent system to automate call follow-ups and estimate hourly costs. Previously, the company struggled to identify and act on smaller commitments that weren't noted as action items. After implementing the multi-agent system, the team saw a 48% increase in leads and saved $30,000 annually.
Here's the workflow:
An AI agent reviews a call transcript and records all the action items.
It logs the details in the CRM, which triggers a Slack notification.
A second agent drafts follow-up emails based on the notes that the first agent took.
Another agent estimates the wage cost associated with each scheduled call.
Marketing
Here's how Zapier editor Steph Spector uses a multi-agent system to research and create product feature guides for Zapier:
She submits a feature name, notes, and links to relevant help documents to a Zapier Table.
Agent 1 is a user insights researcher: It searches the Zapier Community forum for common questions, issues, and customer love points, then summarizes key insights to inform the guide's tone and focus.
Agent 2 is a use case researcher: It calls Glean to search Zapier's documentation for solid use cases to ensure guides show real-world, practical examples.
Agent 3 is a writer: It pulls the research into a basic feature guide using a template.
Agents 4-7 are editors: They revise the draft, each focusing on a particular editorial dimension.
This is a good example of "agents as specialists"—research, drafting, and editing (even each type of editing!) stay separate, so each step is easier to control and evaluate.
Social media
Emily Mabie, AI automation engineer for HR at Zapier, uses a multi-agent workflow to manage her LinkedIn posting schedule. When someone wants her to draft a LinkedIn post, they message her in Slack, she responds with an emoji, and the workflow begins.
The first agent gathers all the information from the request and sends it to the second agent.
The second agent checks Emily's LinkedIn calendar, identifies any conflicts, and books an appropriate time with enough context for her to write a post.
Want to automatically schedule your LinkedIn posting opportunities based on availability and urgency, like Emily? Try the prebuilt templates she uses, or customize your own workflows to match your social channels and campaign needs:
This agent automates the process of retrieving Slack data when a LinkedIn ToDo emoji is used.
Schedule LinkedIn posts for employer branding, job openings, and recruiting content -- automatically conflict-checked against your calendar.
Benefits of multi-agent systems
Multi-agent systems use agents as building blocks to create more complex, autonomous systems. Agents can share information and coordinate across different environments. And unlike our favorite "Stranger Things" heroine Eleven, they don't need to get dunked in big tubs of salt water to do it.Â
Here are some of the key benefits:
Operational efficiency: Agents automate multi-step workflows, reducing manual handoffs and eliminating bottlenecks by ensuring tasks follow an optimal sequence.
Cost control: Narrower agents can reduce rework (and the "redo it again but better" loop that eats time).
Seamless scalability: Adding or reconfiguring agents is straightforward, allowing you to adapt to higher workloads or new processes without overhauling the entire system.
Error reduction and consistency: Built-in guardrails guide agent activity along defined paths. This minimizes mistakes, reduces rework, and maintains data consistency across different workflows.
Enhanced decision-making: Agents synthesize information in real time, enabling quick and coordinated responses.
Expanded automation potential: Agents work together to move beyond simple tasks and handle complex, cross-functional processes—like summarizing months of work for specific departments—unlocking new business opportunities.
Reduced AI sprawl: A unified framework prevents fragmented deployments, improves visibility, and ensures a single set of rules applies to all agents in a workflow.
Challenges of multi-agent systems
Multi-agent systems come with pitfalls because of the nondeterministic nature of AI agents. The trick is deciding where autonomy helps and where you need tighter control.
Initial setup: Developing agents and the systems they use is often complex. Zapier reduces that barrier with a no-code platform that lets you build integrations and workflows without writing code.
Unexpected actions: Agents are based on probabilistic large language models, so they might do things you don't intend. Multi-agent systems lower this risk by narrowing the role of each agent, but issues can still occur. You'll need to monitor the workflows as they run and tweak the instructions as needed.
Hallucinations: Like LLMs, agents can hallucinate. Limiting their scope helps minimize these inaccuracies, and connecting agents to a system of record, like a ticketing system, CRM, or Zapier Tables, helps control where they get information.
Debugging and evaluations: Human oversight remains essential. As the complexity of AI workflows increases, agents can become prone to errors. Luckily, evaluating and debugging plug-and-play systems is easy because you can see the whole system and adjust how the bots interact without needing to code.
Data safety: Privacy and security are common concerns whenever generative AI is involved. Zapier uses enterprise-grade cloud security, and enterprise accounts are automatically opted out of LLM model training to maintain data privacy when adding agents to workflows.
Build a multi-agent system with Zapier
The best way to understand multi-agent systems is to build a small one and watch where it breaks (and then add guardrails). Zapier makes that easier by letting you build agents that work with the tools you already use and set up multi-step workflows without custom code.
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