How to Build Multi Agent Systems

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Airtable

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AI agents are changing what's possible at work, yet each individual agent can take you only so far. For the kind of complex, multi-stage workflows businesses need to run, the real benefit is in building multi-agent systems: coordinated teams of specialized AI agents that divide tasks and work collaboratively to reach a goal.

This guide covers what multi-agent systems are, how they work, when to use them, and how to build your first one.

What is a multi-agent system?

A multi-agent system is a collection of AI agents that work together to accomplish tasks that are too difficult or involved for a single agent to handle well. Instead, each agent in the system has a defined role, a specific set of tools, and clear inputs and outputs. Together, they form a coordinated team capable of running sophisticated workflows.

Think about how a high-performing team operates. You don't ask your data analyst to also write executive summaries and manage the project timeline. You hire specialists, give each person a clear job, and let them collaborate. Multi-agent systems work the same way. Instead of one AI agent trying to do everything, you build a team of specialists that each do one thing well.

Single agent versus multi-agent systems

A single agent can handle a range of tasks. For example, it can search the web, summarize documents, draft emails, or manage simple workflows. But as tasks grow more complex, you begin to see cracks in the agent’s work.

Complex, multi-step work requires different skills at different stages. It’s a large, near impossible job to optimize a single agent's prompt to handle all skills; the more you ask of the agent, the more its performance degrades.

Here's how single agents and multi-agent systems compare:

Single agent: Handles one task at a time with a focused prompt and limited toolset. Best for well-defined, contained tasks like drafting a document, answering a question, or processing a form.
Multi-agent system: Handles complex, multi-stage workflows by breaking them into parallel or sequential tasks assigned to specialized agents. Best for work that involves multiple skill sets, high volume, or continuous operation.

The difference is outputs that are less generic and more consistent. Instead of tasking a single agent to run a weekly business intelligence report, you create a multi-agent system where one agent collects data, one analyzes patterns, and a third writes the executive summary. Together, their work is faster, more accurate, and can continue running without you needed to reprompt.

How do multi-agent systems work?

Multi-agent systems work by breaking complex workflows into stages, assigning each stage to a specialized agent, and connecting those agents through a shared operational surface.

Agents

If we take a closer look, the agent assigned to each stage has four defining elements:

A name and core responsibility: one job (this should be simple enough to describe in a single sentence)
Required inputs: what it needs to start working (e.g., shared context and specific guidelines or types of data)
Expected outputs: what it produces for the next stage or end user (e.g., analysis and recommendations vs. a finished report)
Tools and integrations: what it can access to do its job (e.g., Which tools can it read? Where is it allowed to update data?)

Shared context

You need to set up each agent for success similar to how you’d onboard a new employee filling a specific role. Determine which company-wide information or data they need access to, along with anything more specific to their job. The important thing is that each agent within the system can reference shared context as they pass work between them. This shared context may live in shared documents (for narrative handoffs like analysis or briefs), shared tables (for structured data like research findings), and persistent agent memories (for information every agent should always know, like company priorities or key competitors).

Collaboration

There are different agent orchestration patterns you might design for your multi-agent system, depending on what needs to be accomplished and the order the work can be done. Sometimes that’s sequential, but sometimes two agents may execute tasks simultaneously. For example, a pipeline triggers work linearly from one agent to the next. Or, parallel specialists may work simultaneously on different parts of the same problem. A hub-and-spoke pattern uses a coordinator agent to dispatch tasks to specialists, and a review loop sends work back through an agent if it doesn't meet quality criteria. Once an agent finishes its job, its output becomes the next agent's input, and the handoffs continue until the workflow is complete, and often without any human intervention.

Use cases of multi-agent systems

Multi-agent systems are well-suited for tasks that are so structured and repetitive you'd confidently hand them to an intern, as well as more complex tasks that just aren’t possible to manage scale, such as reading every customer interaction across an entire quarter or continuously auditing hundreds of assets. Using this last example, you’d train an agent to look for the same flags a human might, but the agent can do it much faster.

Here are some common use cases across business functions:

Business intelligence: One agent collects market data and competitor activity, a second identifies patterns and strategic shifts, a third produces an executive-ready briefing that’s delivered every Monday morning at a specified time.
Order fulfillment: Agents verify inventory, reserve stock, create shipment records, update order status, and notify customers, naturally moving each order through its lifecycle without any hiccups, delays, or manual intervention.
Content production: Research agents pull source material, writing agents draft content, review agents check for quality and compliance, and publishing agents route final output to the right channels.
Customer support: Triage agents classify and route incoming requests, specialist agents handle domain-specific questions, and escalation agents flag issues that need human review.
Financial operations: Data agents pull figures from multiple sources, analysis agents validate against historical baselines, and reporting agents compile weekly business reviews, drastically reducing the time it takes to do all of this manually.

Advantages of multi-agent systems

Gartner predicts that agentic AI is expected to autonomously resolve 80% of common customer service issues by 2029, leading to a 30% reduction in operational costs. That’s significant. Here’s why multi-agent systems offer advantages over single-agent approaches and traditional automation:

Higher output quality. Specialized agents with focused system prompts outperform generalist agents on complex tasks as each agent is optimized for exactly one job.
Scalability. Parallel agents can process high volumes of work simultaneously — something a single agent can't do.
Continuous improvement. As your pipeline runs over time, agent corrections get stored as memories and repeated patterns become reusable skills. Your system gets better the longer it operates.
Resilience. Modular systems are easier to debug and improve. If one agent underperforms, you update its prompt or tools without rebuilding the entire system.
Human focus on high-value work. When agents handle repetitive and time-consuming processes, your team can focus on defining and refining agentic systems, making judgement calls on anything escalated or flagged, and on the kind of time-bound strategic projects they’re routinely pulled away from. Agents can handle a marketing onboarding nurture flow, for example, while your team focuses on their annual tentpole report.

Challenges of multi-agent systems

Naturally, multi-agent systems are more complex than single-agent setups. They are powerful, but there are some challenges to be mindful of as you design your system:

Coordination: Agents don't communicate automatically (or meet around the proverbial watercooler), so it’s up to you to design the handoffs. If agents don’t share important context or the output formats between agents aren’t clearly mapped, a pipeline can break down mid-run.
Partial completion risk: In multi-step workflows, a failure at step three can leave the system in an inconsistent state if you haven't designed for potential error handling. Think through some potential “What if?” scenarios.
Observability: When something goes wrong, you need to be able to see what happened, when, and why. Without traceability fields and audit logs, debugging a multi-agent system is difficult. Ensure the platforms you use provide transparency into agent decisions and actions.
Trust and verification: Especially in finance and operations, agent output needs to be validated before it reaches stakeholders. Building verification steps into your pipeline is critical for adoption.
Data quality dependency: Agents are only as good as the data they work with. Inconsistent field naming, duplicate records, and outdated information all produce unreliable agent behavior.

Core components of multi-agent systems

Every multi-agent system is built on the same underlying components, regardless of the platform or use case:

Specialized agents: Each agent has a name, a core responsibility, a system prompt that defines its behavior, and a set of tools it can use. The goal is for that agent to be laser-focused and to become an expert at its task.
Shared context: This is a common workspace, like Airtable, where agents access shared information, whether that’s documents passed along or structured data from an underlying database. Agent memory plays a role here, as persistent memories carry information and preferences that agents may need ongoing.
Triggers and automation: These are the mechanisms that set agents in motion — time-based schedules, communication events (like a Slack message), or webhook-driven data events (like a record being created or a status changing).
Tools and integrations: These are the external systems your agents need to access and/or move between. They might include your Airtable bases, data warehouses, Slack channels, or Google Workspace, for example. You determine what agents can do within each environment.
Observability layer: Your tools or platforms should provide audit fields, action logs, and a monitoring dashboard so you can see what each agent did, when, and based on what data. This is what lets you debug, improve, and build trust in the system over time.

How to implement a multi-agent system

While working with multiple agents is more involved than working with just one, you don’t need to start with your most complex workflows. Instead, look for workflows that include only two or three stages (and agents), design your system, let them run for a week, and then iterate from there. Here are five tips to getting started.

1. Choose the right workflows to orchestrate

Audit your recurring workflows and determine the simplest and most straightforward vs. the ones that are difficult at scale. From there, consider which workflows have natural seams where different skillsets are required. A weekly briefing, for example, has a clear collection stage, an analysis stage, and a communication stage. These are three distinct jobs that are candidates for agent creation.

2. Prepare your data before you build your agents

Evaluating your AI readiness starts with looking at your data quality. Agents can’t produce accurate outputs when the data is inconsistent, ambiguous, or out of date. Before building, ensure that your data is structured, data labels are unambiguous, and that your data is up to date.

3. Design each agent

Give each agent a name, define its responsibility, what information it needs, and what you expect from it. Really pause to consider what "good" looks like at each stage (and where manual processes sometimes break down). For example, a research agent can deliver findings, but if they’re incomplete, then it hasn’t actually succeeded. By contrast, a reporting agent that delivers a clear, concise summary that doesn’t require editing has met its goal.

4. Build your communication layer with intention

Think about how you hire — it’s strategic. When you want someone with great expertise to come on board, you prepare and give them access to everything they’ll need. And as capable as agents are, they don’t begin that way — you also need to prepare them. Think through the handoffs, the context needed, memory requirements, and how agents will be able to speak to one another and access the same info that your human team does. It’s generally a good idea to build in a verification layer as part of the pipeline, as quality assurance, before agent output goes live.

5. Start with one workflow before adding others

Multi-agent systems are still new, so it’s best to get started now and learn from the process, and to hold off scaling until you’ve established your own best practices around prompting, validation requirements, agent memory, and/or data cleanliness.

Invest in a system of record to support your agents

Great multi-agent systems need a foundation. That foundation is a system of record — a single source of truth where your teams, agents, data, and workflows all live together. Without one, agents can't access shared context or connect their outputs to real workflows — which is what stalls agentic AI adoption.

Airtable is built for exactly this. It's a system of record designed for human-agent collaboration, giving both a shared operational surface to work across. Agents connect directly or via MCP, and your teams can build the workflows they know best — no ticket to engineering required. And when you need to know what your agents are actually doing, the visibility is already there: outputs, decisions, and context stay linked in the same views, so you can govern agent work at scale without bolting on a separate observability layer.

Airtable is your agents’ home base

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Frequently asked questions

There's no fixed number of agents for multi-agent systems, though it’s generally a good idea to begin with a 2- or 3-agent workflow before moving into more complex multi-agent systems.

Use multi-agent systems when your workflow has distinct stages that require different skills, or when volume exceeds what a single agent can handle, or when you need the workflow to be always-on.

A simple but common example is preparing a weekly competitive intelligence report. Here’s what this might look like within a multi-agent system: one agent (the Data Collector) scans competitor news, product launches, and hiring activity every Monday morning; a second agent (the Analyst) reads those findings, identifies patterns, and compares them against company strategy; a third agent (the Reporter) synthesizes the analysis into a concise executive briefing, delivered to a Slack channel before the workday begins.

While AI agents can help individuals do more, they don’t necessarily connect to the higher value multi-stage workflows that businesses need. Multi-agent systems let you automate those workflows from end to end, using the same key information, while maintaining quality at each stage.

Multi-agent systems help teams move from "how do we keep up?" to "how do we scale what's working?" Agents handle the parts that are easy (e.g., data collection, synthesis, and report formatting) and reserve the rest for human judgment (i.e., strategy, stakeholder relationships, quality review, and fostering continuous improvement).

Humans divide agents' tasks based on workflow stages and skill sets. Each agent gets the inputs it needs and produces a defined output that the next agent can pick up and use.

About the author

Airtableis the AI-native platform that is the easiest way for teams to build trusted AI apps to accelerate business operations and deploy embedded AI agents at enterprise scale. Across every industry, leading enterprises trust Airtable to power workflows and transform their most critical business processes in product operations, marketing operations, and more – all with the power of AI built-in. More than 500,000 organizations, including 80% of the Fortune 100, rely on Airtable's AI-native platform to accelerate work, automate complex workflows, and turn the power of AI into measurable business impact.