What are multi-agent systems?

Multi-agent systems (MAS) are groups of specialized components that work together to solve problems a single AI agent can’t manage as effectively. They streamline processes, coordinate actions, and process data throughout an organization. For example, handling invoices may involve one agent extracting the data and another routing it to the right system or team.

If your business content is unstructured and scattered across platforms and apps, multiple agents give you an edge. Each agent has a clear role, whether it’s finding information, breaking it down, explaining it, or putting it into action. Working together, AI-powered agents help you make better use of your data and support smarter decisions.

Key highlights:

• Multi-agent systems are groups of specialized AI-powered agents that work together to solve complex problems or perform time-consuming tasks
• In a multi-agent system, each agent handles a set sequence of tasks or responds dynamically to new information and to each other in real time
• Businesses use multi-agent frameworks to connect tools and teams across the organization, which supports better data-driven decisions and greater efficiency
• Multiple agents streamline processes by coordinating document workflows, enabling faster search and clear answers, and syncing data across systems
• Box, the leader in Intelligent Content Management, empowers enterprises to build and deploy multi-agent AI systems that unlock unstructured data value, automate workflows, and maintain strict security and governance

How does a multi-agent system work?

A multi-agent system works by letting agents handle different parts of a process, either inside one system or across multiple tools. Each AI agent acts independently but aligns with a shared goal, using a common set of rules.

In multi-agent frameworks, agents interact in two ways:

1. Coordinated execution: AI agents run in a specific sequence — like in intelligent document processing, where one agent extracts data and another pulls information to generate reports
2. Dynamic interaction: Agents respond to changing inputs and each other in real time — like a customer service system where an agent routes the initial request and another provides a comprehensive answer based on the question

See how an AI content agent puts your unstructured data to work.

What’s the difference between agents and multi-agent systems?

The key difference between agents and multi-agent systems lies in collaboration. A system depends on AI agents coordinating their actions, while a single agent focuses on a specific task. When agents interact and work together, the system can handle more complex workflows.

Take HR onboarding. You could deploy an individual agent to answer policy questions. Or you could orchestrate a sequence of tasks where multiple AI agents work in sync:

• One collecting all the documents needed
• A second handling questions from employees
• And a third generating summaries for the hiring manager

Discover how to enhance your business strategy with Content + AI.

What are the benefits of using multiple agent systems?

The benefits of using multiple agent systems go beyond time savings or faster task completion. These solutions help you build a framework that connects tools and teams. By integrating and orchestrating systems across the organization, you give departments, from marketing to finance, the ability to act on shared data and reduce manual handoffs.

Benefits of building a network of AI agent integrations include:

• Unstructured data insights: Multi-agent AI systems excel at making sense of emails, documents, and images that don’t fit neatly into databases or spreadsheets. Each agent adds its expertise, then they combine their insights across the network. This content intelligence enables decisions that unlock deeper value from your unstructured data.
• Reduced costs from custom development: With low-code or no-code tools, your business can design and configure multi-agent systems without waiting on IT or developers. All departments can adjust workflows and collaborate efficiently, without expensive custom builds.
• Scalability: Multiple agent systems grow with your demands, including the ability to process increasing volumes of data. You can add or adjust functions over time without requiring costly upfront investments.
• Interoperability: Agent systems allow you to orchestrate workflows across multiple platforms, helping break down data silos. Agent communication makes it easier to cut down on manual information transfers and keep workflows running across the entire organization.
• ROI visibility: Multi-agent systems provide the visibility and data necessary to justify your investment. They help quantify key benefits such as time and cost savings, error reduction, faster delivery of value, and strategic impact, so you can measure exactly how the system improves your business outcomes.
• Increased productivity: By taking on repetitive, fragmented work, agent-based systems work as AI productivity tools. Tasks get distributed efficiently, delays drop, and overall efficiency and responsiveness improve across the business.

Learn how to measure AI business impact on your operations.

What are the best use cases for multi-agent collaboration?

The best use cases for multi-agent collaboration, where multiple agents operate within the same system, include scenarios where teams process and merge data from multiple sources, need AI workflow automation at scale, and respond to events in real time.

Here are three applications that showcase the strengths of multi-agent collaboration.

1. Document workflow automation

Document workflow automation covers the full lifecycle of your business documents, from creation, classification, and review to compliance tracking. In many organizations, these steps often involve manual handoffs that slow down approvals.

In a multi-agent architecture, each stage runs in sync. One AI agent can categorize documents by type and priority, while another can store the final version in the correct content repository. The agents communicate in real time, so if a document is updated or flagged, the workflow adjusts instantly without manual intervention.

Review key document categorization methods you might want to try.

2. Enterprise search and Q&A

Researchers often waste hours searching for articles and records instead of focusing on their work. Engineering departments, marketing teams, and financial analysts face the same challenge.

Using multiple agents, one can search for relevant files across different systems while another applies natural language processing (NLP) to pull key points and generate clear summaries. NLP makes interaction feel like a conversation, letting users ask questions about documents and receive precise, context-aware answers.

See how next-level large language models (LLMs) understand complex enterprise data.

3. Multi-agent interaction across platforms

Imagine your sales team updating a customer contract stored in your document management system. Normally, changes like new pricing or renewal dates mean manually updating client records in your CRM. This back-and-forth risks data mismatches and opens the door for errors.

But when your document system and CRM work together via agentic workflows, contract updates automatically sync with client profiles and sales opportunities. This way, sales reps always have the latest info without chasing paperwork.

Key points to consider when implementing systems with multiple agents

According to the State of AI in the Enterprise report by Box, 87% of organizations have at least started piloting basic AI agents, while 41% are already piloting fully autonomous operations in select areas, a sign of growing trust in AI’s ability to deliver in real-world scenarios. If you’re heading that way, check these key considerations for working with multi-agent systems.

1. Inter-agent communication protocols: Decide how agents will exchange information, request help, and handle errors. Consistency here avoids confusion and data mismatches.
2. Security and access control: Limit what each agent can see and do with file and folder permissions, especially when working with sensitive systems or content.
3. Scalability and multi-agent orchestration: Think about how agents will work together as demand increases. You may need scalable cloud data storage and a central system to manage workflows.

Take a look at the benefits of adopting a workflow management system.

Build a multi-agent architecture for your enterprise with Box AI

With Box, you get a comprehensive platform to simplify how you create, manage, and use your enterprise content. As the leader in Intelligent Content Management, Box helps you unlock the value of your unstructured data with the power of AI, streamlining collaboration, automating workflows, and keeping your information secure every step of the way.

Box AI makes it easy to:

• Extract key data from documents to save time and reduce manual errors
• Ask questions and pull accurate insights from complex, lengthy files
• Build and customize AI agents for specific workflows using Box AI Studio
• Integrate AI into your apps with flexible APIs and advanced AI models
• Protect sensitive data with enterprise-grade security through responsible AI that respects permissions and governance controls

Contact us to build multi-agent systems that fit your enterprise workflows.

Frequently asked questions

What are some examples of systems with multiple agents?

A few real-world examples of systems with multiple agents include:

• Virtual assistants: Handle client inquiries by delivering instant answers from onboarding documents or assist legal teams by extracting key contract clauses for a faster content review process
• Support chatbots: Analyze customer interactions to provide accurate product information, speed up problem-solving, and resolve support requests faster
• Enterprise search: An autonomous agent retrieves content, and another delivers AI summaries

Check out the Box AI Explainer Series podcast to learn how AI is transforming enterprise search. Watch the full episode.

How to build a multi-agent AI system

Building a multi-agent AI system starts with identifying the specific task or workflow you want to improve. A practical, step-by-step guide:

1. Set clear success metrics: After defining the workflow, decide how to measure improvement. Focus on factors like turnaround time, user experience, accuracy, or cost.
2. Break the workflow into tasks: Divide the process into small steps that are easy to assign and manage.
3. Define inputs, outputs, and benchmarks: For each task, specify what it needs to start, what it should produce, and how to measure its performance.
4. Assign each task to a dedicated agent: Match each task with an agent designed to handle it using the right tools, models, and logic.
5. Establish communication protocols: Use shared APIs, message queues, or orchestration tools so agents can exchange data reliably.
6. Simulate real-world usage: Test how the system performs under realistic conditions, both at the agent and system levels.

Why do multi-agent LLM systems fail?

Multi-agent LLM systems fail when teams treat them as “just LLMs talking to each other” instead of carefully orchestrating them. According to Gartner, 40% of artificial intelligence projects will fail by 2027 because of poor ROI or execution. Common failure points of multi-agent systems include:

• No orchestration layer: Agents operate in isolation. There’s no shared logic or connectors to integrate workflows and systems.
• Low adaptability: Agents lack the design to evolve beyond their initial tasks. Scaling demands rework instead of expansion.
• Low user adoption: Systems focus on technical features instead of solving real user problems. Users reject tools when results don’t match business goals.

*While we maintain our steadfast commitment to offering products and services with best-in-class privacy, security, and compliance, the information provided in this blog post is not intended to constitute legal advice. We strongly encourage prospective and current customers to perform their own due diligence when assessing compliance with applicable laws.