Multi-Agent Systems: Infrastructure, Architecture & Examples

AI is moving from answering questions to completing tasks, connecting to tools, making decisions, and doing real work. As enterprise workflows get more complex, it may no longer be possible for one AI agent to control all of its steps.

This is where multi-agent systems (MAS) come in.

The work is distributed among different AI agents in a multi-agent system. Each agent specializes in one job, with a layer of orchestration on how they work together.

This approach is gaining attention as enterprises move AI from experimentation into real business processes. Gartner predicts that 40% of enterprise applications will feature task-specific AI agents by the end of 2026, compared with less than 5% in 2025. Gartner also estimates that by 2027, one third of the agentic AI deployed will involve agents with diverse capabilities working together to accomplish complex jobs.

At the same time new standards are making it easier for AI agents to communicate with tools and with other agents. MCP is about linking agents to tools, APIs and data, whereas A2A is about communication between autonomous agents.

Poorly designed multi-agent systems can be expensive, slow, hard to debug and cascades of errors can occur. In this guide, we’ll explain what multi-agent systems are, why enterprises use them, how to build multi-agent systems, their infrastructure and architecture patterns, popular frameworks, real-world examples, costs, challenges, and why multi-agent LLM systems fail.

What Is a Multi-Agent System?

A multi-agent system is an AI system, in which several specialized agents cooperate to perform a larger task or business workflow.

Think of it like a group of employees.

Instead of assigning a researcher to do all the work of researching a market, checking financial information, legal requirements, writing a report and sending it to a customer, you can divide the work up among specialists.

For example:

Research Agent → Data Analysis Agent → Compliance Agent → Report Agent

While most AI agents consist of an LLM that serves as the reasoning engine of the agent, an agent is more than an LLM. It can also have:

• Instructions
• Tools
• APIs
• Databases
• Memory
• Business rules
• Access permissions
• Guardrails
• Workflow responsibilities

Why Are Enterprises Adopting Multi-Agent Systems in AI in 2026?

Businesses have started deploying the technology for functions like content creation, file summarization, question answering, and coding.

1. Growing Enterprise Adoption

Gartner also estimates that 40% of enterprise apps will contain task-specific AI agents by the end of 2026, as opposed to less than 5% in 2025. Gartner also anticipates that collaborative agents will play a growing role as organizations take on more advanced workflows.

2. Infrastructure Is Becoming More Mature

Building agents used to take a lot of custom engineering. These days, developers have more standardized options for connecting agents to tools and to one another. Two illustrative examples in this regard are:

• MCP: Hooks up AI apps and agents to tools, APIs, data sources, and resources.
• A2A: Enables AI agents to find other AIs, assign them tasks, talk to them and share outputs.

3. AI Is Moving From Experiments to Production

Modern agent frameworks now provide capabilities such as:

• Agent handoffs
• Workflow control
• Tool calling
• Structured outputs
• Memory
• Guardrails
• Tracing
• Evaluation
• Human approval
• State management

Multi-Agent Systems vs. Single-Agent AI – When Do You Need MAS?

A single AI agent can perform many business tasks reasonably well when the workflow is simple, only a few tools are required, and one model can capture all the relevant context. A multi-agent AI system is one in which specialized agents deal with different tasks that are components of a more complex workflow.

How to Build a Multi-Agent System – Step-by-Step

A multi-agent system requires more than just interlinking several LLMs and asking them to collaborate. If you are thinking about how to build a multi-agent system, the next steps represent a practical guide for multi-agent AI development.

Step 1: Define the Workflow and Agent Roles

Start with the business process, not the AI models.

Map the complete workflow and identify where the system needs reasoning, data retrieval, decision-making, validation, or action. Divide those responsibilities into logical agent roles. An enterprise customer service workflow, for example, might involve:

• Intent Agent
• Knowledge Agent
• Resolution Agent
• Validation Agent
• Action Agent

Step 2: Choose Your Architecture Pattern

After knowing what is to be done by each agent, a multi-agent architecture pattern suited to your workflow should be chosen. A sequential architecture is appropriate when one task is dependent on the output of another.

Step 3: Select the Framework and Tech Stack

The framework you choose will have lots of influence on how easy it is to build, coordinate, test and operate the system. Popular choices are CrewAI, LangGraph, AutoGen/AG2, Google ADK, and OpenAI Agents SDK. Your technology stack may include:

• LLMs
• Agent framework
• APIs and enterprise systems
• Vector database
• State and memory layer
• Observability tools
• Security layer

If your team lacks in-house agent expertise, you can hire AI engineers experienced in multi-agent orchestration.

Step 4: Design the Communication and Memory Layer

There must be some reliable means for agents to communicate information. They also need to be able to have the right context available to them without having to bring the entire workflow history with them to each model call. Protocols like MCP can provide a means for agents to manipulate tools and data, whereas A2A can enable communication between them.

Step 5: Build, Test, and Iterate

Do not roll out the entire agent network in one go, but incrementally build the system up.

Start with the smallest useful workflow. Connect two or three agents, test the handoffs, and see if the workflow works as anticipated.

Agent-level testing should examine:

• Instruction following
• Tool selection
• Output accuracy
• Failure recovery
• Permission boundaries
• Response consistency

System-level testing should examine:

• Agent handoffs
• Workflow completion
• State management
• Error propagation
• Latency
• Token usage
• Model costs
• Conflicting agent outputs
• Human escalation

Step 6: Deploy with Observability and Guardrails

It is more than just hosting the agents and connecting the APIs to get this into production. You need visibility into what each is doing, and control over what it is allowed to do. Incorporate observability early on. Your production controls should cover:

• Authentication and authorization
• Tool permissions
• Data protection
• Output control
• Human-in-the-loop controls
• Audit trails
• Failure recovery
• Quality assurance

How Do Multi-Agent Systems Work?

A multi-agent system is one where a complex goal is decomposed into smaller goals, which are then assigned to specialized AI agents who will be coordinated by means of a workflow. A typical multi-agent system architecture follows a flow such as:

1. Perception: Breaking Complex Goals into Subtasks

It begins with a business goal or a user request that comes to the system. One perception or planning component takes that goal and analyzes it to find the individual tasks it will take to accomplish it.

2. Agent Specialization: Predominantly Powered by a Large Language Model (LLM)

Each agent generally has a large language model (LLM) working as its reasoning engine, but the LLM is not alone. The agent is provided with the model as well as role instructions, tools, data sources, memory, and permissions. For example, a multi-agent AI system might include:

• Authentication and authorization
• Tool permissions
• Data protection
• Output control
• Human-in-the-loop controls
• Audit trails
• Failure recovery
• Quality assurance

3. Inter-Agent Communication and Coordination

Once agents have started working on different subtasks, they need a way to communicate results and request additional work. This is where communication between the agents has significance in multi-agent systems.

4. Orchestration: Managing the Workflow End to End

Orchestration manages how the entire multi-agent workflow runs. It determines which agent should act, when that agent should act, what information it receives, and where its output goes next.

An agent orchestration layer can manage:

• Agent sequencing and dependencies
• Parallel task execution
• Agent handoffs
• State and workflow memory
• Tool calls
• Retries and failure recovery
• Human approval steps
• Output validation
• Model routing
• Workflow completion

Core Infrastructure for Multi-Agent AI

An LLM and a prompt are not all that is needed for a robust multi-agent system infrastructure. It has to support coordination, communication, memory, selection of models, security, and monitoring of agents.

1. Agent Orchestration Layer

The orchestration layer is the multi-agent system’s control centre. It decides who should perform a task, when to perform it, what input/context to provide to it, and where to direct its output.

Effective agent orchestration should support:

• Task routing
• Workflow management
• Agent handoffs
• State tracking
• Failure recovery
• Human escalation
• Tool management
• Workflow tracing

2. Agent Communication Protocols: MCP, A2A, and ACP

Agents also need a standard way of communicating with tools, data sources and other agents.

• MCP (Model Context Protocol): It provides a standard interface for AI applications to access external tools and sources of data. In practice, MCP protocol agents can now connect to any approved system without every integration needing to be constructed as an entirely distinct custom connection.
• A2A (Agent2Agent): It is about communication between AI agents. The A2A protocol allows agents of different capabilities, frameworks or underlying implementations to cooperate and communicate.
• ACP (Agent Communication Protocol): It is another approach to agent-to-agent communication and interoperability. Just as enterprises should not by default deploy all ACP, MCP and A2A, they should reflect on which one of those protocols fits best to their ecosystem and interoperability needs.

3. State Management and Memory

Multi-agent workflows produce significant amounts of temporary and persistent data. If the state is not properly managed, agents can lose context, repeat work, or act on stale data. The main elements can include:

• Workflow state
• Working memory
• Long-term memory
• Knowledge retrieval
• Session management
• State synchronization

Multi-Agent System Architecture Patterns

The right architecture also determines how agents will divide work, how they will exchange information, or how they will react when things go wrong. The most frequent patterns of multi-agent architectures are sequential, parallel, hierarchical and dynamic.

1. Sequential (Pipeline) Architecture

A serial architecture processes work through agents in a predetermined sequence. Each agent performs its task and sends the output to the next agent.

Best for:

• Step-by-step business workflows
• Document processing
• Research and analysis
• Data transformation
• Processes with clearly defined dependencies

Limitation: If there is a failure or delay in one stage, the whole process becomes stalled.

2. Parallel Architecture

In a parallel architecture the different agents perform separate tasks simultaneously. Instead of waiting for one agent to finish a job for the other to start, this system assigns different jobs to various agents and blends the results later.

Best for:

• Independent research work
• Heavy data workloads
• Multi-source analyses
• Workflows in which response time has a cost; independent tasks

Limitation: Synchronization problems come in as operations run parallel. The system must also know how to appropriately fuse the outputs, handle agents that finish at different times, and know how to deal with conflicting outputs.

3. Hierarchical (Supervisor-Worker) Architecture

There is a hierarchy of supervisor or coordinator agent over specialist worker agents. The supervisor breaks down the goal, assigning broken pieces of it as tasks, evaluates the outcomes, and decides what should happen next.

Best for:

• Complex enterprise workflows
• Multiple specialized agents
• Decision-heavy processes
• Workflows requiring centralized control
• Tasks that may require different execution paths

Limitation: Overreliance on one coordinating agent could mean that the supervisor becomes a bottleneck or single point of failure.

4. Dynamic (Adaptive) Architecture

The architecture is dynamic, so that this system can assign the workflow at runtime. Agents are not necessarily pursued in a pre-established order or within a strictly defined hierarchy; rather, they can be chosen depending on the endeavor, the information accessible, prior outcomes, or shifting conditions in business.

Best for:

• Unpredictable or open-ended workflows
• Complex decision-making
• Adaptive research and analysis
• Agentic systems that need runtime planning
• Business processes where the required steps vary by case

Limitation: Dynamic architectures are harder to predict, test, monitor, and administer.

Structures of Multi-Agent Systems

The multi-agent system structure determines the way agents interact, share tasks, and coordinate to achieve a common goal. If architecture patterns account for agents’ execution of a workflow, system structures account for the organization of agents and their interaction in such an environment.

1. Hierarchical Structure

The agents are organized in a hierarchical fashion. The higher-level agent usually coordinates the lower-level agents, allocates tasks, monitors progress, and integrates their outputs.

Key characteristics:

• Clear chain of responsibility
• Centralized task delegation
• Specialized agents at different levels
• Easier control over complex workflows
• Suitable for supervisor-worker systems

2. Holonic Structure

The agents are organized in groups called holons. A holon is an autonomous functional unit that can be integrated into a larger MAS (multi-agent system). An SCM (supply chain management) system for example could be formed by distinct holons at procurement, inventory and logistics.

Key characteristics:

• Agents can act independently
• A group can have its own internal coordination
• Holons may work with other holons
• Enables modular systems design
• Well suited for distributed environments

3. Coalition Structure

A coalition structure refers to the temporary grouping of the agents to work towards an objective. A coalition is not a stable hierarchy, but one that can form, shift and dissolve according to a particular task.

Key characteristics:

• Task-specific collaboration
• Flexible agent membership
• Temporary relationships
• Useful for changing workloads
• Supports dynamic resource allocation

4. Teams

Agents working within such a framework are working with a shared goal and each agent contributes a certain capability.

Key characteristics:

• Shared objective
• Agent roles defined
• Joint decision-making
• Continuous exchange of information
• Ideal for sophisticated joint processes

Advantages of Multi-Agent Systems in AI

The benefits of multi-agent systems can be seen in workflows that require multiple sequences of tasks, tools, data, and decisions.

Better Task Specialization

Multi-agent AI allows each agent to perform a specific task and have a specific responsibility for things like research, data analysis, compliance checking, coding, customer support and such.

Improved Scalability

Having a multi-agent system means being able to distribute workloads among different agents rather than having the whole workflow on one agent. Agents can be plugged into organizations as processes get more complex without re-engineering the entire AI workflow.

Parallel Task Execution

Agents can work on autonomous tasks simultaneously. One can, for instance, interpret market trends while another agent reads through customer data. An orchestration layer can then combine their results, minimizing delay in the workflow given that the tasks are independent from one another.

Challenges of Multi-Agent Systems in AI

Multi-agent systems offer many benefits for complex AI workflows, but companies must solve several issues before they can roll them out securely, cost-effectively, and reliably.

• Higher Development and Infrastructure Costs: More agents can drive up LLM calls, API usage, compute requirements, and infrastructure costs.
• Conflicting Agent Action: Agents require defined roles, communication protocols, handoff procedures and logic for who decides and on what, to not work at cross purposes or duplicate work.
• Error Propagation: If one agent makes an error, all downstream agents that depend on its output will also be erroneous.
• Increased Latency: Multiple calls to agents and hand-offs introduce delays and processing time, particularly with complicated processes.
• Context and Memory Management: Giving agents too much context will lead to an expensive token use and less pertinent answers.

Top Frameworks for Building Multi-Agent Systems: CrewAI vs LangGraph vs AutoGen

The right one for you will depend on what degree of workflow control, agent collaboration and orchestration you need on your application.

CrewAI: Role-Based Agent Teams

• Strength: Role-based design is simple, allowing specialized agents to work toward a common goal.
• Architecture: Tasks, agents, flows, and crews organize workflows by sequence or by hierarchy.
• Limitation: Not very flexible if you have workflows that need customized transitions between states, and complex routing.

LangGraph: Graph-Based Workflow Control

• Strength: Provides extremely fine control over agent workflows, state, branching and execution.
• Architecture: Defines workflows as graphs of tasks or agents, connected by edges which affect the execution.
• Limitation: Requires more engineering and knowledge of the states that are being represented in a graph.

AutoGen / AG2: Conversational Agent Collaboration

• Strength: Allows agents with different capabilities to work together in structured dialogues.
• Architecture: Supports different team patterns like round-robin or based on a selector.
• Limitation: Use of agent dialogues tends to raise coordination costs and complexities, costs of execution and difficulties in termination.

Google ADK, OpenAI Agents SDK, and Claude SDK

• Google ADK: Offers orchestration of agents and tools within Google’s AI ecosystem, but be aware of ecosystem dependencies before using it.
• OpenAI Agents SDK: Supports agents, tools, handoffs, guardrails, sessions, MCP and tracing, can be used for controlled multi-agent workflows.
• Claude SDK: This is a solid starting point for any application built around Claude, but the level of multi-model flexibility sought by organizations should raise concerns about provider dependency.

Multi-Agent System – Real-World Examples by Industry

Multi-agent systems are able to deal with complex business processes by assigning different tasks to different specialized agents and coordinating the multi-agent output through a common workflow. Here are industry-specific examples of this approach in action.

1. Financial Services: Fraud Detection and Compliance

Banks can then use these agents to analyze transactions, alerting them to potentially problematic or suspicious patterns, regulatory compliance, and generating alerts.

Multi-agent pipeline: Transaction Monitor → Pattern Analyzer → Compliance Checker → Alert Agent

Potential result: An improved workflow can manage up to 40% fewer false positives while allowing near real-time compliance reporting.

2. Healthcare: Credentialing and Workforce Management

Credentialing and workforce development in healthcare organizations can also be automated by parceling out document review, credential matching, scheduling, and communication to separate agents.

Multi-agent pipeline: Document Verifier → Credential Matcher → Scheduling Optimizer → Notification Agent

Expected outcome: Automation can reduce manual credential verification process to 80% depending on the complexity of the documents and place of integration.

3. Supply Chain and Logistics: Global Operations

Credentialing and workforce development in health organizations can also be parceled out by having separate agents perform document review, credential matching, scheduling and communications.

Multi-agent pipeline: Document Verifier → Credential Matcher → Scheduling Optimizer → Notification Agent

Expected outcome: Automation can reduce this manual process of credential verification by 80% depending on the complexity of the documents and place of integration.

4. Retail and E-Commerce: Personalized Customer Experience

Retailers can string several of these agents together to understand customer intent, recommend products to customers, inquire about inventory, respond to customers using real-time product and availability information, etc.

Multi-agent pipeline: Customer Intent Classifier → Product Recommender → Inventory Checker → Responder

Potential result: A responsive workflow can deliver customer queries to agents as much as 3x faster when integrated with other systems of product data and commerce.

5. Software Development: AI-Augmented Engineering

Teams of developers can offload the coding, reviewing, testing, and deploying to specialized agents.

Multi-agent pipeline: Code Generator → Code Reviewer → Test Writer → Deployment Agent

Potential result: The NineHertz states that its AI-native development approach can deliver 4x to 7x faster velocity where AI use is permitted.

How Much Does It Cost to Build a Multi-Agent AI System?

The cost of creating a multi-agent AI system ranges from $30,000 to over $200,000, depending on a variety of factors such as the number of agents, the complexity of the workflows, specific capabilities and types of integrations needed, chosen AI models, the security levels required, and the size of the deployment.

The Future of Multi-Agent Systems – What’s Coming Next

The future of multi-agent systems is now heading towards interoperable specialized AI agents that can collaborate across applications, business functions and enterprise systems instead of being functionally isolated assistants.

Greater Adoption of Agent Interoperability Standards

Protocols like MCP and A2A will become more relevant as companies connect agents that were developed with different platforms and technologies. While A2A is meant for agents to learn capabilities and converse with one another in multi-framework environments, MCP is oriented around linking AI applications to tools and data.

Dynamic Agent Teams

There will not always be a set pool of agents in future systems. Instead, an orchestration layer can choose and coordinate the agents based on the task, expertise needed, data available and state of the workflow.

Stronger Observability and Governance

As organizations deploy greater numbers of agents, monitoring and governance will become a necessary evil. Gartner estimates that by 2028 an average Global Fortune 500 organization could have in excess of 150,000 agents in use and will need centralized visibility, lifecycle management, access controls and policy enforcement.

Partner with The NineHertz for Multi-Agent System Development

As multi-agent AI transitions from experiment to enterprise deployment, just connecting up multiple agents is not enough for success. Organizations require a defined workflow strategy, the appropriate architecture, robust orchestration, secure access to enterprise data, and high observability to help agents collaborate successfully.

The NineHertz uses its AI-native engineering culture and ContinuumAI framework to help companies bring these kinds of needs into production-ready multi-agent AI systems. The NineHertz can assist with every step of an agentic AI project, from defining the roles and workflows of agents to building, integrating, governing and ongoing fine-tuning.

Are you ready to convert your AI workflow into a scalable multi-agent system? Contact The NineHertz to plan, build, and deploy your next agentic AI solution.

Frequently Asked Questions About Multi-Agent Systems

What is a multi-agent system in AI?

A multi-agent system uses a number of specialized AI agents that collaborate, communicate and coordinate to perform more complex tasks.

What distinguishes a multi-agent system from a single AI agent?

In a single-agent system, the workflow is handled by one agent, while in a multi-agent system the workflow is split across specialized agents that coordinate among themselves.

What are the architectures for multi-agent systems?

They support different needs in workflow and coordination, such as sequential, parallel, hierarchical and dynamic communication patterns.

Which framework is best for building multi-agent systems?

CrewAI, LangGraph and AutoGen are just a few examples of the many possible technologies to use; the best option depends on how complex and controlled you want your workflow to be.

How much does it cost to build a multi-agent system?

A multi-agent system cost range is $30,000 to $200,000 or more, depending on complexity, integrations, models, security and deployment needs.

Where are multi-agent AI systems used?

Such multi-agent AI systems are actively deployed in numerous industries such as financial services, healthcare, retail, logistics, manufacturing, software development, insurance, etc.

Can LLMs of different kinds be used in a multi-agent system?

Yes. Multi-agent systems can route different tasks to different LLMs based on capabilities, performance, latency and cost.