Multi-agent systems (MAS)

Multi-agent systems (MAS) are distributed computing systems in which several autonomous agents interact to achieve shared or individual goals. Unlike monolithic AI solutions, where a single model tries to handle every task, the multi-agent approach solves complex, multi-level tasks effectively through the specialization and coordination of autonomous agents.

What is a multi-agent system (MAS)?

A MAS has the following properties:

• autonomy: each agent makes decisions independently within its role;
• distribution: agents work in parallel, each responsible for its own area;
• social ability: agents exchange data and coordinate actions;
• specialization: each agent is tuned to a specific class of tasks;
• coordination: agents align their actions to achieve shared or individual goals (through centralized orchestration or distributed mechanisms).

When designed properly, a MAS can reduce the likelihood of language model hallucinations through cross-checking techniques: for example, one agent generates an answer, a second verifies it against knowledge sources via RAG, and a third assesses the consistency of the result. This multi-agent verification approach makes the system more reliable than a single model.

Components and architecture of multi-agent systems

A MAS architecture includes several interconnected components that keep the system running effectively. Understanding these elements is critical for designing reliable enterprise solutions.

Enterprise MAS implementations, such as SimpleOne, use the following types of agents:

• Autonomous Workers: virtual employees with roles and permissions that independently perform multi-step tasks through AI actions in processes;
• Service & Support Assistant: first-line agents that work alongside human operators, implementing the human-in-the-loop concept;
• Skill Builder: a library of reusable components that different agents use so the system can scale.

According to Forrester analysis, each agent represents a discrete cognitive capability that is modular, reusable and extensible enough to fit several related use cases. AI agents are autonomous software modules that can perform tasks without constant human involvement, analyze the context of a request, make decisions, and take actions in enterprise systems.

Coordination and orchestration

In enterprise platforms, coordination between agents is often implemented through a centralized orchestrator pattern, in which a controlling component distributes tasks and monitors their execution. Alternative approaches include distributed coordination (contract net protocol, auction-based mechanisms) and hybrid models. Platforms implement this through visual builders (No-Code AI Builder) that let business analysts create interaction scenarios without programming. A library of ready-made AI actions (classification, content search, analysis, text and speech recognition) serves as the building blocks for coordination.

Shared knowledge base

A critical component of a MAS is a shared knowledge base, a single data store available to all agents. In enterprise systems, this is implemented through a vector RAG (Retrieval-Augmented Generation) engine that provides access to corporate memory: the knowledge base, documents and historical data. A data integration layer connects agents to external systems (CRM, ERP, HR) and gives them up-to-date data without duplication.

Enterprise MAS require built-in security mechanisms:

• RBAC/ABAC: granular access control at the level of roles and attributes;
• action auditing: full logging of all agent operations for compliance and incident investigation;
• row-level security: each agent sees only the data it has permission to access.

Where are multi-agent systems already at work? MAS use cases

Multi-agent systems are used across many industries to automate complex, multi-level processes. Let's look at the most common scenarios, with real cases.

1. Financial services

In the financial industry, MAS are used for high-frequency trading, fraud detection, and risk analysis. Agents interact in real time with enterprise data and business platforms, enabling fast data processing and decision-making. These systems can analyze very large volumes of transactions in real time, detecting anomalies and suspicious behavior patterns. High-frequency trading (HFT) systems process huge numbers of orders, using specialized agents to make decisions at the microsecond level.

2. Healthcare and pharmaceuticals

In healthcare organizations, MAS are used to coordinate diagnostic processes, route patients, and analyze medical data. Different agents specialize in processing test results, scheduling appointments and working with electronic health records.

Genentech developed the gRED Research Agent, a multi-agent system that automates information retrieval and speeds up drug development. The system brings together agents that analyze scientific publications, clinical data and research results, reducing drug development time.

3. IT operations and service desk

In the enterprise sector, MAS automate the handling of incidents and service requests effectively. For example, a typical multi-agent service desk architecture might use the following specialized agents:

• Classifier Agent receives a request and classifies its type (incident, service request or change);
• Knowledge Agent searches the knowledge base for a solution using Corporate Brain & RAG;
• CMDB Agent checks the configuration of the affected systems through the data integration layer;
• Resolution Agent resolves the issue automatically or hands the task over to a human;
• Communication Agent notifies the user and updates the ticket status.

4. Cybersecurity

In one industry project, a multi-agent system was developed to automate the processing of SAST results. The system classifies findings, prioritizes fixes, generates patches, and integrates with Git repositories and task trackers. A typical report can contain thousands of warnings, and the MAS takes this routine load off the team, leaving only a limited number of critical cases for manual review.

5. E-commerce and recommendation systems

eBay developed the Mercury platform, an internal multi-agent system for creating personalized recommendations on its marketplace. The platform lets teams build and scale autonomous, goal-driven AI workflows efficiently. The system includes internal models that detect and block prompt injection attempts by attackers.

Uber implemented Enhanced Agentic RAG (EAg-RAG) to improve the quality of answers from its on-call assistant Genie. To achieve human-level answer accuracy, Uber added AI agents at several stages of request processing, which significantly increased the reliability of the support system.

6. Logistics and supply chain management

Amazon, FedEx and Maersk use MAS to manage global supply chains. Multiple agents work in parallel: some plan delivery routes, others manage warehouse inventory, and others optimize vehicle loading. This coordination cuts fuel costs, speeds up delivery and keeps thousands of moving parts of the system running smoothly.

7. Manufacturing and industry

The automotive industry is actively adopting multi-agent systems. 

Ford uses AI agents for predictive equipment maintenance: the system warns maintenance teams before failures occur. 

General Motors uses multi-agent robotics that adapts to production schedule changes without downtime. 

BMW is developing driver assistance systems for its 2025 vehicles using cloud AI tools and the coordination of multiple agents.

Pros and cons of the multi-agent approach

Multi-agent systems offer significant advantages for solving complex tasks, but they also have limitations that are important to consider during design.

Advantages of MAS

Multi-agent systems offer several significant advantages over monolithic solutions:

• scalability: the system is easy to expand by adding new agents without hurting overall performance;
• parallel processing: agents work on different tasks at the same time, speeding up the solution of complex problems;
• fault tolerance: if one agent runs into a problem, others compensate for it, preventing failure of the whole system;
• reliability through verification: with cross-checking techniques (multi-agent verification), agents can validate each other's results, reducing the risk of errors and language model hallucinations;
• flexibility and adaptability: the system is easy to adjust to changing conditions by modifying individual agents.

According to a McKinsey report, 62% of organizations are already using or experimenting with AI agents, and almost a quarter are scaling them in at least one business function.

Disadvantages and limitations

For all their advantages, MAS come with certain challenges:

• coordination complexity: when there are many agents, managing their interaction becomes a nontrivial task, especially when their goals conflict;
• unpredictable behavior: in complex systems, it is hard to predict in advance how all agents will behave in unusual situations;
• infrastructure requirements: MAS require substantial computing resources and a reliable communication system between agents;
• cost control: without dedicated monitoring mechanisms, it is hard to track the consumption of computing resources and language model tokens.

Enterprise platforms address some of these problems with built-in monitoring, logging and budget management.

Designing a multi-agent system

Building an effective MAS requires a systematic approach and attention to many factors. Getting the design right at the start helps avoid scaling and maintenance problems later.

Defining roles and responsibilities

The first step is to break the target task down into separate functional blocks. Each agent should have a clearly defined role and specialization. For example, a customer request processing system might have agents for classification, knowledge base search, data validation, action execution, and communication with the user.

Designing the communication system

You need to define protocols for interaction between agents. Today's systems use event-driven architecture and graph-based orchestration, in which agents exchange messages through a shared data bus. It is important to provide error handling and fallback scenarios that hand tasks over to a human when confidence is low.

There are several main approaches to coordination in a MAS:

• centralized orchestration: a single coordinator manages all agents (the orchestrator pattern);
• distributed coordination: agents negotiate the distribution of tasks among themselves through interaction protocols (for example, the Contract Net Protocol);
• hierarchical structure: agents are organized into a multi-level system with supervisors;
• market-based mechanisms: agents compete for resources through auctions;
• blackboard systems: a shared board for asynchronous coordination.

For enterprise tasks, centralized orchestration or hybrid models are used more often, as they balance transparency, control, and flexibility.

Integration with enterprise systems

A MAS has to work with the existing IT infrastructure. MAS are increasingly integrated with existing IT infrastructure, including ERP and CRM platforms. As the technology develops, this integration will deepen, allowing agents to analyze data automatically and take targeted actions with minimal human intervention. It is important to provide secure data access through APIs and to comply with information security policies.

Monitoring and control

It is critical to make the system's work transparent. Every agent step should be logged so that the decision-making chain can be traced. You need to monitor the consumption of computing resources and language model tokens, as well as SLA compliance. Platforms use dedicated tools for this (Prometheus, Grafana) with real-time alerts.

The future and trends of multi-agent systems

Multi-agent systems are at the center of enterprise AI development and show several key trends. Understanding these directions helps companies prepare for the next generation of automation.

Multi-model architectures

MAS are moving toward heterogeneous architectures in which different agents use different language models. Model orchestration platforms (LLM gateways) route requests between GPT, Claude and custom fine-tuned models depending on the task. Each agent picks the best model for its task: a lightweight one for classification, a powerful one for generating complex reports. This provides vendor independence and protection against lock-in to a single provider.

Integration with enterprise processes

MAS are increasingly integrated with ESM/ITSM processes, resource management systems and CRM. In the future, multi-agent systems will be able to analyze data and take targeted actions automatically, without human intervention. Built-in integration with a unified ESM platform that includes CMDB, SLA and ITSM processes is becoming the key difference between enterprise and academic MAS.

No-code tools for building agents

Visual builders are evolving that let business analysts create and configure agents without programming. A no-code AI builder with a library of ready-made AI actions makes MAS technology accessible to a wide range of specialists, speeding up adoption and adaptation to specific business tasks.

Stronger control and security

Enterprise MAS are evolving into enterprise-grade solutions with full auditing, granular access control and isolation of sensitive data. Mechanisms for monitoring model usage, managing budgets and complying with regulatory requirements are developing. The ability to deploy on-premise or in sovereign clouds is becoming a critical requirement for large enterprises.

New areas of application

As the technology develops, new MAS use cases are emerging. Beyond classic areas of application (distributed computing, modeling economic systems, robot coordination), multi-agent systems are moving into fields that require complex real-time coordination: smart cities, smart grids and autonomous transportation (vehicle-to-vehicle communication). MAS can distribute tasks and jointly solve problems that require the coordinated actions of many participants.

Summary

Multi-agent systems are the next step in the development of enterprise artificial intelligence, making it possible to solve complex, multi-level tasks by coordinating specialized agents. Unlike academic concepts, enterprise MAS implementations offer ready-made tools for building agents, a built-in orchestration system, a shared knowledge base, and enterprise-grade security.

The key advantage of the approach is the ability to scale and adapt to specific business processes without rewriting the entire system. As the technology develops, MAS are becoming more accessible thanks to no-code tools and integration with existing enterprise platforms. This opens up broad opportunities for automating IT operations, customer request handling, cybersecurity, and other areas that require intelligent coordination of many tasks.