Multi-agent systems (MAS) leverage multiple autonomous agents that collaborate to solve complex problems more effectively than single-agent systems. These agents can operate in various configurations, such as cooperative, adversarial, or mixed, allowing for dynamic interactions based on their goals. By utilizing MAS, businesses enhance operational efficiency and innovation across diverse sectors, particularly in areas like finance and customer service.

1. 1/19
Multi-agent system: Types, working, applications and
benefits
leewayhertz.com/multi-agent-system
Artificial intelligence has progressed significantly, beginning with Large Language Models
(LLMs) that transformed text generation and comprehension. LLM chatbots built on this
technology to automate interactions and customer service, while Retrieval-Augmented
Generation (RAG) enhanced the accuracy of responses by integrating external
information sources. Multi-agent systems (MAS) take AI to the next level by deploying
multiple intelligent agents that collaborate, adapt, and solve complex problems in real-
time. This evolution from single-agent solutions to MAS offers businesses a powerful tool
for optimizing operations and driving innovation across various industries.
Building on this technological evolution, multi-agent systems (MAS) represent a
significant advancement in how businesses address complex challenges. Unlike
traditional single-agent solutions, MAS utilizes a network of specialized AI agents
collaborating seamlessly to tackle intricate problems. Each agent brings unique skills and
knowledge, allowing them to interact, negotiate, and adapt in real time. This collaborative
approach enhances the system’s ability to process diverse data inputs, generate nuanced
insights, and execute tasks more precisely. By harnessing the combined expertise of
multiple agents, businesses can achieve more effective problem-solving and decision-
making, driving efficiency and innovation across various operations.
MAS transforms industries by offering a dynamic and scalable solution to manage
complex, multifaceted issues. For instance, in finance, MAS can integrate various agents
to handle tasks such as risk assessment, fraud detection, and portfolio management,
each contributing specialized expertise. By harnessing the power of multi-agent systems,

2. 2/19
businesses gain a competitive edge, enabling them to respond swiftly to market changes,
improve operational efficiency, and drive innovation in an increasingly complex and
competitive world.
This article will explore multi-agents, their workings, types, components and best
practices in more detail.
What is multi-agent system?
Multi-agent systems (MAS) in AI involve a framework where multiple agents interact or
collaborate to perform tasks, solve problems, or achieve goals. Each agent operates with
a degree of autonomy or semi-autonomy and can be a software program, a robotic entity,
or any other intelligent entity capable of independent action and decision-making. These
agents work together to achieve complex tasks or solve problems that might be difficult
for a single agent to handle alone. Here’s a breakdown of the key components and
concepts of multi-agent systems:
1. Agents: These are the individual entities within the system. Each agent operates
autonomously and has objectives, knowledge, and decision-making processes.
Agents can be software programs, robots, or other entities acting and interacting in
the system.
2. Interactions: Agents in a multi-agent system communicate and cooperate to
achieve their goals. Depending on the system’s nature, they can exchange
information, negotiate, collaborate, and sometimes compete.
3. Coordination: To work effectively, agents must coordinate their actions. This
coordination can be explicit (through direct communication) or implicit (through
shared environments or indirect communication).
4. Collaboration and competition: Agents can work together to achieve common
goals or act in competition, where their goals might conflict. Multi-agent systems
can model both types of interactions.
5. Distributed problem solving: Multi-agent systems often address problems too
complex for a single agent or central controller. The system leverages the
distributed nature of agents to manage and solve these problems collectively.
A multi-agent system leverages the collective behavior of multiple agents to achieve
objectives and solve problems that would be challenging for a single agent to tackle
alone.
Characteristics of multi-agent system
A multi-agent system (MAS) comprises several autonomous agents that work together to
achieve specific goals. Here are some common features of the multi-agent system:

3. 3/19
1. Autonomy: Individual agents in a multi-agent system operate independently,
making their own decisions and taking actions without relying on a central controller.
This independence allows agents to manage their tasks and responsibilities within
the system.
2. Complexity: The interactions within a multi-agent system are complex due to the
advanced processes involved, such as decision-making, learning, and reasoning.
These processes enable agents to function effectively in dynamic environments and
add complexity to the system.
3. Adaptability: Agents within a multi-agent system can adapt their behaviors based
on environmental changes. This means they can adjust their actions in response to
new information or shifting conditions, ensuring the system remains functional even
in unpredictable situations.
4. Concurrency: A multi-agent system supports parallel processing, where multiple
agents work on different tasks simultaneously. This ability to handle concurrent
tasks improves the system’s efficiency and responsiveness.
5. Communication: Agents in a multi-agent system communicate with each other
(inter-agent communication) and within themselves (intra-agent communication).
This communication is essential for coordination, collaboration, and information
sharing among agents to achieve common objectives.
6. Distribution: Multi-agent systems are often distributed across multiple locations or
platforms, meaning that agents operate on different hosts over a network. This
distribution allows the system to use resources from various places, enhancing its
overall capability.
7. Mobility: Agents in a multi-agent system can migrate between platforms or
environments as needed. This mobility is crucial for performing tasks in various
contexts or locations within the system.
8. Security and privacy: Given that agents manage their data and activities, a multi-
agent system must safeguard against potential security risks, including
unauthorized access or data breaches.
9. Openness: Multi-agent systems are designed to be open, meaning they can
dynamically incorporate new agents or remove existing ones based on the system’s
needs. This openness allows the system to evolve and adapt, making it flexible and
scalable.
A multi-agent system is characterized by its agents’ autonomous operation, complex
interactions, adaptability to changing conditions, and ability to function in a distributed,
secure, and open environment.
Unlock Advanced Automation with Multi-Agents System
Leverage multi-agent systems for advanced automation and efficiency. Connect
with our experts today.

4. 4/19
Types of multi-agent system
Let’s dive deeper into each type of agent and their interactions in a multi-agent system:
Cooperative agents
Characteristics:
Common goals: Cooperative agents share common goals and collaborate to
achieve them, with their success relying on the combined efforts of all participating
agents.
Collaboration: They often need to share information and resources. Collaboration
can involve tasks like joint planning, sharing data, or coordinating actions.
Coordination: Effective communication and synchronization are crucial. Agents
might use predefined protocols or strategies to align their actions.
Mechanisms:
Teamwork: Agents may form teams where each member has a specific role or task
that complements the others. For example, in a search-and-rescue operation, one
robot might locate victims while another provides medical supplies.
Resource sharing: Agents might share resources like data, energy, or
computational power to achieve a common goal. For instance, multiple AI agents in
a network security system share threat intelligence and computational resources to
enhance real-time response capabilities against cyber-attacks, ensuring a robust
and unified defense mechanism.
Example of cooperative agents:
Multiple agents cooperate to handle complex user queries in a customer service chatbot
system using large language models (LLMs). One agent specializes in understanding
user requests, another retrieves relevant information from databases, and a third
synthesizes responses to ensure they are contextually appropriate and informative. They
share insights and context through a central system to deliver coherent and accurate
support, streamlining customer interactions across various platforms.
Adversarial agents
Characteristics:
Conflicting goals: Adversarial agents have opposing objectives. Their actions
often aim to hinder or outmaneuver other agents to achieve their goals.
Competition: These agents might compete for limited resources, such as market
share or strategic positions. Their interactions are influenced by the need to
anticipate and counteract the strategies of others.
Mechanisms:

5. 5/19
Strategic planning: Agents engage in strategic decision-making, predicting and
responding to their adversaries’ actions. This might involve tactics such as
negotiation, bluffing, or resource allocation.
Game theory: The interactions of adversarial agents are often modeled using game
theory, where agents make decisions based on the expected actions of others.
Examples include competitive markets or strategic games. For example, in two-
player games like chess or poker, each player (agent) has opposing goals, and their
actions aim to defeat the opponent.
Example of adversarial agents:
In a debate simulation system, two LLM-based agents argue opposing viewpoints on a
given topic. Each agent analyzes the other’s arguments, identifies weaknesses, and
formulates counterarguments to strengthen its position while undermining the opponent’s
stance.
Mixed-agent systems
Characteristics:
Dual nature: Mixed-agent systems combine elements of both cooperation and
competition. Agents work together in some aspects while competing in others.
Complex interactions: The interactions can be dynamic and multifaceted,
reflecting real-world scenarios where collaboration and competition coexist.
Mechanisms:
Negotiation and coalition formation: Agents might form temporary alliances or
coalitions to achieve specific objectives while competing with others outside the
coalition. For example, in a supply chain, companies (agents) might collaborate to
optimize production and distribution processes while competing for market share or
negotiating terms with suppliers and customers.
Conflict resolution: Mixed-agent systems often require mechanisms to manage
and resolve conflicts between cooperative and competitive goals.
Example of mixed agent system:
In a collaborative writing platform, multiple LLM-based agents create a coherent story
while competing for the most impactful contributions. Agents collaborate on plot
development and character consistency but vie for the most creative plot twists or
memorable dialogue.
Hierarchical Multi-Agent Systems (MAS)
Characteristics:
Structured organization: Agents are arranged in a hierarchical structure, with
varying levels of authority and responsibility.

6. 6/19
Management and coordination: Higher-level agents oversee and coordinate the
actions of lower-level agents, ensuring that tasks are aligned with broader
objectives.
Mechanisms:
Delegation: Higher-level agents delegate tasks to lower-level agents, which handle
specific actions or sub-tasks.
Coordination: The hierarchy facilitates coordination across different levels,
ensuring that lower-level agents’ activities contribute to the overall goals.
Example of a hierarchical multi-agent system:
A high-level LLM agent oversees the entire process in a content creation pipeline,
delegating specific tasks to specialized lower-level agents. These subordinate agents
handle research, writing, and editing tasks, while the overseeing agent ensures
coherence and alignment with the overall content strategy.
Heterogeneous Multi-Agent Systems (MAS)
Characteristics:
Diverse agents: Agents have different skills, roles, or capabilities, making the
system flexible and adaptable.
Specialization: Each agent may perform specific functions or tasks based on its
unique attributes.
Mechanisms:
Integration of skills: Agents with different expertise or functions work together to
achieve complex objectives. The diversity enhances the system’s ability to handle
varied tasks.
Adaptability: The system can adapt to changing conditions or requirements by
leveraging the diverse capabilities of its agents.
Example of a heterogeneous multi-agent system:
In a comprehensive customer support system, diverse LLM-based agents with
specialized knowledge in different product areas work together. One agent handles
technical queries, another manages billing issues, and a third specializes in product
recommendations, collectively providing comprehensive customer assistance.
Difference between single and multi-agent system
Here’s a comparison of single-agent and multi-agent systems in tabular form:
Aspect Single-agent System Multi-agent System

7. 7/19
Definition A system with only one
autonomous agent.
A system with multiple autonomous
agents.
Complexity Generally simpler, with fewer
interactions.
More complex, involving interactions
between multiple agents.
Coordination Not required, as there is only
one agent.
Required to manage interactions and
conflicts among agents.
Communication Not applicable, as there’s
only one agent.
Agents communicate with each other
to share information or coordinate
actions.
Scalability Limited to the capabilities of
a single agent.
More scalable as additional agents
can be added to handle complex
tasks.
Robustness Less robust; failure of the
single agent leads to system
failure.
More robust; the system can continue
functioning even if some agents fail.
Use cases Simple tasks, single-user
applications.
Complex tasks, collaborative work,
distributed systems.
Decision
making
Decisions taken by a single
agent, based on its own
goals.
Distributed among multiple agents,
which may have different goals.
Resource
allocation
Managed by a single agent. Requires coordination among agents
for efficient resource use.
Adaptability Limited to the capabilities of
one agent.
Can adapt to dynamic environments
through the collective behavior of
agents.
Unlock Advanced Automation with Multi-Agents System
Leverage multi-agent systems for advanced automation and efficiency. Connect
with our experts today.
The building blocks of multi-agent systems
Multi-agent systems (MAS) function like a well-coordinated team, where each member
(agent) has a specific role and contributes to achieving a shared goal. Comprehending
the core components of these systems unveils their complexity and efficacy.
Large Language Models (LLMs)

8. 8/19
Large Language Models (LLMs) serve as the intellectual core of modern MAS. These
advanced AI systems excel in understanding and generating human language, which is
crucial for parsing extensive datasets and interpreting complex queries. LLMs equip
agents with the reasoning and decision-making capabilities to handle sophisticated tasks
effectively by enabling agents to comprehend and respond coherently to complex
instructions. Their role is pivotal in translating human language into actionable insights,
allowing agents to engage in meaningful interactions and make informed decisions.
Agents
Agents are autonomous entities designed to perform specific tasks, make decisions, and
work collaboratively towards common objectives. Each agent possesses unique
capabilities and responsibilities, using LLMs as their reasoning mechanisms. This
autonomy allows agents to adapt to changing conditions and interact dynamically within
the system. Their ability to work independently with other agents ensures the system can
efficiently handle various tasks and challenges.
Tools
Tools are the specialized functions or skills agents use to accomplish their tasks. These
can range from simple actions like retrieving data from an API to more complex
operations such as performing detailed analyses. Tools are integral to agents’ operational
capacity, providing them with the means to execute their functions effectively. The
selection and integration of appropriate tools are crucial for optimizing the system’s
performance and achieving desired outcomes.
Processes or flows
Processes or flows outline how tasks are organized and executed within a MAS. They
define the sequence and coordination of activities, ensuring tasks are distributed
efficiently and aligned with overall objectives. Processes can be categorized as inter-
agent (how agents interact with each other) or intra-agent (how an agent interacts with its
tools and handles outputs). These workflows are essential for managing the dynamic
interactions and ensuring that all system components work harmoniously towards the
shared goal.
Together, these building blocks—LLMs, agents, tools, and processes—form the
foundation of multi-agent systems, enabling them to tackle complex tasks, adapt to
changes, and deliver efficient and performant solutions.
How do multi-agent systems work?
In a multi-agent architecture, the system can be conceptualized using the container-
component architecture model, where:

9. 9/19
Components: The components are the individual agents themselves. Each agent
operates autonomously, with specific roles and functions, and can interact with other
agents to accomplish collective goals.
Container: The container is represented by the agent environment. This
environment serves as the framework within which the agents operate. It provides
essential services such as discovery, communication, and coordination, enabling
agents to find each other, exchange information, and collaborate effectively.
Let’s break down this concept and understand it in more detail:
Agents as components:
Each agent functions as a self-contained unit with diverse capabilities, decision-
making abilities, and objectives.
Agents can specialize in different tasks and operate independently while interacting
with other agents.
Agent environment as the container:
The agent environment acts as the overarching system that supports the agents. It offers
essential services that facilitate interaction and cooperation among agents. For example,
Discovery: This allows agents to locate and identify other agents within the
environment. It ensures that agents can find each other and establish
communication.
Communication: This encompasses the protocols and mechanisms that enable
agents to exchange messages and data. It supports various forms of interaction,
such as sending commands, sharing information, and coordinating actions.
Coordination: This helps manage interactions among agents to achieve collective
goals, ensuring that their activities are synchronized and work towards a common
objective.
Agent/ Component
Environment / Container
Accurate Decision
Making
Agent
Interation
Agent
Interation
Search
Agent
Extraction
& Analytic Agent
Decision
Making Agent
LeewayHertz
Output
How do agents interact?

10. 10/19
1. Finding each other: Agents first must identify or discover other agents in the
environment. This is essential for communication and collaboration.
2. Communication: Once they find each other, agents communicate using set rules.
This can be as simple as sharing information or as complex as negotiating tasks.
3. Making decisions: Agents make decisions based on what they know about the
environment and information from other agents. These decisions guide their
actions.
By constantly interacting and adapting, agents work towards their objectives, showing the
flexibility of MAS.
How do multi-agents interact with their environment?
Agent
Environment
Action
Text Tools
Embodiment
Calling API
Generalize/Transfer
Summary Recall Learn Retrieve
Storage
Memory Knowledge
Brain
Decision Making
Planning/
Reasoning
Inputs
Perception
Brain
LeewayHertz
Multi-agent systems interact with their environment through continuous cycles of
perception, decision-making, and action. These interactions allow agents to gather
information, make informed decisions, and take actions that influence their surroundings
and other agents within the system.
Environment
In the context of an LLM-based agent, the environment refers to the external context or
surroundings that the agent interacts with. This can include:
1. Physical environment: The actual physical world where the agent operates. For
example, for a robotic assistant, the physical space could include rooms, objects,
and people in those rooms.

11. 11/19
2. Digital environment: The digital context in which the agent operates. This could
include online data sources, digital platforms, and virtual spaces where agents
process information and interact with users.
3. Interaction context: The specific scenario or situation in which the agent operates.
For example, if a user asks about the weather, the interaction context involves
weather information and the user’s current location.
Perception
Agents collect information from their environment. This is done using sensors or data
inputs that provide details about the current state of the environment or other agents. The
goal is to understand the context or changes in the environment that are relevant to the
agent’s tasks or goals.
Receives data: The perception module gathers information from the environment.
This could be text (like a user’s question), auditory signals (like sounds or speech),
or visual data (like images or videos).
Processes and translates: It converts this raw data into a format the brain can
understand. For example, if the environment is a physical space with various
objects, the perception module might use sensors to recognize these objects.
Brain
The brain of the LLM-based agent is like the central processing unit. It handles the core
functions of the agent, such as:
1. Knowledge storage
Stores information: The brain keeps information similar to how our memory
works. This includes facts, past interactions, and learned experiences.
2. Information processing
Processes input: When the agent receives input (like a question or a
command), the brain analyzes and processes this information. It uses the
stored knowledge to understand the context and meaning.
Reasoning and decision-making: The brain uses knowledge to make
decisions about the input. For example, if asked about the weather, the brain
will use weather data and reasoning to decide if it will rain.
3. Reasoning and planning
Plans actions: Based on reasoning, the brain plans the appropriate actions. It
decides what response to give or what action to take.
Handles unseen tasks: The brain can adapt to new or unexpected situations
by applying reasoning and knowledge in novel ways.
Action
Agents perform actions based on their decisions. These actions can alter the environment
or influence other agents, such as moving, sending messages, or modifying settings. The
goal is to ensure changes that help the agent accomplish its objectives and improve the

12. 12/19
system’s functionality.
The agent cycle is a continuous process in which an agent perceives its environment,
makes decisions based on that perception, and then takes action to achieve its goals.
This cycle repeats as the environment changes and new information becomes available.
Rules and algorithms
MAS relies on rules (protocols) and decision-making methods (algorithms) to function
smoothly:
Protocols: These are the rules for how agents interact, communicate, and resolve
conflicts.
Algorithms: These guide how agents make decisions, from basic rules to
advanced learning models that allow agents to improve over time.
These rules and algorithms ensure that agents work together effectively, whether
cooperating or competing.
Multi-agent systems involve multiple agents interacting within a shared environment to
solve problems and achieve goals. We can see how these systems are decentralized and
adaptable using the container-component metaphor. Agents continuously gather
information, make decisions, and act guided by rules and algorithms. This collaborative
approach makes MAS powerful and versatile for many applications.
Unlock Advanced Automation with Multi-Agents System
Leverage multi-agent systems for advanced automation and efficiency. Connect
with our experts today.
Applications of multi-agent system
Multi-agent systems (MAS) have diverse applications across various industries,
leveraging their decentralized and collaborative nature to solve complex problems. Here
are some key applications:
Supply chain management
Multi-agent systems (MAS) transform operations by enabling autonomous agents—
representing suppliers, manufacturers, distributors, and retailers—to collaborate
intelligently. Agents share real-time inventory data, allowing dynamic adjustments that
minimize stockouts and reduce holding costs. They also leverage historical and real-time
data for accurate demand forecasting, leading to more efficient production planning and
resource allocation. In logistics, MAS optimizes routing and scheduling by considering

13. 13/19
real-time conditions, thereby reducing delivery delays and transportation costs. This
approach enhances transparency, agility, and collaboration across the supply chain,
ultimately driving significant cost reductions and improving overall efficiency.
Healthcare
In healthcare, multi-agent systems enhance patient monitoring, resource allocation, and
personalized treatment planning. These agents work together to monitor patient health in
real-time, allocate resources such as medical staff and equipment, and develop
personalized treatment plans based on individual patient data. The system facilitates
seamless communication between patients and healthcare providers, improves decision-
making, and efficiently uses resources. By integrating data and coordinating actions, MAS
supports better patient outcomes and more effective management of healthcare
resources.
Finance
In finance, multi-agent systems (MAS) transform operations by enabling autonomous
agents to manage complex tasks such as fraud detection, risk assessment, and financial
monitoring. Each agent operates with its own rules and objectives, sharing and analyzing
data to identify fraudulent activities and assess financial risks. MAS can continuously
monitor real-time transactions, flagging anomalies and inconsistencies that might indicate
fraudulent behavior. They also support dynamic risk management by evaluating market
conditions and financial metrics. This collaborative approach enhances transparency,
improves decision-making, and helps institutions adapt to emerging threats, leading to
more robust and resilient financial systems.
Game development and simulations
Multi-agent systems are extensively used in game development and simulations to create
complex and interactive environments. Each agent represents a character or entity with
specific behaviors and objectives in games. These agents interact with one another and
the environment, creating a dynamic and immersive gameplay experience. In simulations,
MAS models real-world scenarios where multiple entities interact according to predefined
rules. This approach enables the creation of realistic and engaging simulations for
training, entertainment, and research purposes.
E-commerce
In e-commerce, multi-agent systems (MAS) enhance user experiences through
sophisticated personalization and dynamic interaction management. MAS consists of
multiple interacting agents that analyze user data such as browsing history, preferences,
and purchasing patterns. These agents work together to generate accurate, personalized
product recommendations by sharing insights and collaborating on data analysis.
Additionally, MAS facilitates real-time adjustments to offers and promotions, as agents

14. 14/19
continuously monitor market trends and customer behavior. By coordinating these
activities, MAS ensures that e-commerce platforms provide a highly relevant and
customized shopping experience, which boosts customer satisfaction and drives sales.
Manufacturing
In manufacturing, multi-agent systems (MAS) enhance production planning and
scheduling by enabling real-time responsiveness to changes such as fluctuating demands
and machine breakdowns. MAS employs multiple agents that monitor various aspects of
the production process, including machinery, workstations, and inventory levels. These
agents interact to coordinate and adjust tasks dynamically, optimizing resource use and
minimizing delays. Agents can detect and address bottlenecks by continuously analyzing
production data, redistributing workloads, and adapting schedules. This collaborative
approach ensures a balanced production flow, improves overall efficiency, reduces
downtime, and helps meet production targets more effectively.
IT
Multi-agent systems (MAS) optimize operations in IT management, including network
management, system monitoring, and incident response. MAS consists of multiple agents
that perform specific roles, such as monitoring system performance, detecting anomalies,
and managing network resources. These agents work collaboratively to analyze data
from different sources, identify potential issues, and coordinate responses to maintain
system stability and performance. By continuously sharing information and adapting to
changing conditions, MAS enhances the efficiency of IT operations, improves incident
resolution times, and ensures a more reliable and secure IT infrastructure. This proactive
and collaborative approach helps manage complex IT environments and address issues
before they impact overall performance.
Benefits of using a multi-agent system
Multi-agent systems (MAS) offer numerous advantages in AI applications due to their
decentralized and collaborative nature. Here are the benefits of using a multi-agent
system:
1. Scalability: MAS can efficiently manage large-scale problems by distributing the
workload across multiple agents. Each agent handles a portion of the overall task,
allowing the system to scale up as more agents are added. This is particularly
useful in complex environments where a single agent would struggle to manage the
entire problem space.
2. Robustness: One of MAS’s key strengths is its robustness. If an individual agent
fails or encounters an issue, the remaining agents can continue to operate and even
take over the failed agent’s responsibilities. This ensures the system remains
functional, reducing the risk of complete system failure.

15. 15/19
3. Flexibility: MAS is inherently flexible and capable of adapting to dynamic and
changing environments. Agents can adjust their strategies, reconfigure their
interactions, and adapt to new information or conditions in real time, making MAS
well-suited for unpredictable or evolving scenarios.
4. Efficiency: By dividing tasks among multiple agents, MAS can often reach
solutions faster than a single agent working alone. Each agent can focus on a
specific aspect of the problem, allowing for parallel processing and faster overall
performance. This efficiency is particularly valuable in time-sensitive applications.
5. Parallelism: MAS naturally supports parallel processing, where multiple agents
simultaneously work on different parts of a problem. This parallelism speeds up
problem-solving and allows the system to tackle more complex tasks that would be
too resource-intensive for a single agent.
6. Modularity: MAS is designed modular, meaning that agents can be developed,
tested, and deployed independently. This modularity simplifies the development
process, as new agents can be added to the system without disrupting existing
ones. It also makes it easier to upgrade or replace individual agents as needed.
7. Specialization: In MAS, agents can be designed with specialized skills or
knowledge, allowing them to focus on specific tasks where they excel. This
specialization leads to more effective problem-solving, as each agent can leverage
its strengths in its designated area, contributing to the system’s overall success.
8. Improved decision-making: MAS can enhance decision-making by aggregating
insights from multiple agents, each with its perspective and expertise. This
collective intelligence often leads to more informed and balanced decisions as
different viewpoints and strategies are considered.
9. Enhanced collaboration: MAS enables agents to collaborate effectively, sharing
information, resources, and strategies to achieve common goals. This collaboration
is particularly useful in complex, multi-faceted problems where no single agent has
all the necessary knowledge or capabilities to succeed alone.
10. Resilience to uncertainty: In environments with high levels of uncertainty or
incomplete information, MAS can provide resilience by allowing agents to make
independent decisions based on local information. This decentralized approach
helps the system adapt more effectively to unexpected changes or disruptions than
a centrally controlled system.
11. Real-time response: MAS is well-suited for applications requiring real-time
responses. Since agents can operate autonomously and in parallel, they can quickly
respond to environmental changes, making MAS ideal for applications like
autonomous vehicles, financial trading, and emergency response systems.
Leading multi-agent frameworks
Choosing a multi-agent framework is crucial for determining the system’s scalability,
autonomy, and control. Here’s a detailed comparison of three prominent frameworks:
AutoGen

16. 16/19
AutoGen is designed to support conversational agents, focusing on high-level interaction
through dialogue. It allows agents to engage in conversations, facilitating collaborative
tasks and decision-making. Here’s what the key features entail:
Conversational engagement: AutoGen enables agents to participate in dialogue,
exchanging messages to work together. This feature supports dynamic interactions
and collective problem-solving by allowing agents to communicate and share
insights effectively.
Customization through integration: This framework supports integrating various
components, such as Large Language Models (LLMs) and human inputs. This
flexibility lets developers tailor agent behaviors and interactions, adapting the
system to different use cases and requirements.
CrewAI
CrewAI combines autonomous operation with a structured role-based approach, making it
versatile for development and production. Compared to AutoGen, it provides a more
flexible framework, emphasizing defined roles and autonomous task management.
Role-based agent design: CrewAI allows agents to have specific roles and goals,
with associated toolsets to enhance their functionality. This role-based design
ensures agents can perform their tasks efficiently and align with their designated
responsibilities.
Autonomous inter-agent delegation: Agents in CrewAI can autonomously
delegate tasks and consult each other. This feature streamlines task management
and problem-solving by enabling agents to coordinate and collaborate without
requiring constant external intervention.
LangGraph
LangGraph is a graph-based framework focused on defining and managing complex
agent interactions. It is particularly useful for custom-built systems that require fine-
grained control and scalability.
Stateful multi-actor applications: LangGraph supports the development of
applications where multiple agents interact, maintaining their state throughout these
interactions. This is crucial for applications that require ongoing and consistent
agent interactions.
Cyclical computation support: The framework supports cyclical computations
within LLM applications, allowing for iterative processes and simulations. This
feature is valuable for scenarios that involve repetitive interactions or complex
workflows, enabling more sophisticated agent behaviors.
Each framework offers unique strengths, catering to different needs in developing and
deploying multi-agent systems. AutoGen excels in conversational engagement, CrewAI
provides structured flexibility, and LangGraph allows for detailed control over agent
interactions through graphical representation.

17. 17/19
Best practices for building a multi-agent system
Building effective multi-agent systems (MAS) requires carefully balancing design
simplicity, robust communication, and continuous evaluation. Here’s an explanation of the
best practices:
1. Match architecture to the problem: The right architecture is crucial because MAS
introduces complexities such as coordination and communication between agents
that aren’t present in single-agent systems. A single-agent system may suffice for
simple tasks, but a MAS can offer greater flexibility and resilience for complex,
dynamic environments.
2. Start simple and iterate: Begin with a basic setup involving a minimal number of
agents. This allows for early testing and validation of core functionalities before
scaling up. By starting small, developers can identify and resolve issues early,
making it easier to manage the added complexity as more agents are introduced.
3. Define clear roles and responsibilities: Each agent should have a specific role
within the system, focusing on a particular task or set of tasks. This specialization
ensures that agents can operate efficiently without overlapping responsibilities,
which can lead to conflicts or inefficiencies. Clearly defining these roles allows the
system to function more cohesively.
4. Enable flexible inter-agent communication: Communication is key in MAS. Static
communication structures are easier to manage and debug, but dynamic
communication allows agents to adapt in real time to changing conditions. The
choice between static and dynamic topologies depends on the system’s needs—
static for predictability and dynamic for adaptability in complex scenarios.
5. Balance autonomy and control: Finding the right level of autonomy for agents is
critical. Too much control can stifle the system’s responsiveness, while too much
autonomy can lead to unpredictable behaviors. Adjustable autonomy, where the
level of control can vary depending on the context, is a valuable approach to
maintaining balance.
6. Design for human-agent interaction: Since humans often interact with MAS,
designing intuitive interfaces and interaction methods is essential. Whether
presenting the system as a single unified entity or as a collection of individual
agents, the design should facilitate easy and effective communication between
human users and agents, ensuring that the system operates smoothly and user
goals are met.
7. Continuously evaluate and improve: MAS is modular, allowing for individual
agents’ ongoing assessment and optimization. Developers can regularly analyze
system performance and make iterative improvements by implementing monitoring
tools and evaluation agents. This continuous feedback loop is vital for maintaining
and enhancing system efficiency.

18. 18/19
8. Proactively identify and mitigate risks: MAS presents unique safety and security
challenges, particularly due to its distributed nature. Failures or security breaches in
one agent can have cascading effects across the system. To mitigate these risks,
robust security measures such as encryption, anomaly detection, and secure
execution environments should be implemented.
Endnote
Multi-agent systems (MAS) offer a transformative approach to addressing complex,
dynamic challenges by harnessing the collective intelligence of autonomous agents.
These systems are designed to operate in environments where agents—each with
specific roles and capabilities—interact, collaborate, and make decisions in real time. By
distributing tasks among multiple agents, MAS can efficiently manage and optimize
processes across various domains, such as supply chain management, traffic and
transportation systems, healthcare, finance, and beyond.
One of MAS’s key strengths is its ability to adapt to changing conditions. As agents
continuously gather data from their environment, they can adjust their actions and
strategies accordingly, ensuring the system remains responsive and resilient. This
adaptability is particularly valuable in scenarios that require real-time decision-making,
such as managing traffic flow or coordinating supply chain operations.
Furthermore, MAS’s decentralized nature allows for greater scalability and robustness.
Rather than relying on a single central system, MAS distributes the workload across
multiple agents, reducing the risk of system-wide failures and enabling more efficient
resource use.
As technology advances, the role of MAS is expected to grow, driving innovation in
automation and intelligent systems. Their ability to enhance collaboration, optimize
operations, and adapt to evolving challenges positions MAS as a cornerstone of future
intelligent systems.
Don’t let your business lag in the era of advanced technology. Partner with the experts at
LeewayHertz to leverage the power of multi-agent systems, ensuring your operations are
innovative, efficient, and poised for future success.
Listen to the article
37:20
00:00

19. 19/19
Author’s Bio
Akash Takyar
CEO LeewayHertz
Akash Takyar is the founder and CEO of LeewayHertz. With
a proven track record of conceptualizing and architecting
100+ user-centric and scalable solutions for startups and
enterprises, he brings a deep understanding of both
technical and user experience aspects.
Akash's ability to build enterprise-grade technology solutions
has garnered the trust of over 30 Fortune 500 companies,
including Siemens, 3M, P&G, and Hershey's. Akash is an
early adopter of new technology, a passionate technology enthusiast, and an
investor in AI and IoT startups.
Write to Akash
Related Services
AI Agent Development
Empower your operations with custom AI agents, driving automation for a smarter future.
Our custom AI agents continuously improve and adapt, enhancing business capabilities.
Explore service