Understanding Agents and Multi Agent Systems for Better AI Solutions

Melissa Malec
March 16, 2025

Updated: March 16, 2025

When a single AI agent is tasked with solving every problem, you’re setting yourself up for frustration.

Because, as complexity mounts, that single agent becomes overwhelmed, unable to juggle the diverse, simultaneous demands of a modern business environment.

Critical decisions are delayed, opportunities are missed, and bottlenecks proliferate.

So, rather than forcing one agent to wear every hat, multi-agent systems exist. What are they? How do they work? Why are they so effective?

Keep reading to find out.

What is a Multi-Agent System in AI?

A multi-agent system in AI (or MAS for short) is just as it sounds—separate agents working together to complete tasks.

Single agent systems rely on one solitary program to do all the heavy lifting, but a MAS is all about the dynamic interplay between agents.

Each agent is like a mini decision-maker, equipped with its own goals, abilities, and even a bit of personality.

These agents operate within a shared environment, collaborating when it makes sense and competing when it doesn’t, much like a sports team or a bustling city of ambitious individuals.

Let’s dig into that sports team analogy. What’s the object of a volleyball game? To score points by preventing the ball from hitting the ground on your side of the court and instead forcing it to hit the ground on your opponent’s side.

Now, imagine if all the rules are the same but your team only has one player. That objective becomes far more challenging. Sure, a single player might pull it off, but the game would be vastly different. Now, introduce a team where each member excels in a different role—digging, setting, hitting—and suddenly, every play becomes sharper and more effective. That’s a multi-agent system.

This system’s magic lies in its collective behavior. When agents collaborate, they can tackle complex tasks more effectively than any one agent could alone.

And when they compete, the system often drives innovation and adaptation, sometimes leading to surprising, emergent behaviors that you wouldn’t expect from a single agent.

Key Components of Agent Systems

At its core, a MAS is built on two fundamental elements:

The Agents: Independent entities that make decisions, adapt, and interact with both their environment and each other. Think of them as the team members, each with unique skills and perspectives.
The Environment: The shared space where all the action happens. This could be a simulated world, a network, or any context where these agents live and operate.

Agents

Agents are the digital equivalents of specialists working independently yet contributing to a larger goal. For example:

Software Bots: These agents can handle tasks like customer support, data analysis, or network monitoring, operating around the clock without human intervention.
Sensors: Acting as the system’s sensory organs, sensors detect changes—such as shifts in temperature, motion, or light—and provide critical data that inform decision-making.
Robots: In the physical realm, robots range from industrial arms assembling products on a factory line to autonomous drones surveying remote areas. They bring tangible interaction to the system, adapting to real-world challenges.

Environments

Environments can be broadly categorized as:

Static: Conditions remain relatively constant and predictable. For instance, in a well-organized manufacturing plant, robots operate within fixed layouts and controlled conditions, allowing them to perform their tasks with high efficiency and precision.
Dynamic: Here, change is the only constant. Agents must constantly adapt to fluctuating conditions. Consider the world of autonomous driving, where vehicles must negotiate varying traffic patterns, weather changes, and unexpected obstacles, or the fast-paced operations in logistics, where warehouse robots respond to real-time order updates and shifting inventory levels.

Architectures for Multi-Agent Systems—Your Options Explained

Multi-agent architectures define how agents connect, interact, and coordinate their actions.

They can be as varied as graph nodes, handoffs, or sophisticated network structures, each offering a unique approach to orchestrating distributed intelligence.

Here are a few commonly used architectures:

Hierarchical Architecture:

The design mirrors a well-organized sports team or a corporate ladder. Here, individual groups of agents are managed by their own supervisors, all under the watchful eye of a top-level conductor. With this approach, each team can focus on its specialized tasks, while the overall strategy is guided by a higher authority that keeps everything on track.

Graph-Based Architecture:

Each agent is represented as a node in a graph, with connections defining how they interact. This model can be used to design custom workflows where the order of agent activation is predetermined.

Custom Multi-Agent Workflows:

When you craft a custom multi-agent workflow, the system is visualized as a network of graph nodes. The sequence in which these nodes are activated is pre-defined. It lets you design a precise, choreographed process tailored to the specific needs of your task.

For example, LangGraph (a platform designed to build and manage multi-agent systems using a graph-based approach) offers two modes:

Explicit Control Flow: Normal edges define a fixed sequence for agent activation, ensuring consistency.
Dynamic Control Flow (Command): A supervisor node—often an LLM—dynamically selects the next agent based on real-time conditions.

All of these architectures balance structure with adaptability, directly impacting how effectively a multi-agent system performs complex, coordinated tasks.

Master and Subordinate Agents: A Hierarchical Approach

In many multi-agent systems, a hierarchical structure known as the master-subordinate model is employed to further refine coordination and task delegation. This model leverages a central “master” agent to guide the overall strategy, while subordinate agents focus on executing specific tasks. Here’s how this division of labor enhances system performance:

Master Agents: The Orchestrators

Task Allocation: Beyond simply assigning tasks, the master agent evaluates each subordinate’s strengths to match them with the right job.
Strategic Decision Making: By aggregating and analyzing information from various sources, the master agent formulates overarching strategies and adjusts them dynamically.
Coordination & Synchronization: The master ensures that all agents not only work toward a common goal but also remain in sync, mitigating conflicts and redundant efforts.
Continuous Monitoring: Regular performance feedback and monitoring enable the master agent to reassign resources as needed, ensuring optimal operation in real time.

Subordinate Agents: The Specialized Executors

Task Execution: These agents focus on performing specific operations accurately, whether it’s processing data or executing physical actions.
Progress Reporting: Their role includes keeping the master informed with timely updates, which is crucial for adaptive decision-making.
Specialization: Often optimized for particular tasks, subordinate agents bring focused expertise—ranging from data retrieval to customer interaction.
Adaptive Responsiveness: Designed to adjust to evolving instructions, these agents provide the flexibility needed in dynamic environments.

A Comparative Snapshot

Aspect	Master Agent	Subordinate Agent
Control Level	High – Drives overall strategy and task allocation	Low – Executes specific tasks as directed
Autonomy	Engages in strategic decision-making	Focuses on execution and status reporting
Communication	Aggregates inputs and directs the team	Delivers regular progress updates
Complexity	Handles complex, computationally intensive planning	Operates with simpler, task-specific logic

By exploring these detailed nuances, you can appreciate how a well-designed master–subordinate structure not only streamlines operations but also scales efficiently across various applications—from AI-driven workflow automation to robotic swarms.

When Agents Talk: The Art of Communication and Coordination in Multi-Agent Systems

When independent agents need to work together, they rely on a shared state channel—typically a list of messages—to keep everyone on the same page.

There are two primary approaches to how agents share information:

Full Thought Process Sharing: Agents exchange their entire chain of thought, providing a comprehensive view of their decision-making. This can be useful for debugging or when transparency is key.
Final Result Sharing: Agents communicate only their end results, streamlining the process and reducing overhead.

Historically, protocols such as the Knowledge Query Manipulation Language (KQML) and the Agent Communication Language (ACL) were utilized. These days communication across agents normally takes place through JSON payloads delivered via API calls, event streaming, websockets, and other more modern approaches.

It is crucial to design multi-agent systems with resiliency and fault-tolerance as a key design principle. As a distributed system – a series of decentralized programs working towards the same goal – a multi-agent system is prone to encounter failures when any of its nodes behave unexpectedly. Mitigating this requires the intersection of DevOps, MLOps, and AI Governance responsibilities, to ensure that there is proper conflict resolution, that Service Level Agreements (SLAs) are met, and that behavior is auditable and consistent.

The Benefits of Multiple Agents

When you invest in a multi-agent system, you’re turbocharging your operations. Each agent focuses on a specific function, eliminating bottlenecks and streamlining processes in real time.

Because multi-agent systems break away from the one-size-fits-all model where each agent focuses on a specific function, users are in store for a host of benefits.

We’re going to explain three that stand out to us as an AI company:

Lightning-Fast Workflows: Operational Efficiency that Delivers

When every agent hones in on its own specialty, workflows become razor-sharp. Imagine a software development pipeline where one agent compiles code, another runs automated tests, and a third manages deployments.

This division of labor not only accelerates decision-making and ensures reliable task execution but also boosts cost efficiency. By specializing our models to perform only one task well, we can select smaller, more efficient models tailored for that specific role.

While a monolithic agent might require a heavyweight model like OpenAI’s o4 to handle every task, a microagent in a multiagent system can operate with a leaner, cost-effective alternative—driving projects from ideation to launch without the usual delays.

Future-Proof Operations: Scalability and Flexibility for Evolving Demands

As business needs change, multi-agent systems can evolve right alongside you. Picture a SaaS platform that suddenly experiences a surge in user activity.

With a multi-agent setup, you can effortlessly add or reconfigure agents to manage the increased load. This adaptability helps your operations remain robust and agile, no matter how market conditions shift.

Multi-agent systems allow you to smoothly scale your workflows to accommodate both the increasing complexity of your growing AI programs and the volume of traffic that they face. Just like microservices in software development, the multi-agent architecture decomposes monoliths to enable systems to grow in a reliable, decoupled way.

Collective Intelligence: Enhanced Problem-Solving with Specialized Expertise

Complex challenges call for a spectrum of skills. By combining agents with distinct strengths, these systems can address multifaceted issues more effectively than a single, general-purpose solution.

Consider a scenario in software development: a critical bug surfaces in production. One agent digs into system logs, another simulates user behavior, and a third scans the code for anomalies.

Together, they diagnose and resolve the issue swiftly, turning what could be a major crisis into a manageable hiccup.

Real-World Applications of Multi-Agent Systems

Multi-agent systems prove their worth in a wide range of fields, where solving complex problems demands collaboration, adaptability, and resilience.

Here are some real-world scenarios where MAS shine:

Robotics and Automation

In industrial settings, specialized agents control different aspects of automated assembly lines or warehouse operations, ensuring each unit performs its task with accuracy.

Autonomous drones, operating under the guidance of a multi-agent network, can inspect infrastructure or manage deliveries without continuous human intervention.

Amazon Robotics is a prime example. In Amazon’s fulfillment centers, thousands of robots work in tandem to move products, restock inventory, and prepare orders for shipment. Each robot is an independent agent that communicates with others to prevent collisions and optimize routes, ensuring efficiency in a high-demand environment.

Gaming and Simulation

In gaming and simulation, multi-agent systems can help transport you into the world more convincingly by giving virtual characters independent decision-making abilities.

Instead of scripted behavior, non-playable characters (NPCs) operate based on their own sets of rules, creating unpredictable and engaging gameplay.

For example, in strategy games, each agent might manage different units or resources, contributing to dynamic challenges that keep even experienced players on their toes.

Finance and Trading

When markets shift in the blink of an eye, you need a system that not only keeps pace but outsmarts the competition. Multi-agent systems deploy specialized agents that operate around the clock, monitoring market fluctuations, executing trades, and managing risks in real time.

These agents analyze vast data streams, identifying patterns and trends with minimal latency.

High-frequency trading firms such as Renaissance Technologies and Citadel leverage multi-agent systems to gain split-second advantages in the market.

By automating these critical tasks, they eliminate delays and reduce the risk of human error. The result is a dynamic trading environment where decisions are made faster and more accurately, allowing traders to capitalize on fleeting opportunities while mitigating potential losses.

Want to stand out with AI in the finance industry? Learn more about how we can give you a competitive edge here.

Designing and Implementing a Multi-Agent System

Now, to really explain how to design and implement a multi-agent system, we would need to jump on a call. But what we can do here is give a brief explanation of some of the critical steps involved:

Breaking Down Complexity

Instead of building one giant, unwieldy application, split your system into bite-sized, autonomous agents. Each agent takes on a focused responsibility, which means issues can be isolated and resolved without impacting the entire system.

This method helps you maintain tighter control over your application and makes it simpler to scale as your needs evolve.

Design Communication Protocols

Determine how your agents will talk to each other. This means choosing message formats, defining shared data structures (like a state channel or message list), and deciding whether agents share their full reasoning process or just the outcomes. Effective communication is the backbone of a MAS.

Choosing Your Architecture

There’s a lot of freedom in how you structure these systems. You could set up a hierarchical architecture, where a top-level supervisor directs smaller teams of agents, keeping everything on track.

Or maybe a custom workflow is more your style—one where you define exactly when and how each agent jumps into action.

And if you like having a central brain calling the shots, a supervisor-based architecture might be your best bet, with a main node dynamically deciding which agent should tackle the next part of the problem.

Select Your Tools

Look for platforms or frameworks that can streamline your build. For instance, a tool like LangGraph allows you to define state schemas and manage control flows.

Be sure to pick tools that align with your architectural vision.

Breaking an application into multiple smaller, independent agents and composing them into a multi-agent system can help tackle complex tasks.
The primary benefits of using multi-agent systems include improved control and management.
Multi-agent systems can be designed using various architectures, including hierarchical, custom, and supervisor-based architectures.
LangGraph allows for different state schemas, such as a search agent with a query and document schema.

You can also orchestrate pre-built individual agents and save yourself from reinventing the wheel, making the design aspect of this section irrelevant.

For example, you might use one of LangChain’s agents (Conversational Agent, Self-ask with search Agent, etc) to orchestrate a Claude agent that is using Model Context Protocol templates or Computer Use. Third party services such as Dialogflow and Azure Bot Framework can also play a role.

Deploy and Monitor

Roll out your integrated system and monitor its overall system performance. Use unified logging and monitoring tools to track how each agent contributes to the overall operation. This will help you quickly spot any issues or bottlenecks.

Use the feedback from real-world usage to refine your MAS.

Whether it’s updating communication protocols, reassigning roles, or adjusting the architecture, continuous improvement is key to keeping your system efficient and responsive as requirements evolve.

If you do go with pre-built agents, you can always add more agents to fill gaps or go with a hybrid approach and build custom agents for specific tasks.

Build Smarter Software with HatchWorks AI

Are your software development workflows falling behind because you can’t figure out the best way to leverage AI?

At HatchWorks AI, we can help you pave the path to effective AI adoption, cutting costs on development and shipping solutions faster.

We offer three options to ensure you get the exact level of support you need:

Built for You: Let our Generative-Driven Development™ process build your software faster and at a lower cost.
Train & Implement: Equip your engineering team with expert-led training to seamlessly integrate AI agents into your workflows.
Embedded Experts: Accelerate development by embedding our HatchWorks AI specialists directly into your team, ensuring projects move swiftly while knowledge transfers organically.