Here's a bee. On its own, it can collect nectar, but that's about it. Now add thousands

of bees, and suddenly they're making honey. They're cooling the hive and defending it. That's exactly

how multi-agent systems work. Many simple AI agents, each with a small job,

coming together to solve big, complex problems. At its core, an AI agent is an

autonomous system that can perform tasks on behalf of another agent or another

system by designing its workflow and using available tools. And the performance of AI

agents depends on the large language model, or LLM, used to power them, as

well as their set of tools. And of course, a reasoning framework that dictates

how they take the output of these tools to make decisions. Multi-agent systems take this a step

further by allowing agents to remain autonomous, but also cooperate and coordinate in agent

structures. What are these structures look like? Let's start with a decentralized network.

Let's imagine you have several AI agents. Each of them

can communicate with one another to share information and resources to help

inform their decision process, each operating with the same amount of authority.

This is often referred to as an agent network.

There are also hierarchical structures which are tree like, and

they contain agents with varying levels of autonomy. The simplest example is a simple or

supervisor hierarchical structure in which one agent has the decision-making authority

over other agents. And by adding more layers

and more subtrees, we can make the system more complex.

There are many ways to distribute the authority here, for example, in uniform hierarchical

structures. All agents at the same level play the same role and have the same authority.

And the agents coordinate laterally, meaning at the top of the structure you might have a single

manager or a coordinator agent, and in the middle levels there are supervisor agents, each

managing a group of agents below them. And at the bottom these are the worker agents. So they're

directly performing the tasks. And this structure helps break down responsibilities. So, higher

levels coordinate. Lower levels execute. Let's keep in mind, though, that the authority doesn't have to

be strictly top down or centralized. It can also be distributed across sub-hierarchies, in

which one agent has the decision-making authority over other agents. Or maybe we

want the structure to be dynamic so that the authority shifts based on agent expertise or on

a situational basis. Now that we've covered the basics, let's talk about implementing these agents

in the real world. What are the advantages of using multi-agent systems over a

single agent? Well, to start they provide us with flexibility. This is

because multi-agent systems can adjust to varying environments by adding, removing or

adapting agents. Multi-agent systems also allow for scalability. And

the cooperation of several agents implies a greater pool of shared information, and this

collaboration allows these systems to solve more complex problems than a single agent could.

These systems also encourage domain specialization. in a single-agent structure, one agent

performs tasks in various domains, whereas each agent in a

multi-agent system can hold specific domain expertise. Perhaps one agent specializes in

synthesizing research papers, the other performs complex calculations and another specializes

in web search via an API. The last advantage I want to emphasize here is that, believe it or not,mult

multi-agent systems outperform single agents. And this is because the more

action plans that are available to an agent, the more learning and reflection occur. An AI agent

incorporating knowledge and feedback from other agents allows for a greater magnitude of

information synthesis. And of course, as with most things in life, there are some

challenges with building multi-agent systems. When they're built using the same LLMs, for example,

they can ex experience shared pitfalls and agent malfunctions.

Such weaknesses might cause a system-wide failure of all involved agents or expose

vulnerability to adverse attacks. Our choice of the LLM, among other factors,

impacts how effectively an agent performs, and this is why thorough training, testing and data

governance are critical to minimizing failures. Another challenge of building multi-agent

systems is coordination complexity. As developers, we're tasked

with enabling coordination and negotiation between agents so that they're not competing with

resources or simply overriding each other's outputs. Instead, agents need

mechanisms to share information, resolve conflicts and synchronize decisions in a way that

maximizes the collective performance rather than causing bottlenecks or contradictions. And

the last challenge we'll touch on here is the risk of unpredictable behavior.

Though this drawback in and of itself isn't unique to multi-agent systems, it can easily be

amplified. Typically, the more agents are involved, the greater the unpredictable behavior becomes.

All right. Now, if we want to avoid having too many cooks in the kitchen, when is it best to stick to

a single agent system versus a multi-agent system? Well, it depends on the task and our

objectives, right? If the problem is complex, and

perhaps it spans various domains. Maybe it has limited resources

or it needs to scale across changing environments. This is exactly

when a multi-agent system truly shines. If we want to

build on this restaurant analogy of cooks in the kitchen, for example, let's put it this way. One

chef is completely fine for a small kitchen, right? Think of it like making breakfast for yourself.

But if you're running a restaurant that serves different cuisines and desserts, etc., you'll want

the whole kitchen staff working in sync. I hope you found this video useful and if you have

questions or comments, let me know below. Don't forget to like the video and subscribe for more

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