Hey! It's me again, and I'm back to talk about agents. In my last videos, I covered topics like

the differences between agentic AI and conversational assistants, and I even explained

orchestrator agents. You know, the ones that are supervising how work gets done across tools and

other agents, knowing which agents do what and are basically like a nervous system for AI tools.

Orchestrator agents are helpful when we have multiple sub-agents collaborating or working

together. More on that later. But, quick tip: When this happens, we have what's called a multi-agent

system. Of course, there are various types of this orchestration, like centralized or hierarchical.

But that's for another video. Today, we're going to lift the hood and explore what's happening under

the covers. Let's talk about what's going on when multi-agent systems work together to get work

done through an orchestrator agent. Let's use an example. Pretend that you're at work and you've

asked an orchestrator agent something like, I need your help to write some customized thank-you

notes to the members of my team that helped me with our most recent project.

The question's been sent in and it's time to get to work. Once the orchestrator agent of choice is

set up, of course, and the APIs are connected for data access and the task execution sequences are

defined, orchestration usually occurs in a few steps. The first step is all around

agent selection. The second is more around workflow

coordination. The third, of course, is the data sharing.

And lastly, of course, is continuous learning. Let's

start with agent selection. This is where the orchestrator agent is doing the part of its job

that's probably most familiar to you if you watched my last video. Think of it as flipping through a

booklet of members on its team that it knows can help. It looks through a catalog of existing

agents and tools and makes a selection for the right ones for the job. If you're writing these

thank-you notes from our example, the orchestrator agent might decide it wants to collaborate with a

project management system, an email-writing or

-generating agent, and the employee appreciation app that your

company uses. The next step is workflow coordination. This is when the orchestrator will

break down the task of getting these thank-you notes into subtasks, assign them to the right

agents or tools, and use APIs to connect any systems to get the right data. The

orchestrator agent is going to integrate via API to the project management system, which of course,

has information on the team members who helped on the different projects. It will leverage the email

generation agent that can generate thank-you notes in a certain tone or style that suits us

best, and the employee appreciation app that your team uses to then send thank-you notes. Then

it's all about data sharing. Each agent or tool executes their subtasks and sends that

information all the way back to the orchestrator agent.

Boom! It's important to note that through this process, the AI agents and tools working together,

which we would actually refer to as sub-agents, are constantly sharing information and context.

The orchestrator keeps the agents in the multi-agent system updated in real time. Quick

pause on our list for just a moment. You likely know better than anyone that most AI users have

more than one tool from various vendors. They're all built a bit differently and can help in

various ways. What happens, though, when the sub-agents or systems where we're pulling data from

are not from the same vendor? Maybe they weren't coded in the same language. What do we do? We

rely on MCP. MCP or model context protocol

gives your agent the ability to ask, hey, give me information about X without knowing

where the information is stored or how it's retrieved. MCP has been described as some as kind

of like a USB-C port for AI applications. The M stands for model, of course,

and this is referring to the large language model at the heart of your agent. The C is context.

This is all about the extra information needed to get work done—maybe documents, search results, or

data from some of those systems we talked about earlier. And the P, of course, is protocol. This is

the standardized way of communicating that lets the model interact with those tools and data

sources. Okay, back to the list. The outcome of the task is packaged all

together into what's called an artifact. Think of that as the deliverable or the result of the task.

Before you know it, the orchestrator agent returns a nicely written thank-you note to each of your

teammates. If you're ready to automate, maybe it'll even ask you if it wants to do it for you through

the employee appreciation tool, all powered by the agent. You don't even have to leave the chat

window. You reply with yes, please, thank you, and get a confirmation that the notes have been sent.

Now, don't forget—that last part of the process is continuous learning. Agents are very, very good at

looking back and can reflect on their work. Orchestrator agents will monitor performance and

make any tweaks needed for next time. Orchestrator agents are not only key to a multi-agent system

strategy but are super helpful when it comes to selecting agents from the job and coordinating

workflows, accessing data thanks to MCP, and reflecting for improvements. Next

time you use AI agents to get work done, instead of supervising them yourself, bring in that

supervisor agent so you can take a break.