We've talked a lot about how to build a single agent to complete tasks for you. In a multi-agent or multi-agentic workflow, we instead have a collection of multiple agents collaborate to do things for you. When some people hear for the first time about multi-agent systems, they wonder, why do I need multiple agents? It's just the same LLM that I'm prompting over and over, or just one computer. Why do I need multiple agents? I find that one useful analogy is, even though I may do things on a single computer, we do decompose work in a single computer into maybe multiple processes or multiple threads. And as a developer, thinking, even though it's one CPU on a computer, say, thinking about how to take work and decompose it into multiple processes and multi-computer programs to run, that makes it easier for me as a developer to write code. And in a similar way too, if you have a complex task to carry out, sometimes, instead of thinking about how to hire one person to do it for you, you might think about hiring a team of a few people to do different pieces of the task for you. And so in practice, I found that for many developers of agentic systems, having this mental framework of not asking, what's the one person I might hire to do something, but instead, would it make sense to hire people with three or four different roles to do this overall task for me, that helps give another way to take a complex thing and decompose it into sub-tasks and to build for those individual sub-tasks one at a time. Let's take a look at some examples of how this works. Take the task of creating marketing assets, say you want to market sunglasses. Can you come up with a marketing brochure for that? You might need a researcher on your team to look at trends on sunglasses and what competitors are offering. You might also have a graphic designer on your team to render charts or nice-looking graphics of your sunglasses. And then also a writer to take the research, take the graphic assets and put it all together into a nice-looking brochure. Or to write a research article, you might want a researcher to do online research, a statistician to calculate statistics, a lead writer, and then an editor to come up with a polished report. Or to prepare a legal case, real law firms will often have associates, paralegals, maybe an investigator. And we naturally, because of the way human teams do work, can think of different ways that complex tasks can be broken down into different individuals with different roles. So these are examples of when a complex task were already naturally decomposed into sub-tasks that different people with different skills can carry out. Take the example of creating marketing assets. Look into detail into what a researcher, graphic designer, and writer might do. A researcher might have the task of analyzing market trends and researching competitors. And when designing the research agents, one question to keep in mind is what are the tools that the researcher may need in order to come up with a research report on market trends and what competitors are doing. So one natural tool that an agentic researcher might need to use would be web search. Because as a human researcher, asked to do these tasks might need to search online in order to come up with their report. Or for a graphic designer agent, they might be tasked with creating visualizations and artwork. And so what are the tools that an agentic software graphic designer might need? Well, they may need image generation and manipulation APIs. Or maybe, similar to what you saw with the coffee machine example, maybe it needs code execution to generate charts. And lastly, the writer has transformed the research into report text and marketing copy. And in this case, they don't need any tools other than what an LLM can already do to generate text. In this and the next video, I'm going to use these purple boxes to denote an agent. And the way you build individual agents is by prompting an LLM to play the role of a researcher or a graphic designer or a writer, depending on which agent it is part of. So for example, for the research agents, you might prompt it to say, you are a research agent, expert at analyzing market trends and competitors, carry out online research to analyze market trends for the sunglasses product and give a summary as well of what competitors are doing. So that would allow you to build a researcher agent. And similarly, by prompting an LLM to act as a graphic designer with the appropriate tools and to act as a writer, that's how you can build a graphic designer as well as a writer agent. Having built these three agents, one way to have them work together to generate your final reports would be to use a simple linear audit workflow or a linear plan in this case. So if you want to create a summer marketing campaign for sunglasses, you might give that prompt to the research agents. The research agent then writes a report that says, here are the current sunglasses trends and competitive offerings. This research report can then be fed to the graphic designer that looks at the data the research has found and creates a few data visualizations and artwork options. All these assets can then be passed to the writer that then takes the research and the graphic output and writes the final marketing brochure. The advantage of building a multi-agent workflow in this case is when designing a researcher or graphic designer or writer, you can focus on one thing at a time. So I can spend some time building maybe the best graphic designer agents I can, while maybe my collaborators are building research agents and writer agents. And in the end, we string it all together to come up with this multi-agent system. And in some cases, I'm seeing developers start to reuse some agents as well. So having built a graphic designer for marketing brochures, maybe I'll think about if I can build a more general graphic designer that can help me write marketing brochures as well as social media posts, as well as help me illustrate online webpages. So by coming up with what are the agents you might hire to do a task, and this will sometimes correspond to who are the types of human employees you might hire to do a task. You can come up with a workflow like this with maybe even building agents that you could choose to reuse in other applications as well. Now, what you see here is a linear plan where one agent, the researcher does his work, then the graphic designer, and then the writer. With agents, you can also, as an alternative to a linear plan, you can also have agents interact with each other in more complex ways. Let me illustrate with an example of planning using multiple agents. So previously, you saw how we may give an LLM a set of tools that we can call to carry out different tasks. In what I want to show you, we will instead give an LLM the option to call on different agents to ask the different agents to help complete different tasks. So in detail, you might write a prompt like you're a marketing manager, have the following team of agents to work with, and then give a description of the agents. And this is very much similar to what we're doing with planning and using tools, except the tools, the green boxes, are replaced with agents, these purple boxes that the LLM can call on. You can also ask it to return a step-by-step plan to carry out the user's request. And in this case, the LLM may ask the researcher to research current sunglasses trends and then report back. Then it will ask the graphic designer to create the images and then report back, then ask the writer to create a report, and then maybe the LLM will choose to review or reflect on and improve the report one final time. In executing this plan, you would then take the step one text of the researcher, carry out research, then pass that to the graphic designer, pass it to the writer, and then maybe do one final reflection step, and then you'd be done. One interesting view of this workflow is as if you have these three agents up here, but this LLM on the left is actually like a fourth agent that's a marketing manager, that is a manager of a marketing team, that is setting direction and then delegating tasks to the researcher, the graphic designer, and the writer agents. So this becomes actually a collection of four agents for a marketing manager agent coordinating the work of the researcher, the graphic designer, and the writer. In this video, you saw two communication patterns. One was a linear one where your agents took actions one at a time until you got to the end. And the second had a marketing manager coordinating the activity of a few other agents. It turns out that one of the key design decisions you may end up having to make when building multi-agentic systems is what is the communication pattern between your different agents? This is an area of hard research and there are multiple patterns emerging, but in the next video, I want to show you what are some of the most common communication patterns for getting your agents to work with each other. Let's go see that in the next video.
