Avoiding Mistakes in Defining Agents and Tasks in CrewAI

Summary

The video script discusses the importance of detail when defining agents and tasks within a Customer Intelligence (CI) system. The speaker uses the example of hiring an editor, outlining the roles of a business analyst, video editor, talent recruiter, and project manager. Each role is given a backstory to provide context and guide their behavior within the system. The speaker emphasizes the need for detailed task descriptions and expected outputs to ensure organized and consistent results. The video also touches on the use of various CI tools, such as web scraping and Google searches, and the significance of clear communication between agents. The speaker concludes by noting the importance of detailed prompts for effective agent interaction, drawing parallels to the way large language models like Chat GPT are trained to predict and fill in missing information.

Takeaways

• 📋 The importance of defining detailed agents and tasks when using Conversational AI (CI) is emphasized for effective results.
• 🔍 The speaker is using CI to assist in hiring the best editor by outsourcing research to a project manager in Korea.
• 💡 The level of detail in the agent's backstory provides context for their behavior, information search, and retrieval.
• 📈 The business analyst's role is highlighted as a bridge between customer needs and business requirements.
• 🎬 For the video editor, the focus is on connecting customer needs with professional skills, emphasizing expertise in freelancing.
• 🔑 The talent recruiter's task is to compile a job posting based on discussions with the business analyst and the video editor.
• 📝 The project manager is responsible for researching market rates and preparing well-organized documents for consistent results.
• 📚 The use of report templates is suggested to standardize the output from each agent, ensuring a structured and detailed final report.
• ✍️ The script mentions utilizing Chat GPT for generating report templates, showcasing the tool's utility in creating detailed prompts.
• 🤖 The effectiveness of CI tools depends on the detail and clarity of the prompts given to the agents, affecting their performance in complex tasks.
• 🌐 The upcoming project will involve advanced CI features like web scraping, Google searches, and adjusting communication hierarchies between agents.
• 📈 Large language models, like Chat GPT, are trained to predict and fill in missing information, which is crucial for detailed and accurate task performance.

Q & A

What is the main topic of the video?

The main topic of the video is about defining agents and tasks with CI (Conversational AI) in detail, using the example of hiring the best video editor for a specific project.

Why is the level of detail important when defining agents and tasks?

The level of detail is important because it provides the agents with context, guiding their behavior, the information they look for, and the information they are expected to retrieve.

What role does the business analyst play in the example?

The business analyst serves as a bridge between the customer's needs and the business, helping to translate broad requirements into more detailed specifications for the video editor.

How does the video editor's backstory contribute to the task?

The video editor's backstory, which emphasizes their expertise in freelancing, helps to connect customer needs with professional skills, ensuring a detailed understanding of the requirements for the editing task.

What is the talent recruiter's role in the process?

The talent recruiter is responsible for understanding the job requirements discussed with the business analyst and creating a job posting for the video editor position, potentially using freelancing websites.

What is the expected output from the business analysis task?

The expected output is a well-defined document that includes an executive summary, key findings, introduction, and background, providing a comprehensive report on the findings of each agent.

How does the use of report templates benefit the project?

Using report templates ensures that the output from each agent is organized and consistent, allowing for efficient information conveyance and solidified input for other agents in the process.

What is the significance of detailed expected outputs in tasks?

Detailed expected outputs help to guide the agents towards a specific outcome, ensuring that the final report is comprehensive, well-structured, and meets the project's requirements.

How does the video mention the use of CI features and tools?

The video mentions that various CI features and tools will be used to complete the project, including web scraping, Google searches, and adjusting the communication hierarchy or sequence between agents.

What is the role of large language models in understanding and predicting text?

Large language models are designed to understand the English language and predict what the next word or phrase will be, filling in blanks to complete sentences or paragraphs.

How does the training of Chat GPT differ from other large language models?

Chat GPT is trained on a question-and-answer basis, which makes it particularly useful for providing detailed answers to specific questions, as opposed to just predicting text.

What is the importance of well-structured prompts for agents in CI?

Well-structured prompts are crucial for agents in CI because they enable better communication between agents, leading to more effective performance and completion of complex tasks.

Outlines

00:00

📝 Defining Agents and Tasks with CI: A Detailed Approach

This paragraph discusses the importance of detail when defining agents and tasks in a project using CI (Conversational AI). The speaker is working on a project to hire an editor and uses different roles like a business analyst, video editor, talent recruiter, and project manager. Each role is given a backstory to provide context for their behavior and the information they seek. The speaker emphasizes the need for detailed task definitions to ensure effective communication between agents and to achieve consistent, well-organized outputs. The paragraph also mentions the use of report templates to structure the expected outcomes from each agent's tasks.

05:02

🤖 The Role of Detail in Effective AI Communication

The second paragraph explains the significance of detailed prompts for AI agents when performing complex tasks. It touches on the functionalities and tools of CI that will be utilized in the project, such as web scraping and conducting Google searches. The speaker highlights that without detailed agent and task prompting, these tools would be less effective. The paragraph provides an overview of how large language models like chat GPT work, focusing on their ability to predict and fill in missing information. It concludes by stressing the importance of detailed questions between agents for better performance in completing complex tasks and encourages viewers to practice using detailed agent and task definitions to observe the impact on their AI's responses.

Keywords

💡CI

CI, or Crew Intelligence, is a system that utilizes multiple AI agents to perform complex tasks. In the context of the video, CI is used to streamline the process of hiring an editor by assigning different roles to various AI agents, such as a business analyst, video editor, talent recruiter, and project manager. The AI agents work together, leveraging their specialized functions to achieve the desired outcome, which in this case is finding the best editor for the job.

💡Agents

In the context of the video, agents refer to the AI personas that are programmed to perform specific tasks within the CI system. Each agent has a unique role, such as a business analyst or a video editor, and is designed to interact with other agents to achieve a common goal. The agents are given detailed backstories and tasks to ensure they operate with a clear understanding of their objectives and the information they need to gather or provide.

💡Tasks

Tasks in the video refer to the specific duties or actions that each AI agent is assigned to perform within the CI system. These tasks are designed to be detailed and structured, with clear expected outcomes that contribute to the overall project goal. The tasks are not only about the actions the agents need to take but also about the quality and format of the output they produce, such as a well-defined report or a template for a job posting.

💡Backstory

A backstory in the context of the video is the detailed background information assigned to each AI agent. This information provides context for the agent's behavior, the type of information it should look for, and the information it is expected to retrieve. Backstories help to define the agent's role and guide its interactions with other agents and the system as a whole.

💡Detail

Detail in the video refers to the depth and specificity of the information provided when defining agents and tasks within the CI system. The level of detail is crucial for ensuring that the AI agents understand their roles and objectives, and it helps to produce more accurate and useful outcomes. Detailed definitions and tasks enable the agents to interact effectively and contribute meaningfully to the project.

💡Output

Output in the context of the video refers to the end result or deliverable that is produced by each AI agent as it completes its assigned tasks. The output is expected to be well-organized and detailed, following a specific format such as a report or a template. This structured output is essential for other agents in the system to reference and use effectively, ensuring a smooth flow of information and a coherent final outcome.

💡Freelancing

Freelancing in the video refers to the practice of working independently, often on a project-by-project basis, rather than being employed by a single company. The video editor agent is described as an expert in freelancing, which means they understand how to connect customer needs with the professional skills of freelancers and how to navigate the freelance market to find the best editor for the job.

💡Market Rates

Market rates in the video pertain to the typical compensation or payment structures for specific services or roles within a given industry. In this context, the talent recruiter is responsible for understanding market rates for video editors, which is crucial for setting a competitive pay scale and budgeting for the project.

💡Project Management

Project management as discussed in the video involves the coordination and organization of resources, tasks, and people to achieve specific goals within a project. The project manager agent is tasked with conducting research and managing the overall process of hiring an editor, including understanding the requirements, coordinating with other agents, and ensuring that all tasks are completed efficiently and effectively.

💡Communication

Communication in the video refers to the exchange of information between AI agents within the CI system. Effective communication is essential for the agents to work together and achieve the project's goals. The video discusses setting up hierarchies and sequences for agents to communicate, which helps in the efficient flow of information and the successful completion of tasks.

💡Chat GPT

Chat GPT is a language model used in the video as a tool for generating human-like text-based responses. It is utilized to create detailed prompts and templates for the CI system, helping to define the tasks and expected outputs more clearly. The video also mentions using Chat GPT to understand how large language models work and to train the CI agents for better performance.

Highlights

The video discusses the level of detail needed when defining agents and tasks with CI (Conversational AI) for a project

The project aims to use CI to find the best video editor for the user's needs

Four roles are defined: Business Analyst, Video Editor, Talent Recruiter, and Project Manager

Each agent has a detailed backstory to provide context for their behavior and information gathering

The Business Analyst serves as a bridge between the customer and the business needs

The Video Editor is an expert freelancer who understands customer needs and professional skills

The Talent Recruiter is responsible for creating a job posting and researching market rates

The Project Manager conducts research and provides detailed reports to other agents

The level of detail in agent and task definitions is crucial for the effectiveness of the CI tool

Writing detailed expected outputs for each task helps produce organized, consistent results

Using report templates from chat GPT can streamline the process of defining expected outputs

The agents communicate sequentially, passing information through detailed reports

CI tools like web scraping and Google searches will be used to complete the project

The way agents are prompted and tasks are defined is more important than the specific CI tools used

Large language models like GPT are trained to predict the next word or sentence in a sequence

The effectiveness of multiple agents communicating depends on the quality of their questions to each other

The tutorial aims to make using CI tools as easy as possible for the viewer

Previous projects and tutorials are linked in the video description for further practice

Transcripts

so in this video we're going to talk a

little bit about the level of detail

that you can go into when defining your

agents and your tasks with CI so for

this particular example this is still

going off from the original project that

we've been working on in this case I'm

trying to use CI basically to help me

figure out how to hire the best editor

that I can find given the needs that I

have for you know the editing task and

just I'm going to use a subset of AIS

one's going to be a business analyst

one's going to be a video editor the the

one's going to be a basically a talent

recruiter and then finally a project

manager to basically do the research for

me in terms of you know what I'm going

to need what the market rates are and

basically they're going to I'm

Outsourcing this to Korea in terms of

the you know the headache of the

research that I would have to do in

order to get everything ready to start

looking for an editor now what we're

going to talk about today it's not going

to be too technical again the point of

my tutorial is the reason why I'm trying

to to show you this is to make it as

easy as possible for you to understand

and be able to start using these tools

and I think one crucial component that

gets overlooked is the level of detail

that you could and should use when you

start writing out your agents and your

tasks so as I mentioned earlier what you

see up here these are the current

definitions of the roles so let me just

we're going to go over that real quick

so as you can see from the the business

analyst and all of them they all have a

pretty lengthy backstory so the back

story is really what's going to give

your agent context in terms of the way

it's supposed to behave the information

it's supposed to look for and also the

information that you wanted to retrieve

so for my business analyst again I

emphasize that it has very has a lot of

experience and I also emphasize that I

want it to be basically the bridge

between what the customer needs in this

case me and the business analyst is

going to communicate to the other parts

of the business you know what it is that

I want right because I can say that I

want a video editor and that sounds very

simple but with the perspective of

business analyst they can go more in

depth in terms of you know what those

requirements are going to

be so I'm here for the video editor

right for the video editor I also put a

backstory that they're you know experts

in freelancing that they understand how

to connect you know the customer needs

with professional skills and again I try

to be as detailed as possible in terms

of the outcome that I want and same

thing with the with a t Talent recruit

right at the end of the day I don't want

to waste time you know looking at market

rate again what it's going to cost me I

just know that I have a certain need and

I want that after the town recruiter

talks to the videor talks to the

business analyst they can more or less

put together what would be a job posting

for the position that I'm hiring with

and also emphasize some of these uh

freelancing websites as well and last

the project manager same thing I wrote a

few lies just to try to be as detailed

as I can be about how I want this agent

to be now for some of the other examples

that we've done we've usually kept the

backstory pretty short that was more so

to Showcase how to put the project

together and run it but again if you

want this tool to be useful to you you

do have to spend a little bit of time

writing these out the same way that the

same way that you get a good outcome

when you write you know a nice detailed

prompt on chat GPT so now we're going to

talk a little bit about the tasks so in

the tasks it's going to be even more

detailed so from like the business

analysis task for expected output you

see I have this long you know written

out basically a sample report of what I

wanted to give me so it's going to be an

executive summary with the purpose key

findings introduction background and

really I just want a very well- defined

document of what each of these agents

finds out now maybe that seems like that

would take you aot a lot of time to

write this out but honestly I just used

chat GPT I asked it for some report

templates if I have you know a video

editor that I'm trying to get a certain

analysis from and they basically Give It

All to me now for each agent I tried

different templates but I didn't want to

make sure that whatever output I got

from any of the tasks was a very know

very organized document that way when

the other agents reference it they're

also able to get very solidified input

and also whenever I run this screw I

want to get basically consistent results

all throughout and again when you write

out your expected output sure you could

just put one sentence like oh I want a

really good report about a really good

editor but that's that's very vague and

that's really not going to help you in

the long run the same thing for video

editor right the video editor they're

taking care of all the of anything

related to you know the professional

requirements that come with the things

that I need from a videor but same thing

the output I want from this task is also

going to be a template report again with

their feedback based on the information

that they gathered from the previous

agent and again as you can see this

repeats for for the recruitment task

from the talent recruiter and this

repeats from the project manager as well

they all basically output reports from

one to the other in order to convey

information very efficiently now as we

complete this project there's going to

be a lot of really cool CI features and

tools that we're going to use in order

to finish this complicated task but

again those tools that we're going to be

using and that's including scraping the

web that's including doing Google

searches with CI that's including

changing the way that the agents speak

to each other because you can set it at

a hierarchical level you can at a

sequential level none of those things

are going to matter if the way you've

prompted your agents and your tasks

isn't done with detail now that might

seem a little bit frustrating but let's

take a step back and talk a little bit

about how gpts work so before chat GPT

blew up large language models had been

something that a lot of people were

working on at the time now the way those

models worked it wasn't so much in a

chat you know question answer it was

more so they would set these models to

try and guess what the the end of a

sentence would be what the end of a

paragraph would be to fill in the blanks

for certain things now in a very

simplified way all large language models

are it's really just understanding the

English language and trying to predict

what the next thing is going to be on

there so if you had a blank sentence had

a letter missing or a word missing with

large language models the way they would

train this was to try and fill that out

now chipt became really useful because

the way that was trained was in a

question and answer basis so it's

something like crew AI something that is

meant to have multiple large language

agents speaking to each other just like

when you use chat gbt and you ask it if

you ask it a very detailed question you

get a very good answer if the

expectation is that these agents are

supposed to be communicating with one

another then I think also the

expectation should be that the better

the questions that they can ask between

each other the better they're going to

be able to perform in order to finish

that more complex task so that's going

to be it for today I'm still working to

finish up and clean up some things on

this project so we can go over some of

those more complex tools on CI as well

as setting them up so if youve watched

some of the other tutorials you've seen

how easy it is to set up crei using

Google collab I'm going to attach the

links on the description to the previous

projects that we've already finished so

that even if you don't have a project as

complicated this one you can start

practicing using those more detailed

agent definitions as well as task

definitions to try and see the

differences in the way that your crew or

your crew response reacts thank you for

watching and I'll see you in the next

one