Chatbot or AI Agent Setting up crewai framework for scaling tasks

Summary

The speaker discusses the differences between AI agents and chatbots, highlighting the advanced decision-making capabilities of AI agents. They share their experience with using AI for podcast guest research and content creation, emphasizing the efficiency of AI in automating repetitive tasks and generating thoughtful questions. The speaker also outlines the components of a 'crew' in AI, including agents, tasks, and tools, and suggests that AI agents are suitable for complex tasks and personal use cases, while chatbots are limited to specific domains.

Takeaways

• 🤖 AI agents are the next step beyond tools like ChatGPT, offering autonomous decision-making capabilities.
• 🔍 The speaker is experimenting with AI, specifically using a cloud-based platform called Replit for their AI setup.
• 📚 AI agents can automate manual research processes, such as gathering information about a guest for a podcast.
• 🔗 The AI workflow involves a sequence of tasks, where one agent's output serves as another agent's input.
• 🔎 Tools like DuckDuckGo can be integrated for data gathering in AI workflows.
• ✍️ One AI agent might generate thoughtful questions based on another agent's research, showcasing the collaborative nature of AI agents.
• 📈 AI agents can be used for complex tasks like scheduling appointments and controlling email, unlike chatbots which are limited to specific domains.
• 🚀 The concept of 'crew' is introduced, which is a group of agents working together on a sequential or hierarchical process.
• 📝 AI agents have defined roles and backstories, which help them make decisions and escalate tasks when necessary.
• 🛠️ The speaker emphasizes the importance of defining the agent's goal and role for effective task delegation and decision-making.
• 🌐 There are various resources available for learning about AI agents, including templates, Discord channels, social media, and YouTube videos.

Q & A

What is the primary difference between AI agents and chatbots as discussed in the transcript?

AI agents are designed to process complex queries, adapt responses based on user interactions, and perform tasks such as scheduling appointments and controlling email, whereas chatbots are limited to a specific domain and focused on a narrower set of tasks, like acting as a knowledge base.

How does the speaker's perception of AI have evolved over time?

The speaker initially assumed AI would have all-encompassing, all-knowing innate knowledge and anticipate desires. However, they realized that tools like Chat GPT, while incredible, lack decision-making abilities, leading them to explore AI agents as the next step for more autonomous functionality.

What is the role of a research assistant AI in the speaker's workflow?

The research assistant AI helps the speaker by automating the manual research process when bringing on a guest. It looks up the guest's LinkedIn profile, previous posts, podcasts they've been on, and generates thoughtful questions based on the gathered information.

Can you explain the sequential process the speaker uses with AI agents?

The sequential process involves defining a topic, using an external tool like DuckDuckGo for research, passing the data to another agent, and having that agent generate a summary and questions. This is a linear assembly line approach to task completion.

What is a crew in the context of AI agents?

A crew is a group of AI agents working together on a sequential or hierarchical process. It involves agents, tasks, tools, and is designed to automate and streamline complex workflows.

How does the speaker ensure their AI agents are scalable?

The speaker ensures scalability by using a regularly updated repository with examples and templates that can be added to the crew. This allows for multiple agents to plug in and work together on tasks.

What tools can AI agents use to perform their tasks?

AI agents can use a variety of tools such as online data analysis platforms, web scraping tools, and social media for gathering information and performing their tasks.

How does the speaker address the issue of AI-generated responses being too verbose?

The speaker uses system prompts to instruct the crew to be more actionable and concise, eliminating unnecessary fluff from the responses.

What is the significance of an agent's backstory in the hierarchical decision-making process?

An agent's backstory defines its specific roles and goals, which helps in hierarchical decision-making by allowing the agent to escalate tasks to other agents when it encounters decisions beyond its capabilities.

How does the speaker plan to use AI agents locally?

The speaker is looking forward to using AI agents locally on their own machine once they become more familiar with the process and the tools involved.

What are some examples of use cases for AI agents mentioned in the transcript?

Some examples include making hiring decisions, acting as a website developer using Next.js, writing job descriptions, and planning trips.

Outlines

00:00

🤖 AI Agents vs Chatbots and Decision Making

This paragraph discusses the difference between AI agents and chatbots, highlighting the decision-making capabilities of AI agents. The speaker clarifies that while Chat GPT is impressive, it lacks the autonomous decision-making ability that AI agents possess. They describe their experience with AI agents, particularly using a cloud-based platform called Replit, and explain how AI agents can automate the research and question generation process for podcast guests. The paragraph also touches on the sequential task assembly line approach and the use of different AI models for various tasks.

05:00

🔄 Hierarchical and Sequential AI Agent Processes

The speaker delves into the hierarchical and sequential processes of AI agents, explaining how complex queries are broken down into subtasks. They mention the concept of a 'crew' in AI agent frameworks, which is a group of agents working together on a task. The paragraph also addresses the difference between chatbots and AI agents, emphasizing that chatbots are limited to specific domains while AI agents can handle more complex, personal tasks like scheduling and email management. The speaker shares their experience using AI for decision-making in hiring and web development, and points out the importance of defining roles and goals for agents. They also discuss the tools used by agents, such as data analysis and web scraping, and the option to customize the response style of agents.

Keywords

💡AI agent

An AI agent refers to an autonomous program designed to perform specific tasks, make decisions, and interact with users or other systems. In the context of the video, the AI agent is distinguished from a chatbot by its ability to handle complex queries and adapt responses based on user interactions, as well as execute tasks such as scheduling appointments or controlling email. The speaker is exploring AI agents for their potential to automate manual research and content creation processes.

💡Chatbot

A chatbot is a conversational computer program designed to simulate how a human would respond to user inputs. Unlike AI agents, chatbots are typically limited to a specific domain of knowledge and are used for retrieving responses to queries. They can escalate to AI agents when more complex decision-making is required. In the video, the speaker contrasts chatbots with AI agents, highlighting the latter's superior capabilities in handling complex tasks and interactions.

💡Decision making

Decision making in AI refers to the process by which an artificial intelligence system can make choices or determine actions based on input data or predefined conditions. The video discusses how AI agents have a decision-making ability that chatbots lack, allowing them to adapt responses and execute tasks autonomously. The speaker is interested in using AI agents to automate the decision-making process involved in researching podcast guests and creating content.

💡Replit

Replit is a cloud-based platform that allows users to create, collaborate, and deploy code. In the video, the speaker mentions using Replit as the cloud environment to set up and run their AI agents, indicating that Replit provides the necessary infrastructure to support the development and operation of AI agent workflows.

💡DuckDogo

DuckDogo appears to be an external tool mentioned in the video, which is likened to Google, and is used by the AI agent to return research data. It seems to be a search engine or a data retrieval tool that aids in gathering information for tasks such as researching podcast guests.

💡Podcast content creator

A podcast content creator is an AI agent in the video that generates questions for a podcast based on research data provided by another agent. This role demonstrates the specialization of AI agents in content creation, where they can produce thoughtful and engaging content tailored to the research findings.

💡Crew

In the context of the video, a 'crew' is a collection of AI agents working together to perform a sequential or hierarchical process. It represents a team of agents, each with a specific role, collaborating to complete complex tasks. The crew is managed through a developer framework that chains together automations and defines the actions of the AI agents.

💡Hierarchical process

A hierarchical process in AI involves breaking down a complex task into smaller subtasks and assigning them to different agents based on their roles. This structure allows for a more organized and efficient workflow, where each agent knows its place and responsibilities within the larger task. The video discusses how AI agents can escalate decisions to higher-level agents if they cannot handle a particular subtask.

💡Backstory

In the context of AI agents, a backstory defines the narrative or history of an agent, which can influence its decision-making and actions. The backstory helps to contextualize the agent's purpose and goals, allowing it to act in a manner consistent with its defined role. The video emphasizes the importance of giving each AI agent a backstory to guide its behavior and interactions.

💡Scalability

Scalability refers to the ability of a system, process, or model to handle growth effectively. In the context of AI agents, scalability means that the system can accommodate an increasing number of agents or tasks without a significant degradation in performance. The video highlights the benefits of scalability, allowing for the operation of an entire crew of AI agents to manage complex workflows.

💡Task delegation

Task delegation is the process of assigning tasks to other agents or systems. In the AI context, it allows for the distribution of work among different agents, each with specialized roles, to optimize the workflow. The video discusses how AI agents can delegate tasks to other agents or confirm actions before execution, ensuring efficient and accurate completion of tasks.

Highlights

AI agents represent the next step in AI technology, offering autonomous decision-making abilities beyond the capabilities of platforms like ChatGPT.

ChatGPT, while impressive, lacks the decision-making prowess that AI agents bring to the table.

AI agents can automate manual research processes, such as gathering information about a guest for a podcast.

The use of AI agents can streamline sequential tasks, such as researching and generating content for a podcast.

AI agents can leverage external tools like DuckDogo for research, showcasing their ability to integrate with various platforms.

The concept of 'crew' in AI involves a team of agents working together on a sequential process.

AI agents differ from chatbots in their ability to process complex queries and adapt responses based on user interactions.

Chatbots are limited to specific domains and tasks, whereas AI agents can perform a broader range of functions.

AI agents can be personalized with backstories and specific roles, enhancing their ability to handle tasks and make decisions.

Hierarchical decision-making in AI agents allows for complex task breakdown and delegation.

AI agents can be scaled to run entire 'crews,' managing multiple tasks simultaneously.

The AI agent framework is highly adaptable, with numerous use cases and examples available on platforms like Discord and social media.

AI agents can be used for practical applications such as hiring decisions or website development with Next.js.

The ability to customize responses and remove unnecessary fluff enhances the practicality of AI agents.

Delegation capabilities in AI agents allow for confirmation before taking action on tasks.

AI agents can be utilized both in the cloud and locally on a machine, offering flexibility in deployment.

Transcripts

agents versus chap pods so I was

speaking to someone recently about what

an AI agent is and they were like oh

I've already got chat GPT I don't need

an AI agent I was like hold up you do do

you even know what that is so when I

don't know about you but when I used to

think about AI I assumed that it would

just have this all-encompassing all

knowing innate knowledge

that and be able to anticipate my next

kind of desire and move and then we

ended up getting chat gbt which is

incredible don't get me wrong but it's

kind of missing that decision making

ability so the AI agents is that next

step that's the autonomous part and cre

AI is what I've been trying out but

there's a couple of different

Frameworks and I've got it set set up uh

on the cloud it's called replit and I'll

pop the blog post that I said that I

would but I made a video that got a lot

of questions about the different types

of I guess decision making like well how

do you know when to actually give it a

task versus uh consult with you so

there's a few different ways that this

AI makes decisions and the way that I've

got mind set from my personal use case

was okay I would like I wish I didn't

have to do so much manual research when

I'm bringing somebody on as a guest so

the great thing with having a research

assistant is it takes that manual

process that I do repeatedly which is

things like go to the person's LinkedIn

or see any previous posts or um podcasts

that they've been on see what they've

talked about and then think up some good

questions uh based on the prior context

and that I would say is a sequential

kind of assembly line way of doing the

tasks so for example you might have the

piece of code would say uh you'd have

the name of the agent and in my case it

would be the researcher and then you

define the topic and my must podcast

research on this specific name and the

external tool was is duck Dogo which is

like Google and then it Returns the

research data and then it will pass it

along to another agent and the other

agent that I had was a podcast content

creator who would write pretty verbose

and thoughtful questions based on the

research that was given from the other

agent and so that agent might have a

name length writer and it would generate

a summary and then it would use a

language model which doesn't have to be

open AI it can be um clawed or it can be

you know any local model I uh sorry open

model I have played around with um

Claude and I want to make extra effort

to kind of test out everything I don't

know if you've

experienced the quality of writing from

chat GPT leaves a little bit to be kind

of desired so then you do the the Run

command and then query the kind of

foundation is creating a crew and a crew

is a a number of agents for that

sequential process there is also another

way which is the more hierarchical which

I briefly went into considering it was

like a 30C Tik Tok with a really simple

developer framework that lets you chain

together automations and really Define

your AI agents to do different things in

either hierarchical or sequential

way what do I mean by that some of the

key components that go into crew are

agents tasks tools and then you form a

crew here's probably a great place to

stop and talk about the difference

between a chatbot and an AI agent so if

you're looking to process complex

queries uh and adapt your responses

based on user interactions or if it's a

personal use case for your own

interaction and learning and execute

tasks like scheduling appointments

controlling your email and the things

that you do on a day-to-day basis you'd

be looking at an AI agent if you're

looking for something as like a

knowledge Bas or a something that's

limited to a specific domain then you'd

be looking at a chatbot so chatbots are

focused on a much more narrow kind of

set of tasks and so I've put them in the

simple chatbot logic and they're able to

retrieve responses but then they can

escalate to AI agents so AI agents are

able to actually per perform the task

that's given to them have a backstory

for each of your agents and you define

like the specific roles so you say okay

you're just a writer if you can't figure

it out then you you have to kind of pass

it along so a hierarchical or manager

process would be giving a query and then

it would break it down into different

subtasks then there's what I think is

end state or kind of desired state which

is more of the brain that scalability is

great because that all the agents to

kind of plug in like multiple agents so

you could be running an entire team crew

is a regularly updated repo and you'll

find a lot of examples being added on

the Discord on social media lots of

YouTube videos that go into use cases

like I use crei to make hiring decisions

or to uh act as a website developer

using

nextjs but a good starting place is

either on replat stter templates and

they've got a a few of the sort of plug

andplay examples like writing job

descriptions planning trips uh the

things that I think are really important

to note are just the basic definitions

so not really having a role like the

function of an agent without a goal what

are you actually giving the agent its

task so then when you get further down

the line for

those hierarchical decision making it

will know okay I not able to make this

decision I'm going to pass this on to

another agent that's where the backstory

comes in as well so that's background

information about why it's trying to

achieve this potentially there's a long

list of tools that you could be using so

whether that's On's online for data

analysis web scraping social media as I

mentioned before

um and for both is how worthy you would

like your agent to respond I noticed

that when I'm using something like chat

gbt there's always that massive

introduction paragraph at the beginning

and you can you can essentially system

prompt crew to not give you the fluff

and be more actionable allowing

delegation allows you to delegate to

other agents or to come back and confirm

that that's right before taking action

on something else so for example if you

had an agent that was processing

invoices it would be able to check with

you first on the final invoicing amount

and that's the kind of very high level

again uh summary of crew and and its

capabilities as I mentioned I'm using it

uh via Cloud so on replit but I am

looking forward to using it locally on

my own machine once I kind of get up to

speed