Chatbot or AI Agent Setting up crewai framework for scaling tasks
Summary
TLDRThe 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
🤖 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.
🔄 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.
Mindmap
Keywords
💡AI agent
💡Chatbot
💡Decision making
💡Replit
💡DuckDogo
💡Podcast content creator
💡Crew
💡Hierarchical process
💡Backstory
💡Scalability
💡Task delegation
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
Ver Más Videos Relacionados
"I tested 400 AI Agents, these are the best" - Adam Silverman
"Agentic AI" Explained (And Why It's Suddenly so Popular!)
5 AI Agents You Can Build Today (100% no-code)
Crew AI Build AI Agents Team With Local LLMs For Content Creation
I Taught AI Agents How to Build Ads for Me [5 FREE ChatGPT Scripts]
LangChain Agents: A Simple, Fast-Paced Guide
5.0 / 5 (0 votes)