"Agentic AI" Explained (And Why It's Suddenly so Popular!)

Summary

The transcript discusses AI agents as specialized software leveraging large language models for autonomous tasks. These agents can range from chatbots to computer vision and robotics, with the potential to handle complex tasks like travel planning. The concept of a 'Collective' is introduced, where multiple specialized agents work together seamlessly to provide a user-friendly experience. The importance of a natural language interface and the role of scaffolding in agent autonomy are highlighted, emphasizing the shift towards a marketplace of agents and the need for protocols to assist users in selecting the right agents for their needs.

Takeaways

• 🤖 AI agents are software that leverage AI, particularly large language models, to perform tasks autonomously.
• 🗣️ The simplest AI agents respond to user prompts and execute tasks, such as providing daily inspirational quotes.
• 🛠️ Specialized AI agents can perform specific tasks like planning itineraries, booking accommodations, and managing finances.
• 🤝 Collectives of specialized AI agents can work together to provide seamless user experiences, like planning a complete trip.
• 🌐 AI agents can interact with dynamic algorithms and websites to perform tasks like booking flights at optimal times.
• 🔗 The use of AI agents is becoming popular due to their ability to specialize and perform tasks with high efficiency.
• 💼 AI agents can be utilized in a Software as a Service (SaaS) model, moving users through different software tasks.
• 📱 AI agents can be accessed through natural language interfaces, making them easy to interact with through conversation.
• 🏪 The concept of an 'agent store' or 'marketplace' is emerging, where users can find and utilize various specialized AI agents.
• 🧩 Chainal Labs' product, Theoric, aids in decision-making for which AI agent to use within a Collective, offering an assisted selection process.

Q & A

What is an AI agent as described in the transcript?

An AI agent is software that leverages AI, particularly large language models, to perform tasks autonomously. It can act in an autonomous way, planning and executing on tasks, and can be specialized for specific functions like planning itineraries or managing bookings.

How does a large language model fit into the concept of an AI agent?

Large language models are used as the foundation for AI agents, providing the capability to understand and process natural language prompts. These prompts can then be used to scaffold or guide the agent's actions and tasks.

What is meant by 'decentralized execution' in the context of AI agents?

Decentralized execution refers to the ability of AI agents to perform tasks autonomously without continuous human intervention, allowing them to operate across different platforms or services to complete a user's request.

Why are specialized AI agents considered valuable?

Specialized AI agents are valuable because they can become highly proficient at performing specific tasks. This specialization allows them to provide tailored services that meet the unique needs of different users or situations.

Can you explain the concept of a 'Collective' in relation to AI agents?

A 'Collective' refers to a group of specialized AI agents that work together to perform a series of related tasks. For example, a travel Collective might consist of agents that plan itineraries, book accommodations, and manage travel bookings, all in a seamless and coordinated manner.

How does an AI agent differ from a simple chatbot?

An AI agent differs from a simple chatbot in that it can act autonomously, performing tasks beyond just conversing with the user. Agents can execute actions based on user prompts and can operate across different platforms to complete complex tasks.

What is the significance of the term 'scaffolding' in the context of AI agents?

In the context of AI agents, 'scaffolding' refers to the additional software instructions that guide the agent's actions based on user prompts. It helps structure the agent's behavior and task execution, ensuring it performs as intended.

How does the use of AI agents streamline the user experience?

AI agents streamline the user experience by automating tasks and integrating multiple services seamlessly. This allows users to interact with agents in a conversational manner while the agents handle complex processes in the background.

What role does the GPT store play in the ecosystem of AI agents?

The GPT store serves as a marketplace where users can access and utilize various AI agents. It allows developers to share their specialized agents, and users to find and incorporate them into their workflows.

How does Chainal Labs' Theoric product assist in the decision-making process for using AI agents?

Theoric by Chainal Labs provides a protocol with mechanisms to assist users in deciding which AI agent to use within a Collective. It helps users make informed choices about the most suitable agents for their specific needs.

Outlines

00:00

🤖 Understanding AI Agents and Their Specialization

The paragraph introduces AI agents as software that uses AI, particularly large language models, to perform tasks autonomously. It explains that these agents can be specialized for specific tasks, such as planning itineraries for different types of travelers. The example of an agent providing an inspiring quote daily is used to illustrate how agents can execute tasks based on user prompts and instructions. The concept of a 'Collective' is introduced, which is a group of specialized agents working together to provide a seamless user experience, such as planning a trip that involves booking flights and accommodations.

05:01

🛒 The Role of Agents in Automation and Decision-Making

This paragraph discusses the automation capabilities of AI agents, distinguishing them from simple conversational AI by their ability to perform tasks autonomously over time. It uses the example of receiving an inspiring quote daily to highlight the difference between agentic AI and reactive AI. The paragraph also touches on the idea of a marketplace for agents, where users can find and use specialized agents for tasks like itinerary planning. It introduces the product from Chainal Labs, which helps users decide which agent to use within a Collective, suggesting a protocol that assists in making these decisions in an automated manner.

Keywords

💡AI Agent

An AI agent, as discussed in the video, is software that utilizes artificial intelligence, particularly large language models, to perform tasks autonomously. The video emphasizes that these agents can specialize in specific tasks, such as planning an itinerary or managing bookings, and can operate in a conversational manner with users. The concept is central to the video's theme of how AI can be leveraged to create specialized, user-friendly software that can execute complex tasks.

💡Large Language Models

Large language models are a type of AI that can understand and generate human-like text. In the context of the video, these models are used as the foundation for AI agents, enabling them to process natural language prompts and execute tasks based on those inputs. The video suggests that by wrapping software around these models, developers can create agents that can perform a wide range of specialized functions.

💡Autonomous

The term 'autonomous' in the video refers to the ability of AI agents to operate independently, without constant human intervention. This is a key feature that distinguishes AI agents from simple chatbots or interactive systems. The video gives the example of an agent that can provide an inspiring quote daily without the user having to manually request it each time, showcasing the agent's autonomous capabilities.

💡Specialization

Specialization in the video is used to describe how AI agents can be designed to excel at specific tasks. This is highlighted as a significant advantage of AI agents, as they can be tailored to meet the unique needs of different users or scenarios. For instance, the video mentions an agent that specializes in planning travel itineraries for different types of travelers, such as families or elderly individuals.

💡Dynamic Algorithms

Dynamic algorithms are mentioned in the context of platforms that use changing data to make decisions in real-time. The video suggests that AI agents could interact with these algorithms to assist users in tasks like booking flights or accommodations. The use of dynamic algorithms is an example of how AI agents can leverage complex computational methods to provide value to users.

💡Collective

A 'Collective' in the video refers to a group of specialized AI agents working together to perform a series of related tasks. The example given is a 'travel Collective,' where different agents handle itinerary planning, booking, and financial management. The concept illustrates how AI agents can be integrated to provide a seamless, comprehensive service to users.

💡Scaffolding

Scaffolding, as used in the video, is a metaphor for the support structures or additional software instructions that enable AI agents to perform their tasks autonomously. It's mentioned in the context of how AI agents can be programmed to execute a series of actions based on initial user prompts, such as an agent that provides daily inspirational quotes.

💡Seamless Experience

A seamless experience is described in the video as the end goal of integrating AI agents into user workflows. It implies a smooth and uninterrupted interaction with AI systems, where tasks are completed without the need for the user to switch between different software or platforms. The video suggests that AI agents can help achieve this by knitting together different activities and tasks.

💡No-Code Solutions

No-Code Solutions are tools that allow users to build applications without writing code. The video touches on how these solutions are making it easier for anyone to create AI agents, such as itinerary agents, and contribute them to a marketplace. This highlights the democratization of AI development and the potential for a wide range of specialized AI agents to be created by non-developers.

💡Marketplace

In the video, a marketplace is mentioned as a platform where multiple AI agents can be accessed and used by users. It's likened to a store where users can find and select agents that meet their specific needs, such as agents for travel planning or financial management. The concept of a marketplace underscores the growing ecosystem of AI agents and the potential for users to curate personalized collections of agents.

Highlights

AI agents are software that leverage AI, particularly large language models, to perform tasks autonomously.

AI agents can specialize in specific tasks, such as planning itineraries for different types of travelers.

Agents can be combined into a 'Collective' to perform a series of tasks seamlessly.

The use of AI agents is becoming popular due to their ability to specialize and perform tasks with high efficiency.

Agents can interact with dynamic algorithms to assist in tasks like booking flights and accommodations.

An agent can manage a user's wallet, handling different credit cards, accounts, and even digital currencies.

AI agents can be used to create a seamless travel experience by knitting together different tasks.

Large language models (LLMs) power some of the capabilities of AI agents, making them easy to interface with.

Agents act autonomously, setting them apart from continuous conversations with LLMs.

Agents can be programmed to perform tasks proactively, such as delivering daily inspirational quotes.

The concept of 'scaffolding' is used to describe the support structure that allows agents to work autonomously.

Chain ML's product, Theoric, assists in decision-making about which agent to use within a Collective.

No-code solutions enable anyone to build and contribute AI agents, such as itinerary agents, to a marketplace.

Theoric by Chain Labs provides a protocol with mechanisms to assist in the selection of the right agent for a task.

AI agents represent a shift from manual interactions with software to a more automated and autonomous experience.

Transcripts

[Music]

so tell us about AI agents and then

maybe get into what it means for to have

decentralized e execution and

utilization layer for for these agents

so just for everybody who's listening uh

an AI agent let's think of it as

software that leverages Ai and when we

say AI for the moment the agents that

are the hottest right now are the ones

that are using large language models so

think of of a chatbot if you build

software around a chatbot so that it

behaves sort of in an autonomous kind of

way it's able to do some planning and

then execute on a task let's call that

an agent there are multiple variations

you we have computer vision and if you

wrap your software on computer vision

you could call that an agent um if you

put that agent in Hardware we could

start talking about robotics but the

simplest way to think about an agent is

really software that we wrap around

artificial intelligence and specifically

these language models so the simplest

implementation of an agent is where you

enter a prompt and that prompt executes

on something so uh I would like a quote

every day that inspires me to start my

day so I write that prompt into my large

language model and then I scaffold that

with uh some other instructions software

instructions for how I get that message

delivered to me whether that's in an app

or maybe I just open up uh uh you know

one of the the products right the gpts

that's what we've been calling them and

so that's how you might start

interacting with an agent now the reason

we're talking about agents now and why

they're hot is because agents specialize

right so if you're working with an agent

that does something very specific that

agent can get very very good at doing

something specific so my favorite

example is something like airline travel

you would have an agent that helps you

plan an itinerary now that's a task that

does require specialization because if

you're going for a bachelorette party or

if you're traveling with your elderly

parents or with your kids all of those

are quite different itinerary so you

want to have some sort of specialization

on what are the things that will make

that a successful trip so that's one

agent now you also now want to make some

bookings you need to book some

accommodation and you need to book some

travel like flights well those those

platforms right now use Dynamic

algorithms so wouldn't it be amazing if

you had an agent that could support you

in that process that would be good at

interacting with those different

websites uh getting all the right

information and then learning the timing

for when to do the right booking so it

maximizes uh you whatever your

objectives are let's say to to get you

the lowest price and that's a

specialized agent and then finally you

might have a specialized agent that's

really good at managing your wallet so

you might have different credit cards

different types of accounts even

different types of currencies right so

I'll throw out digital currencies

because that's an option you might have

an agent that specializes in dealing in

your wallet dealing with your wallet now

the the art of booking a trip would

involve you typically in a SAS model

going from one task with one bit of

software to another task with another

bit of software but the promise of AI is

that we can knit together these

different activities so that this

happens in a seamless way and these

three specialist agents in our framework

we call them a Collective this

Collective would be your travel

Collective and it would be able to knit

together those different tasks and those

different specialized agents in a way

that makes a really seamless experience

for the user that still as the user you

would interact with in a conversational

way very cool so yeah so agents act

autonomously and so that's what makes

them a little bit different from the

continuous kind of conversation that we

have with an llm so often llms are as

you said to power some of the

capabilities of an AI agent and that's

what makes it so easy to now suddenly

interface with them and I think that's

why they're so hot right now because we

now have we can use an llm as a natural

language interface and then the agent

can use information from the llm to go

out on say the web and come up with an

itinerary for you it could use its llm

weights I guess maybe to come up with

the itinerary and then subsequently like

you said the word scaffolding there I

like that so I'm guessing that's the

kind of thing that chain chain ml helps

with is providing things like that

scaffolding to allow you to have this

agent be working autonomously not just

while you're in that conversation with

it so if you ask the agent to every day

provide you with some inspiring quote

then that's what makes it different so

if if you go into chat GPT or Claud from

anthropic and you go in manually every

day and you ask for an inspirational

quote that isn't an agentic AI situation

because it's just reacting to you it's

just having that conversation with you

at that instant but if you can say the

natural language to an agent say I want

to have an inspiring quote every day and

then it is pushing to you say via email

or SMS or whatever you

prefer pushing that to you automatically

then it is an agent right and I maybe

one layer before that level of

automation if you think about the GPT

store where it's really just it's a

function you pull down an agent to

perform a function so it's not yet

automated it's not being you know

automatically delivered to you via an

app at that point those are agents too

but you have to do the work of going to

get it from the store and then

incorporating it into your workflow um

you know anthropic also now has a store

and I believe they call them projects at

this point but we're all sort of leaning

towards this idea of having a

marketplace where you have multiple

itinerary agents right so anybody can do

that now especially with the no code

Solutions anyone can go and build an

itinerary agent and if you're

particularly good at building one for

you know a family with small kids you

would put up your agent in the store now

where theoric the the product that

chainal Labs has built where theoric

really shines is how would you make a

decision about which agent to use so

you've got access to this GPT store or

the projects or hugging face or a

platform that has multiple different

agents how do you make a decision about

which is the right agent for me to use

in this Collective and what we've built

is a protocol that has some mechanisms

for you for that to happen in an

assisted way it's an automated way but I

let me for now call it an assisted way e