I explained to you the difference between a assistant rack agent and multi-agent systems.
Clients often ask me to clarify these terms.
So let me break it down for you.
Understanding these concepts will save you a lot of time when it comes to building, using, or selling
these AI systems.
I will be discussing it using practical examples.
Let's start with the basics.
Assistance.
So the best example of an AI assistant is ChatGPT, which is essentially a chatbot powered by large
language models that are trained on massive amounts of data.
They are designed to help you with specific tasks, mostly by answering your questions.
And they are trained on general knowledge, but they can't provide answers about your internal data
unless you fine tune them.
So they are great for answering questions and generating text, but are limited as their knowledge is
cut off if they are not connected to the internet or external data sources.
They are designed for direct interaction through voice or text.
Interactions with AI assistants are straightforward.
You provide input and they give you an output.
They rely solely on the llms training data, which is often outdated.
Now let's move on to RAC, which stands for Retrieval Augmented generation.
So this is a fancy way of saying let's give our access to extra knowledge.
So while AI systems like ChatGPT rely on the training data, RAC systems or by default connected to
external knowledge sources.
So this means they can pull in real time or context specific information that's not part of their original
training.
For example, a chatbot on a company's website.
Instead of only answering generic questions like ChatGPT, a rack system can access the company's frequently
asked questions.
Products or services descriptions, for example, stored in Google Docs or vector databases to make
it easy for large language models to fetch.
In short, rack enhances the AI assistants ability, like chatbots, to give you more relevant responses
by combining its language generation skills with external knowledge retrieval.
Next, agents to AI agents are like AI assistants or rack, but they don't just access external knowledge,
they also connect to external tools and know how to use them.
Thanks to the prompts you define so these systems are autonomous so they can take action on your behalf
without constant input.
For example, agents can integrate with tools like CRMs, Gmail and Google Calendar to automate tasks.
And they might draft an email, send it scheduled meetings, or even update your CRM.
While they can access external knowledge like RAC and have access to the internet or a company knowledge
base, they don't need to be called agents.
The key distinction is their ability to act.
So for example, they don't just draft an email.
They will draft it, send it and confirm it's done.
So agents are about action and taking the next step, not just answering questions.
Now let's talk about multi-agent systems.
Why Wide area assistance systems and agents each work independently.
Multi-agent systems combine multiple agents, each with a specific role, into a collaborative team.
Think of it as having a group of specialists who coordinate to handle complex, complex tasks.
So a supervisor agent acts as a manager, delegating tasks to the right agent.
So the calendar agent to schedule meetings.
Email agent to drafts and send emails.
And the research agent gathers information from the internet or other database or the project agent
which updates workflows or project data.
So imagine you message the supervisor agent, schedule a meeting with Sara.
You may have heard the agenda and find the latest AI trends for our discussion.
So the supervisor agent reads your request and splits it into tasks.
So the calendar agent schedules the meeting.
The email agent drafts and sends the agenda to Sara.
The research agent gathers the latest trends.
All these agents send the results back to the supervisor agent.
And the supervisor agent consolidates the results and reports back to you.
So basically each agent specializes in one task, but together they achieve more.
So tasks are done simultaneously and it saves a lot of time.
You can add more agents as needed.
And these systems can handle more dynamic and complex requests.
So in short, multi-agent systems are like a team of autonomous experts led by a supervisor.
The key difference between multi-agent systems and others we have discussed is this collaborative aspect.
Because AI assistants work alone.
Rack systems have access to external sources of data and enhanced knowledge retrieval.
Single agent can use tools but work independently.
And multi-agent systems bring multiple specialized agents together to solve problems that might be too
complex for a single AI agent.
So to conclude, the key differences are independence.
So agents and multi-agent systems are highly independent AI assistants need user commands.
RAC are not autonomous and they enhances large language models collaboration.
So multi-agent systems excel at teamwork.
Agents usually work alone and assistants and RAC don't collaborate at all.
Third knowledge integration.
RAC specializes in adding external information and others rely on pre-existing knowledge.
The next one tax complexity.
Multi-agent systems handle complex, more sophisticated distributed tasks.
And the last one adaptability.
Multi-agent systems and agents can adapt to changes a assistance just follow fixed rules.
And RAC retrieves new info but doesn't learn or evolve.