All right, check this out.
We're going deep on this paper called Magentic One,
a generalist multi-agent system for solving complex tasks.
Oh yeah, I saw that one.
And honestly, the way they're approaching AI
in this is totally different.
Yeah, it kind of flips the script
on how we usually think about AI.
How so?
Well, we usually think of AI as this one big brain
trying to do it all right.
Right, right.
But here, they've built an AI system
that acts more like a team,
like a specialized A team of AI agents all working together.
An AI A team.
Yeah.
That's kind of awesome.
Yeah.
So instead of one giant AI,
we have a bunch of smaller AIs,
each with their own special skill set.
Exactly.
It's like a well-coordinated squad.
OK, I like where this is going.
You've got a web surfer agent that can rise the internet,
a file surfer to handle documents,
a coder that writes code, and a computer terminal
to run that code.
Hold on, there's an AI that can write code
that's both cool and kind of scary at the same time.
Yeah, right.
But why build it this way?
What's the advantage of having a team instead of just one
super AI?
Well, think about it this way.
OK.
Even the smartest person in the world
can't be an expert at everything, right?
True.
But a team of specialists, each focusing on what they do best,
can achieve some pretty amazing things.
OK, that makes sense.
Yeah.
So by breaking down these complex tasks
into smaller, more manageable chunks,
each agent can just focus on their area of expertise.
Right.
It's like a relay race, where each runner
is optimized for their specific leg of the race.
Precisely.
And just like a relay team needs someone
to coordinate the handoffs, this AIA team
has a lead agent called the orchestrator.
The orchestrator.
Yeah.
So that's like the brains of the operation,
figuring out the best strategy, and then assigning tasks
to the right agents.
It's like the coach, making sure everyone knows their role
and plays together effectively.
Gotcha, so the orchestrator is the coach.
But how does it keep track of everything that's going on?
We're talking about a lot of moving parts here.
That's where it gets really interesting.
OK, lay it on me.
The orchestrator uses these things called ledgers.
Ledgers.
Yeah, ledgers to manage the whole process.
The task ledger is like the mission briefing.
It lays out the overall plan, the known facts.
Even educated guesses the AIA team might use.
OK, wait, hold on.
Yeah.
Educated guesses.
Is the AI just making things up?
Not quite.
OK.
Imagine a detective trying to solve a case.
Even with limited information, they
can use their experience and intuition
to develop a theory right.
Yeah.
That's kind of what's happening here.
The AI is making inferences based on the information it has,
even if it's not 100% certain.
So it's using its AI intuition?
Yeah, you could say that.
To fill in the gaps and guide its investigation?
That's pretty clever.
But what about the other ledger, the progress ledger?
Ah, yes, the progress ledger.
What's that do?
That's all about keeping things moving in the right direction.
The progress ledger helps the orchestrator
monitor what's been done, what needs to happen next,
and if anyone is stuck in a rut.
So it's like the orchestrator is constantly checking in,
asking, OK, team, are we making progress?
Is anyone hitting a wall?
Do we need to switch gears?
That seems crucial, especially with complex tasks.
Absolutely.
And to really test this A team approach,
the researchers put Magentic 1 through a series of challenges.
Like what?
They're called benchmarks.
Benchmarks?
Think of them like obstacle courses for AI.
Oh, OK, so they're testing how well
Magentic 1 can handle real world problems.
Right.
What kind of tasks are we talking about here?
Give me an example.
One task from the GAA benchmark was,
of the cities within the United States
where US presidents were born, which two
are the farthest apart from the western most to the eastern
most going east, giving the city names only.
Give them to me in alphabetical order
in a comma separated list.
Whoa, you and I had to read that twice.
It's a mouthful.
So to solve this, the AI would have
to first find all the US presidents and their birth
cities, then figure out the coordinates of those cities,
calculate the distances between them,
and then identify the two farthest apart going eastward,
and list them alphabetically.
You got it.
That's a lot of steps.
And it's not just about finding information.
It's about processing it, making calculations,
and presenting the answer in a specific way.
It's a real challenge.
How did Magentic 1 do?
Pretty well, actually.
It was statistically competitive with other top performing
AI systems.
Wow.
Some of which were specifically designed for just
one of these tasks.
So this generalist approach is really working.
It seems to be.
What's remarkable is that Magentic 1
held its own against these specialists.
So the A team structure is showing real potential
for tackling a wide range of challenges, not just
one specific thing.
Exactly.
But I'm guessing it's not all sunshine and roses.
Of course not.
Every system has its limitations, right?
What are some of the drawbacks the researchers highlighted?
Well, one big one is the potential cost.
OK.
These powerful AI models require a lot of computing power
to run, which can get expensive.
Right, it makes sense.
Plus the coder agent, while promising,
is still pretty basic.
Yeah.
It can write simple programs, but it's nowhere near as
capable as a human programmer.
OK, so there's definitely room for improvement.
For sure.
But even with those limitations,
this idea of a team of AI agents working together
is pretty mind blowing.
Oh, absolutely.
It's like they've created a digital brain trust.
Did the researchers delve into any specific areas
where this team-based approach really shined?
They did.
And this is where it gets really interesting.
They noticed that Magentic 1 was actually
better at handling the harder tasks.
Really?
Yeah, the more steps involved, the more reasoning
and planning required, the better this A team performed.
Interesting.
It kind of goes against intuition, right?
You'd think a specialized AI would be better
at a specific task.
You'd think so, right?
Yeah.
But Magentic 1 even outperformed some specialized
AIs on certain tasks.
Wow.
So the more complex the challenge,
the more this A team structure shines.
Yeah, that's what the data suggests.
Why would that be?
It could be because of how multi-agent systems are
structured.
OK.
Having these specialists and a central coordinator,
like the orchestrator, it really helps
break down these huge problems into smaller pieces.
Yeah, divide and conquer, right?
Exactly.
Makes those complex problems a little more manageable.
But surely they also found some areas
where the A team didn't quite click.
Oh, absolutely.
What did their error analysis reveal?
Well, they dug deep into the logs,
actually seeing how Magentic 1 tackled these tasks.
And they found some opportunities for growth.
Let's just say that.
All right, spill the tea.
What were some of the common hiccups?
One frequent issue is what they called
persistent inefficient actions.
What's that?
Sometimes the agents would get stuck in a loop,
repeating the same action, even though it clearly
wasn't working.
Oh, so like the web surfer keeps
searching for the same term, even though it's finding nothing?
Exactly.
Or the file surfer keeps opening the wrong document.
Yeah, I've been there.
So a key area for improvement is teaching the agents
to recognize when their strategy is failing
and to try something else.
So building in more flexibility and adaptability
make those agents a little more resourceful.
Exactly.
What else did they find?
Another issue was a lack of thorough verification.
What do you mean?
Sometimes the agents would jump to conclusions
without double checking their work.
Oh, overconfident, huh?
Like a student turning in an essay without proofreading.
That's a great analogy.
And that can lead to mistakes, especially when accuracy
is critical.
Yeah, makes sense.
They need to learn to double check their work.
Like a healthy dose of skepticism is needed, I guess.
You could say that.
What other opportunities for growth do they uncover?
Magentic ones sometimes struggled with navigation.
Navigation, like where?
Social media on the web.
Getting lost in the vastness of the internet.
You could say that the web surfer would click around
aimlessly or take a long, inefficient route to reach
its goal.
It's funny how we take navigating websites for granted,
but it's actually quite challenging for AI, right?
It's like a whole different way of seeing the digital world.
Absolutely.
It highlights the importance of well-designed intuitive
interfaces.
Something we understand easily can be very confusing for AI.
Speaking of confusion, did the researchers
mention any risks associated with letting
these AI agents loose on our computers?
They did.
Because these agents can interact with the real world
through the internet and files.
They could potentially take actions
that have unintended consequences.
That sounds a little dicey.
It can be.
Like giving your very smart, but perhaps naive AI assistant
access to your bank account.
Yeah, something like that.
Makes.
There were instances where the agents
tried to do things that, if they had permission,
could have caused problems.
Like what?
Like attempting to reset a website password
after repeatedly failing to log in.
Oh, a bit too eager to solve the problem
without considering the bigger picture.
Precisely.
It highlights the need for safeguards.
We need to teach these agents to recognize actions
that are irreversible or could have major consequences.
They need to know when to pause and ask for human guidance.
Like a safety switch.
Yeah.
Or like, whoa, their partner mechanism
to prevent the AI from going rogue.
Exactly.
Gotcha.
It's about finding the right balance.
Yeah.
Give them the freedom to learn and explore,
but ensure they operate safely and responsibly.
Makes sense.
I bet those safety measures become even more critical
as these AI teams get more sophisticated.
Oh, for sure.
But let's switch gears for a second.
OK.
We talked about the orchestrator, the challenges.
But how does this whole system actually work?
What's going on under the hood?
OK, let's start with the task ledger.
Right.
Think of it as the team's shared brain,
a storehouse of knowledge and assumptions
they need to solve the problem.
More than just a to-do list, then.
Yeah.
It's like a dynamic knowledge base
that grows as the AI team gathers information.
Exactly.
It has four key elements.
OK.
First are the givens.
Givens.
The facts stated in the task description.
Oh.
Of the cities within the United States
where US presidents were born, those are the givens
the AI team starts with.
Gotcha.
Then there are the lookups.
The things the AI needs to find out,
usually through web searches or databases.
So in our example, that would be the list of US presidents
and their birth cities.
Exactly.
OK.
Next are the derivations.
Derivations.
Facts the AI figures out based on the givens and lookups,
often through logic or calculations.
OK.
So figuring out the coordinates of those birth cities,
the distances, and which two are farthest apart going
eastward, those would be the derivations.
You got it.
And finally, there are the educated guesses
we talked about earlier.
Right.
Those AI intuitions, those hunches.
Yeah.
But how does the orchestrator actually use all this information
to guide the team?
That's where the progress ledger comes in.
A progress ledger.
Yeah.
It's the mission control dashboard.
Oh, OK.
Showing the orchestrator the status of the task
and guiding its next move.
So the task ledger is the big picture.
And the progress ledger is the tactical step-by-step guide.
Exactly.
The progress ledger helps the orchestrator
answer some key questions.
Like what?
Well, the most important one is the task complete.
Because if it is, everyone can go home early.
Right.
But if not, the orchestrator needs
to figure out what to do next.
OK.
That leads to the next question.
Is the team making progress?
Because if they're spinning their wheels,
it's time for a new strategy.
Exactly.
And that's where the progress ledger is really useful.
How so?
It can spot these unproductive loops.
Oh, right.
Like the web surfer stuck searching for the same term
over and over.
Precisely.
The orchestrator can then intervene,
redirecting the team, or changing the plan.
So it's like, OK team, we've hit a wall.
Let's try a different approach.
Exactly.
That flexibility is crucial for solving complex tasks
where there's no clear solution path.
Yeah.
That makes a lot of sense.
Yeah.
But this all seems incredibly intricate.
How does the orchestrator actually make these decisions?
Does it have like some super intelligence
that lets it grasp the nuances of the task
in each agent's capabilities?
Well, this is where the power of large language models comes in.
OK.
The orchestrator, just like the other agents, is powered by one.
So it's not just following like pre-programmed rules.
Right.
It's using its understanding of language
and its ability to reason to make decisions.
Exactly.
And here's the clever part.
What's that?
The researchers didn't specifically
program the orchestrator to know how to manage a team
or detect unproductive loops.
Hold on.
So how did it learn to do all those things?
Through a technique called prompt engineering.
Prompt engineering.
Yeah.
It's the art of crafting the right prompts
to guide the language model's behavior.
So giving the orchestrator like guidelines or instructions
to help it understand its role and how
to work with the other agents.
Exactly.
With careful prompting, they were
able to turn this language model into an effective leader,
coordinating a team of AI agents.
Amazing what you can achieve with the right prompts, huh?
But back to the progress ledger for a second.
OK.
We've covered three questions.
Is the task complete?
Is the team making progress?
And are there any unproductive loops?
Right.
What about the last two?
Well, after assessing the situation,
the orchestrator needs to decide which agent to activate next.
The fourth question is, which agent should speak next?
So the orchestrator is like a teacher calling on a student.
OK, Web Surfer, what have you found?
Exactly.
And the final question is, what instruction or question
should be asked of this agent?
So it's not just choosing the right agent.
It's about framing the task in a way to help them succeed.
You got it.
That's critical.
The orchestrator needs to understand each agent's
capabilities and communicate effectively
to get the results it needs.
It's like a manager who knows how
to get the best out of their team
by playing to their strengths and giving clear directions.
Precisely.
Understanding those individual talents and fostering
collaboration is key.
That's what makes Magentic One so innovative.
It's designed for teamwork, with each agent contributing
their expertise to achieve a common goal.
It's a whole new way of thinking about AI.
Not about one super intelligence that does it all,
but a system that learns and adapts by working together,
just like us.
I know.
It's really cool.
This research is incredibly exciting.
It is.
It really opens up a world of possibilities
for how we can use AI to solve complex problems.
Absolutely.
And really enhance our own capabilities.
Yeah, for sure.
It's definitely a fascinating journey.
We've covered a lot in this part of our deep dive.
Yeah.
The inner workings of Magentic One,
the roles of those ledgers, the importance
of communication and coordination.
Right.
But there's one big piece we haven't discussed yet.
We've spent the last two parts digging into the brains
of this AI 18.
Yeah, the architecture.
But we haven't met the actual agents yet.
Right, the ones doing the hands-on work.
The ones actually getting their hands dirty.
Exactly.
So let's introduce the stars of the show.
Starting with the web server, what does that one do?
It's all about navigating the internet,
like a super-powered research assistant.
OK, so it can search for information, follow links,
even interact with websites.
That's a lot of power.
It is.
But the web can be a messy place, even for humans.
Oh, yeah.
How does the web server even understand
what it's looking at?
It uses a technique called set of marks
prompting to interact with those web pages.
Set of marks prompting.
Yeah.
That sounds very technical.
It is a bit of a mouthful.
Bring it down for me.
Imagine you're guiding someone through a website
over the phone.
OK.
You might say, click the Submit button
in the top right corner.
You're giving them landmarks to find the right thing.
OK, so you're providing specific visual cues.
Exactly.
That's what set of marks prompting does.
Gotcha.
The web server takes a screenshot of the web page
and then uses AI to identify and label everything important.
Buttons, links, text boxes, you name it.
So it creates a digital map of the web page,
kind of highlighting the points of interest.
Precisely.
With this map, it can then follow the orchestrator's
instructions with pinpoint accuracy.
So it's like giving the web server x-ray vision
to see the structure of a web page.
Yeah, I like it.
But it's not just clicking and typing, right?
Yeah.
Can it actually understand the content of the page?
It can.
The web server can extract text, summarize information,
and even answer questions about what it reads.
Wow.
It's not just browsing.
It's comprehending.
OK, that is impressive.
It is.
So we have the web server exploring
the wilds of the internet.
Yeah.
What about the file server?
Yes, the file server.
What does that one do?
Is it just the web server's cousin who prefers libraries?
In a way, yes.
While the web server tackles the open web,
the file server is more of a digital librarian,
navigating the structured world of files and folders.
So it's the go-to agent for all things, documents,
spreadsheets, presentations, all that stuff.
Exactly.
The file server can open and read a wide variety of file types,
PDFs, office documents, images, even audio and video files.
Wait, hold on.
Audio and video.
I thought MagentaCon was limited to text-based stuff.
It can't actually watch or listen like we do.
OK.
Instead, it uses speech to text for audio,
converting those spoken words into text for analysis.
Oh.
For videos, it sometimes uses captions or transcripts
if those are available.
So it's like a super-powered note taker listening to lectures
or watching documentaries and summarizing the key points.
A great analogy.
And for text-based files like PDFs,
they can extract text, search for keywords,
and even answer questions about the content.
It's like a super-charged search engine
for your own digital archives.
OK.
OK, let's move on to the agent that really blew my mind,
the coder.
All right, the coder.
How does this AI actually stack up against human programmers?
Honestly, it's still pretty basic compared
to a seasoned human programmer.
Yeah.
It can write simple Python programs,
but it's not ready to build complex software applications
just yet.
So more of a junior developer learning the ropes
and handling those smaller tasks.
That's a good way to put it.
But even at this early stage, the coder
is incredibly useful.
How so?
It can automate repetitive tasks,
write scripts to manipulate data,
even generate code to interact with other systems.
So while it might not be building the next big social media
platform, it can still save human programmers
a ton of time by handling those more mundane tasks.
Precisely.
And like all the agents in Magentic One,
the coder is always learning and improving.
As these large language models evolve,
we can expect its coding abilities
to become even more sophisticated.
So one day, it might actually give those human programmers
a run for their money.
Maybe, yeah.
Now, the last agent is the computer terminal.
Yes, the computer terminal.
And I have to admit, I'm having a little trouble
picturing what it does.
Think of it as the bridge between the code and the real world.
OK.
It's the agent that actually runs the code written
by the coder.
So it's the muscle behind the brains.
You could say that.
The one that takes those digital instructions
and turns them into action.
Exactly.
The computer terminal has access to the operating system,
where it can run code, execute commands,
even install new software libraries.
It's like giving the AIA team direct control
over the computer, allowing them to interact
with other programs.
Precisely.
And that opens up a lot of possibilities,
downloading files, installing software, running simulations,
even controlling hardware devices.
Wow.
It's the Swiss Army knife of agents.
It really is.
It's amazing how all these specialized agents work together,
each bringing their own skills to the table.
It's pretty remarkable.
It's like they've redefined teamwork.
It's a whole new way of thinking about AI.
It's not about building one's super intelligence,
but a system that learns and adapts through collaboration.
Just like us.
And it's just the beginning.
This field is evolving so quickly,
I can't wait to see what other innovative multi-agent systems
emerge.
Me too.
It's a fascinating time to be following AI.
It is.
This technology has the potential to revolutionize
so many aspects of our lives.
Well, this has been an incredible deep dive into Magentik 1.
It has been fun.
We've covered the architecture, the agents, the challenges,
and the potential of this groundbreaking system.
Absolutely.
We've gone through it all.
We hope this has given you a better understanding
of multi-agent AI.
Yeah.
It's a rapidly evolving field full of possibilities.
And as always, stay curious, keep learning,
and join us next time for another deep
dive into the world of cutting-edge technology.