All right, so today we're diving deep into
practical considerations for a Gentic LLM system.
Sounds intriguing.
It is, it was written by Chris Cyford and Vyshak Bell
Ah, yeah.
from the University of Edinburgh.
And this paper, well, it tackles the whole idea
of using LLMs, you know, large language modders,
as the brains behind autonomous agents.
Okay, so not just chatbots,
like things that can actually go out and do things.
Yeah, exactly, like real action, not just words.
Gotcha, that's a pretty hot topic right now.
What's unique about this paper?
Well, that's what's really cool about it.
It bridges the gap between what academic researchers
are all excited about,
and then what's actually happening out there in the industry.
Okay, so are we talking like,
are the academics thinking of, you know,
robot butlers powered by these LLMs
while companies are just trying to like,
I don't know, automate their customer service calls?
Maybe not that extreme, but yeah,
you're definitely picking up in the core difference there.
Okay.
So academics, they're looking at LLMs from a very,
I guess you could say theoretical perspective.
Thinking about things like, you know, beliefs,
reasoning, planning, all those kind of high level things
we associate with, you know, intelligent agents.
So they're trying to figure out if an LLM could like,
in theory, become this truly independent agent,
you know, one that can make its own decisions
and actually act on them in the real world.
Yeah, fascinating stuff.
But on the other side, you have the industry folks
who are much more focused on, well, how can we use this?
How can we apply it?
How can this make us money, I assume.
So they're asking more like, can we use LLMs
to build systems that can actually solve specific problems?
Like planning a trip, managing a project, you know,
very concrete things.
So less existential pondering,
more about getting stuff done.
Exactly, yeah.
They view the LLM as, okay, this is the core reasoning engine.
Okay.
And then they're gonna add on these different modules
for things like how to plan, maybe some memory.
And then how it can interface with different tools.
So this lets the LLM act as this agent
in a more, I guess you could say, concrete way.
Okay, I'm starting to see the distinction here.
And you know what, to make it a little bit more tangible,
the paper uses this example of a pescatarian meal assistant.
Oh, interesting.
What is that all about?
That's a really great way to kind of illustrate
the challenges and also the potential of these LLM agents.
So imagine an LLM that's helping someone who's pescatarian.
So they only eat fish and seafood to plan out their meals.
So this agent, it has to be able to search recipes,
maybe consider allergies or dietary restrictions,
figure out if ingredients are even available,
maybe even order those groceries for the person.
Yeah, that's definitely way more complex
than just like pulling up a recipe on the internet.
Like it needs to understand what the user wants,
then adapt to changing situations,
maybe some ingredients aren't available,
and then interact with all these different tools
to actually get that done.
And that's where the paper really gets
into the nitty gritty.
It goes into all these very specific considerations
for building these kinds of agents.
And it starts with what might seem like a simple thing,
but it's surprisingly tricky and that's planning.
Wait, tricky.
I thought LLMs were supposed to be amazing at planning.
If they can write code, compose poetry,
surely they can handle a meal plan, right?
You would think, right.
But research actually suggests that LLMs
aren't naturally gifted at this kind of step-by-step logic
of planning.
Interesting.
So they're great at understanding and generating language,
but this logic seems to be a weak spot.
So our little pescatarian meal assistant
might be awesome at finding the most delicious fish recipes.
But then when it comes to actually figuring out
the steps to buy the ingredients, cook the meal,
it kind of falls apart.
Yeah, that's the problem.
And so the paper suggests that instead of trying to force
these LLMs to become master planners,
we should focus on this idea of task decomposition.
Cask decomposition.
OK, is that AI therapy?
We're going to help the LLM break down
its problems into these manageable chunks.
It's a really good analogy.
So instead of asking the LLM to plan
an entire week's worth of meals,
you break it down into smaller steps.
So find recipes, create the shopping list,
figure out when to cook things.
And this makes each task much simpler.
And it plays to the LLMs' strengths.
So it's about designing the task to fit the LLM, not
the other way around.
Exactly, yeah.
And the authors, they argue that this is actually
way more effective than trying to use
a less powerful LLM for this complex planning.
So it's like starting with a high performance engine
and adjusting the workload rather than trying to soup up
like a weaker engine.
Exactly.
That makes sense.
But OK, planning is just one piece of the puzzle.
What about memory?
How do we ensure that our little LLM agent actually
remembers these crucial pieces of information,
like dietary restrictions or favorite recipes, all that stuff?
Yeah, great question.
And that's where something called retrieval augmented
generation comes in.
OK.
Or REG, for short.
REG.
And it's basically a technique that
gives the LLM access to these external information sources.
So like databases, stuff like that?
Yeah, databases, knowledge graphs that
are relevant to the task at hand.
So instead of relying only on what
it learned during its initial training phase,
it can consult a cheat sheet.
Right.
Like with curated information.
You got it.
OK.
So for our pescatarian meal assistant,
this could be, I don't know, a database of pescatarian recipes,
information about allergies.
Makes sense.
Or even feedback from the user on past meals,
like what they liked, what they didn't like.
So this seems like a really smart way
to prevent those hallucinations we always hear about with LLMs,
where they just like spout out completely made up stuff.
Yeah, exactly.
Or inaccurate information.
Our RAG helps ground the LLM in reliable data,
so it's less likely to go off the rails.
And it also keeps that information up to date,
which is super important, especially for something
like meal planning, right?
So trends and ingredients are always changing.
Right.
So no more AI recommending outdated recipes
or suggesting you use ingredients
that you can't find anywhere.
Yeah, exactly.
That's great.
It's like giving your LLM agent a subscription
to all the latest food blogs and culinary magazines.
Yeah.
Keeps its knowledge fresh and relevant.
I like that.
Yeah.
But what about remembering things that aren't necessarily
in a database, so like specific user preferences
or feedback from past interactions?
Do we need to give the LLM a digital diary
to keep track of all that?
Right.
So that brings us to the concept of long term memory
for these LLM agents.
OK, interesting.
It's about storing key takeaways from those past interactions,
and then they can use those to inform future decisions.
So things like allergies, dislikes, maybe even just
like a preferred cooking style.
It's all about building that personalized experience.
Exactly.
And the paper, it suggests focusing
on storing information that is independent,
so it's not tied to a specific event.
But it kind of reflects this general preference or constraint.
It also needs to be consistently relevant to the agent's
purpose and applicable over the long term.
So instead of remembering, the user
didn't like the salmon recipe on Tuesday,
it'd be more like the user disliked salmon.
Exactly.
And that's relevant to any meal planning,
not just a specific instance.
That's a great summary.
It's like you're building a knowledge base of the user's
core needs and values, which the LLM can then
draw on to make those more personalized and accurate
decisions.
This is fascinating.
But so far, we've mostly been talking about these LLMs just
interacting with text and data.
But how do they actually do things in the real world?
Can they really interact with our physical environment?
Right.
And that's where tools come in.
Tools are essentially extensions that give the LLMs
the ability to go beyond text and actually interact
with other systems or even physical devices.
So we're giving these LLMs superpowers,
like to search the web, send emails,
control our smart appliances.
Exactly.
Yeah.
So for our meal assistant, a tool
might be something that lets it search for recipes online,
maybe filter by specific ingredients or dietary
restrictions.
Another tool could be something that
lets it order groceries.
Wow.
Hold on.
So the LLM could actually make purchases on behalf of the user.
Yeah.
That's taking this to a whole new level.
It is.
And it really highlights the, I guess you could say,
the vast potential of these LLM agents.
Yeah.
But as we develop these more complex systems,
the number of tools is just going to explode.
Right.
So we're building this AI Swiss Army knife with a tool
for every possible situation.
And that brings us to the very crucial issue
of tool management.
Right, because we don't want the AI
to get lost in its own digital toolbox.
Yes, exactly.
Yeah.
And so the paper, it stresses the importance
of clear documentation.
Organization, almost like you're creating an instruction
manual for each tool.
Right.
So we're not just throwing a bunch of tools at the AI
and just hoping for the best.
Right.
We're carefully curating them, documenting them,
just like we would with any complex software system.
Exactly.
It's all about setting the LLM agent up for success
by making sure it has the right tools.
Right.
And that it knows how to use them effectively.
OK, so we've got planning, memory, tools.
What other considerations are there
when we're building these LLM agents?
Well, another really critical one is control flow.
OK.
And that's basically the decision-making process
of the LLM agent.
So it's like the AI's internal traffic controller.
That's a good analogy.
OK, it's deciding which steps to take,
when to use which tools, and when to deliver a result.
Exactly, yeah.
So effective control flow requires
that the LLM agent understands all of its options.
OK.
The tools that it has, the personas that it can embody,
and the possible actions it can take.
And then based on that understanding,
it can make decisions about how to proceed.
Exactly.
Just like a human would in a similar situation.
Exactly.
So back to our meal assistant.
OK.
The control flow would determine how it responds
if a user says, plan my meals for next week.
OK.
So it might consult its memory about, OK,
what's this person's diet?
OK.
Maybe use a planning module to create a kind of draft schedule.
Right.
And then it could use a tool to maybe check the user's calendar
for any upcoming events.
It's like a well-choreographed dance
where each step flows smoothly into the next.
Yeah, exactly.
That's really cool.
And that smooth flow is really essential for creating
a seamless and efficient user experience.
But what happens when things don't go according to plan?
Right.
Because let's be honest, with all this complexity,
things are bound to go wrong at some point.
LLMs, they're not perfect.
Right.
Neither are the tools and systems they're interacting with.
Exactly.
And that brings us to another critical consideration.
Error handling.
OK.
Error handling.
So are we giving the AI like a panic button?
Not quite.
It's more about anticipating potential issues
and then coming up with strategies to address them.
OK.
So one approach is to simply retry a failed operation.
OK.
But maybe with a slightly different kind of seed,
which can nudge the LLM toward a different output.
So we're giving it a second chance,
but with a little tweak to its thinking.
Yeah, exactly.
Another approach is to give the LLM more context about the error.
OK.
So maybe like a specific error message
or some additional information that might help it
understand what went wrong.
So it's like we're giving the AI a bug report
so it can try to debug itself.
That's a good way to put it, yeah.
OK.
And if the LLM is still stumped,
we can even bring in another LLM,
maybe one that specializes in debugging or error correction.
Oh, so it's like calling in the AI cavalry
to rescue a mission that's gone off the rails.
Exactly.
I like it.
And this kind of multi-layered approach to error handling
is super crucial for building those really robust and reliable
LLM agents that can function in the real world.
Because in the real world, unexpected things happen.
This is all incredibly fascinating.
And it's clear that building these effective LLM agents
requires a lot of thought and planning.
But I'm also struck by how much we're still figuring out
about these systems.
It's like we're explorers charting a new frontier,
constantly discovering new possibilities and new challenges.
Exactly.
And that's what makes it so exciting.
It is exciting.
Speaking of complexity, I'm curious
about how these different LLM agents handle
the sheer volume of information that they have to process.
It seems like it would be so easy for them to just get
overwhelmed or lose track of important details.
Right.
And that brings us to this crucial issue of context
management.
Not text management.
So just like humans, LLMs can get bogged down by too much
information.
Yeah, I feel that.
And that can lead to errors, inefficiencies,
and even nonsensical outputs.
So we need to make sure we're not overloading these AIs
with irrelevant data.
Right.
Or expecting them to remember every single thing
from every bass interaction.
Exactly, yeah.
So the paper highlights the importance
of trimming all that extraneous context,
summarizing past conversations.
And that can prevent the LLM from getting
lost in just this sea of information.
It's like decluttering the AI's workspace.
Make sure it has the essential information,
but not overwhelmed with all the unnecessary details.
Yeah, it's like the difference between having a tidy desk
with all your important documents organized
versus just having a chaotic pile of papers everywhere.
Right, exactly.
OK, so effective context management
is crucial for making sure the LLM agent can actually
focus on the task at hand.
Right.
And make informed decisions.
Yeah.
And deliver those accurate results.
Exactly.
This is all incredibly insightful.
But I'm also curious about how we actually
evaluate the performance of these LLM agents.
Is there some sort of AI performance review
where we sit down and assess their skills
and give them feedback?
It's not quite as formal as that.
OK.
But evaluation is definitely a critical aspect
of this development process.
And the paper emphasizes the need
to move beyond those traditional metrics,
like just accuracy.
OK.
And focus more on real world performance.
So instead of just checking if the AI got the right answer,
we're actually looking at how it performs
in a real world scenario.
Right.
With all the complexities and uncertainties.
Exactly.
So for our meal assistant, we might track things like,
how many steps did it take to create that meal plan?
OK.
What tools did it use?
OK.
How long did it take?
Even the cost of the ingredients that it selected.
So it's not just about if it succeeds.
Right.
It's about how it succeeds.
Exactly.
We're looking at its process, its efficiency,
its ability to adapt to unexpected situations.
It's not just about creating the perfect meal plan
in a lab setting.
It's about testing its ability to function in the real world
where things don't always go according to plan.
Exactly.
It's like giving the AI an internship in a real kitchen
and seeing how it performs under pressure.
That's a great way to put it.
And it sounds like this evaluation
requires a multifaceted approach.
Yeah.
That goes beyond those simple metrics.
Right.
And really gets into the nuances
of that real world performance.
And that brings us to another key consideration
that the paper highlights.
Model size.
OK.
Now, I'm not talking about how much physical space
the AI takes up.
Right.
But the complexity and the scale of the LLM itself.
Yeah.
Model size is a major factor in determining its capabilities,
its cost, its speed.
So bigger models are more powerful,
but they're also slower and more expensive to run.
Exactly.
Yeah.
OK.
And when you're designing these LLM agents,
it can be tempting to start with a smaller, less
expensive model.
Right.
It seems like the logical choice.
Why splurge on this giant AI brain
if a smaller one can do the job?
That's what you might think.
But the paper actually suggests the opposite.
Wait.
Seriously.
Start with the biggest, baddest AI model available.
That's the recommendation.
And it makes sense when you consider that starting
with a powerful model gives you that really high performance
baseline.
So it's like starting with the top of the line computer
and then figuring out which components you can maybe
downgrade without sacrificing performance.
Yeah, exactly.
So you can test different configurations,
experiment with smaller models for maybe specific tasks,
and really fine tune the system to optimize both performance
and cost.
So it's not about being cheap.
Right.
It's about being strategic.
Exactly.
And this actually brings up another point that's often
overlooked when we're building these systems.
And that's the need for integration
with traditional engineering.
OK, so now we're really blurring the lines here
between computer science and AI.
That's the beauty of this field.
It's all about finding the best way
to combine human ingenuity with these AI capabilities.
So how do those traditional engineering practices
fit into the world of LLM agents?
Well, remember how we talked about the inherent randomness
of LLMs?
They can be a little unpredictable.
Yeah, it's like they have a mind of their own,
which can be amazing, but also a little unnerving sometimes.
Yeah, exactly.
And in certain situations, we need things to be predictable.
We need them to be reliable.
OK.
And that's where traditional engineering techniques come in.
So we can leverage those deterministic algorithms,
well-established software development practices
to provide that solid foundation for the LLM agent.
Exactly.
It's about offloading certain tasks or processes
to components that we know are going to behave consistently
and unpredictably.
It's like building guardrails for the AI
to ensure that those critical steps are always
executed correctly, even if the LLM gets a little creative
with its decision making.
Yeah, it's about finding that right balance between AI autonomy
and engineered reliability.
So give us some examples.
How does this integration actually work in practice?
Well, think about context management.
We can use traditional engineering
to automatically trim and summarize
information between those LLM calls.
So the AI isn't overloaded with all that irrelevant data.
It's like having an automated cleanup crew for the AI's
mental workspace, just keeping things tidy and efficient.
Exactly.
Another example is tool management.
So we can group similar tools together
into these tool sets, creating this more organized
and streamlined system.
So instead of having 100 different tools just scattered
about, we create these specialized kits
for specific purposes.
Exactly.
It's like having a toolbox with clearly labeled compartments
for all the different types of tools.
So much easier to find the right tool for the job.
Exactly.
And we can even use traditional engineering
to set up things like callbacks or triggers
for certain events.
Callbacks?
We're talking about the AI returning our phone calls now.
Not quite.
It's more like setting up automated responses
to specific situations.
So for example, we might have a callback that automatically
generates summary of a conversation when the LLM
agents switches personas.
So instead of relying on the AI to remember everything,
we have a system in place that provides the relevant information
automatically.
Exactly.
It's all about creating that more efficient and robust
system by combining really the best of both worlds, AI
and traditional engineering.
I like it.
It's a beautiful partnership.
It is.
And this integration also allows us
to introduce the concept of short circuiting.
OK, that sounds interesting.
Is that like an AI safety feature,
like a circuit breaker or something?
It's more like an efficiency booster.
OK.
So sometimes the answer to a query is obvious,
and the LLM agent doesn't need to go through this whole complex
process to figure it out.
So instead of overthinking it, the AI
can just deliver a simple, straightforward answer.
Exactly.
Makes sense.
Why waste the time and resources?
Exactly.
And that's a great example of how we can use traditional
engineering to enhance the efficiency and performance
of these LLM agents.
It's like giving the AI permission
to use its common sense.
Avoid over-complicating things.
You got it.
And speaking of avoiding complications,
there's another crucial consideration
for building these real world LLM agents,
and that is understanding their limitations.
Right.
Because as amazing as these systems are, they're not magic.
They're things they can't do, at least not yet.
Right.
And the paper acknowledges this.
It highlights a few key areas where more research
and development are needed.
So what are those?
What are the biggest hurdles facing these LLM agents
right now?
Well, one area that's still pretty under explore
is this idea of human in the loop evaluation.
Humie in the loop.
So instead of just running automated tests,
we actually get real people involved in assessing
the AI's performance.
Exactly.
It's about understanding how humans actually interact
with these LLM agents, how they perceive their responses,
and how we can improve that overall user experience.
So it's like conducting those user studies or focus groups
to get feedback on the AI's performance from a human perspective.
Exactly.
Because ultimately, these systems are designed to serve humans.
So we really need to understand how people actually
use them and what their needs are.
Makes sense.
So human in the loop evaluation is all
about bridging that gap between AI and human interaction,
ensuring that these systems are truly user friendly and effective
in those real world scenarios.
You got it.
And then there's the question of model maintenance.
Model maintenance.
So are we talking about giving these AI's
like regular oil changes and tune-ups?
It's not quite that simple.
It's more about understanding how to adapt and update
these LLM agents as the world changes,
as new information emerges, as user needs evolve.
So it's like ongoing software updates, but for AI's.
That's a good analogy, because unlike traditional software,
LLMs are constantly learning and evolving.
OK.
So we need to develop these strategies for keeping
them current and relevant.
So it's like these AI systems need a continuous education
program to keep up with the pace of change.
Exactly.
And that's a challenge we need to address
if we want to build LLM agents that can truly
stand the test of time.
I can see how that would be a major undertaking,
especially as these models become even more complex
and integrated into our lives.
It is.
And it's an area where further research and innovation
are definitely needed.
But I believe it's a challenge worth tackling,
because the potential benefits of these LLM agents
are just enormous.
Yeah.
The possibilities are mind-boggling.
Imagine a future where these systems are helping us
with everything from managing our daily tasks
to tackling some of the world's biggest problems.
Exactly.
It's an exciting prospect.
And it's one that I'm personally very optimistic about.
I share your optimism.
I can't wait to see what the future holds for these LLM
agents.
But for now, let's take a little deeper
look at some of the more practical considerations
for building these incredible systems.
OK, sounds good.
And another big consideration is cost.
Oh, right.
All this AI power doesn't come cheap, does it?
No, it definitely doesn't.
We talked about model size.
But there's so many other factors
that can affect the cost of actually developing and deploying
these LL agents.
Like, give us a little glimpse into the economics
of AI development.
Well, for one, there's the choice between,
do you use a pre-trained model, or do you
try to fine-tune your own?
So are we talking like, buy an off-the-shelf
AI brain or build a custom one from scratch?
That's a very simplified way to put it.
OK.
But yeah, you get the idea.
And there are trade-offs, right, in terms of cost, performance,
and just how much you can customize it.
Right.
So buying pre-trained is probably cheaper and faster.
Right.
But you might not get exactly what you need for your specific
application.
Exactly.
And then there's the decision of,
do you use open-source models, or do you
go with the commercially available ones?
Open-source, that means it's free to use and modify, right?
Right.
But commercial models, they probably
come with, I don't know, better support, documentation,
things like that.
Yeah.
You're totally getting it.
It's like, are you going to build your furniture from scratch,
or are you going to go to IKEA and get a ready-made set?
Right.
Exactly.
So it's this balancing act of cost versus convenience,
customization versus support.
Precisely.
And all of those factors need to be carefully considered
when you're building these real-world LLM agents.
And then on top of that, I mean,
we have to consider, can these LLM agents,
can they ever truly achieve that human-level planning,
a human-level decision-making?
That's a big question.
So are we saying AI will always be
limited in its ability to plan and reason like we do?
It's a question that's still very much open.
The paper actually encourages more research in that area.
How can we better understand the cognitive capabilities
of these LLMs?
And can they really achieve that more human-like intelligence?
But even with all these complexities,
there are some practical steps that we can take right now,
today, to build those more robust and effective LLM agents.
So what are they?
Give us the inside scoop.
One of the key takeaways from the paper
is that we need to be very thoughtful about how we design
and implement these systems.
It's not just about, oh, let's throw a bunch of LLMs together
and see what happens.
Right.
It's like any complex engineering project, right?
You need a solid plan, the right tools,
and an understanding of the challenges that you're facing.
Exactly.
And the paper actually highlights a few specific areas
where we can make some pretty significant improvements.
OK, so what are those areas?
Give us the AI to-do list.
One thing they really emphasize is output processing.
Output processing, OK.
So are we talking about proofreading the AI's writing
assignments?
Not exactly.
It's more about making sure that the information that
flows between those different LLM calls,
it needs to be clear, concise, easily understood by the system.
So it's like translating the AI's thoughts
into a language that other AI's can understand.
That's a really good way to put it, because humans,
we might be able to decipher some messy language
or ambiguous language.
But AI's, they really need things to be structured
and well-defined.
Yeah, it's like the difference between speaking and slang
and writing a formal report.
The meaning might be the same, but that clarity, that precision,
it's crucial for effective communication.
Exactly.
And so the paper actually suggests
using those more structured formats, things like JSON
or even executable code to make sure
that that information is being transmitted accurately
and efficiently.
So we're not just letting the AI's chat amongst themselves
in their own little quirky language.
We're actually providing this framework
for clear communication.
You got it.
And this focus on structured output,
it also helps with error handling,
which we talked about earlier, is just a major consideration
when you're building these systems.
Right, it makes sense.
The more structured the information, the easier it is
to spot and correct those errors.
Exactly.
It's like having a neatly organized spreadsheet
versus just a jumbled pile of notes.
It's much easier to find and fix those mistakes
when things are laid out in a clear and logical way.
Okay, so output processing is all
about creating this more efficient and robust system
by improving the way information flows
between the different components.
Precisely.
And it's one of those practical steps
that can really make a huge difference
in the performance and reliability of these LLM agents.
This is all super interesting stuff,
but I have to admit, it's a lot to take in.
Yeah.
I feel like we're just scratching the surface here
of what's possible with these agentic LLM systems.
You're absolutely right.
This paper is just a starting point.
It's a glimpse into this vast and rapidly evolving world
of AI agents.
It's like we've opened this door
to a whole new dimension of possibilities.
Exactly, and I think one of the most exciting aspects
is that these systems have the potential
to completely transform the way we live and work.
So we're not just talking about AI meal planners
and virtual assistants.
Right.
We're talking about systems that can actually tackle
some of the world's biggest challenges.
Absolutely.
Imagine AI agents that can help us address climate change,
develop new medical treatments,
maybe even explore the depths of space.
Yeah, it's like we're giving these AI systems
a seat at the table.
We're inviting them to collaborate with us
and solving some of humanity's most pressing problems.
And that collaboration is going to require us
to continue pushing those boundaries
of what's possible with AI, to develop new techniques,
and to very carefully consider the ethical implications
of these incredibly powerful systems.
It's a responsibility that we can't take lightly.
Right.
But it's also an incredible opportunity
to shape the future in this positive and impactful way.
I completely agree.
This paper has given us a lot to think about.
It's clear that the journey into the world
of these agentic LLMs, it's just beginning.
It's definitely an exciting time to be following
the field of AI.
And I can't wait to see what amazing developments are ahead.
Yeah, me too.
All right, so on that note, I think
it's time to wrap up our deep dive into agentic LLMs.
Thanks for joining us, listeners.
And as always, we encourage you to kind of delve deeper
into the research yourself, ask those questions,
stay curious about the ever-evolving world
of artificial intelligence.
Until next time, keep learning, keep exploring,
and keep diving deep.
So before the break, we were talking
about that incredible potential of these systems
to really transform the way we live and work.
Right, right.
But as with any powerful technology,
there are always those challenges and those limitations
that we need to be aware of.
Absolutely.
And the paper, it doesn't shy away
from acknowledging those areas where more research
and development are really needed.
And one of those areas, as we touched on a bit earlier,
is this whole question of cost.
Yeah, developing and deploying these AI systems,
it's not cheap.
It's a significant investment.
It is.
And it's not just about the computational resources
to train and run these models, but also the expertise,
the infrastructure, to build and maintain them.
Yeah, exactly.
And the paper really encourages us
to think carefully about those economic trade-offs.
When you're choosing different approaches
to LLM agent development, for example,
using a pre-trained model might be way more cost-effective
than trying to build your own custom model from scratch.
Right, it's like buying a ready-made car
versus designing and building your own.
Right, exactly.
There are definitely some cost savings there
to be had by going with that pre-built option.
But you might have to compromise on certain features
or customizations.
That's a great analogy.
And then there's also the decision of,
do you use open-source models?
Which are freely available, but might require a bit more
technical expertise to implement,
versus those commercially available models
that often come with more support, more documentation,
but they might be more expensive.
So it's a balancing act, as with many things in life.
It is.
We need to weigh those costs, those benefits,
of the different approaches.
And choose the path that makes the most sense
for our specific needs and resources.
Precisely.
And that kind of leads us to another really crucial
consideration that they highlight in the paper.
And that is this need for ongoing model maintenance.
Yeah, I imagine keeping these LLM agents running
smoothly and effectively is a bit like tending a garden.
It requires that constant care and attention and adjustments
to make sure that everything's thriving.
That's a great way to think about it.
Yeah.
Yeah, we need to be prepared to adapt
and update these models, you know?
As the world changes, as new information emerges,
as user needs evolve.
It's almost like providing these AI systems
with a continuous education program,
constantly feeding them new knowledge,
refining their skills to keep them sharp and relevant.
Exactly.
And that ongoing maintenance, it's
essential to ensure that those LLM agents remain
useful and reliable over time.
So it's not a set it and forget it kind of technology.
It requires a commitment to that ongoing investment,
that adaptation to really keep pace with that ever-changing
landscape of information and technology.
Absolutely.
And that brings us to another key takeaway from the paper,
I think.
And that is that need to recognize and address
the limitations of current LLM technology.
Right, because as impressive as these systems are,
they're not a silver bullet, right?
Right.
And certain things they just can't do, at least not yet.
Exactly.
And the paper really encourages us
to be realistic about the capabilities of LLMs
and to avoid overhyping their potential.
We're still very much in the early stages
of developing truly agentic systems.
And there are lots of challenges that remain to be addressed.
So it's a journey of continuous discovery and improvement,
not a destination we've already reached.
That's a great way to put it.
And this journey, it really requires a collaborative effort
between the researchers, the developers, and also the users
to push those boundaries of what's possible,
but also to be mindful of the ethical implications
of these really powerful technologies.
It's a balancing act.
It is.
Between ambition and responsibility,
between the excitement of exploring these new frontiers,
and the need to proceed with caution and awareness.
Exactly.
And I think that balanced approach,
it's really essential to ensure that these LLM agents ultimately
benefit humanity and contribute to a brighter future.
Yeah, for sure.
This paper has given us a lot to think about.
It really has.
And it's clear that the world of agentic LLMs,
it's just full of possibilities and challenges.
It's been a fascinating deep dive.
It has.
And I'm sure our listeners are eager to learn even more
about this rapidly evolving field.
I agree.
And I encourage everyone to stay curious,
explore the research, engage in those thoughtful discussions
about the potential and the implications of these LLM
agents.
Absolutely.
Thanks for joining us on this exploration.
And until next time, keep learning, keep questioning,
and keep diving deep into the world
of artificial intelligence.