All right. Let's let's dive into something pretty
fascinating today. We're talking about AI agents,
you know, those AI systems that can actually
do things out there. They can plan, use tools,
maybe even team up. Right. And we've been looking
through these sources articles, some research
notes focusing on protocols, really, and some
breakthroughs with these agents. Yeah. And what
kind of jumps out right away is this this problem
fragmentation. You've got these powerful agents,
but. It feels like computers before the Internet
had rules, you know, before TCP IP. Exactly.
They can't really talk to each other easily.
It's a bottleneck. A big one. So that's our mission
for this deep dive. Figure out what this fragmentation
actually means, how people are trying to fix
it with these new protocols, and why this is
just so crucial for where AI is going. We're
even going to look at a really cool specific
example later. Sounds good. It's definitely a
key area right now. So diving in, the sources
are clear. Right now, most AI agents are kind
of isolated, siloed. That's the word they use,
yeah, siloed. They have their own ways to connect
to tools, specific APIs, custom wrappers. It's
all a bit bespoke. Yeah, think of it like everyone's
speaking a totally different language. You've
got these really smart systems, right? But they're
locked in their own worlds. You can't easily
scale up complex tasks that need different types
of agents working together. Just because they
don't have a shared way to communicate. Or understand
each other. Exactly. It's just inefficient. This
fractured ecosystem, it's a real drag on progress,
a big bottleneck. Okay. So if they're all speaking
different languages, how do you fix that? What
are the sources suggesting? Well, the strong
consensus looking through this material is standard
protocols. Standard protocols. Okay. It's really
seen as the missing infrastructure layer. Just
like the internet needed TCP, IP, HTTP, all that.
Right. To let everything connect. Yeah. Agents
need that too. Common language, common rules,
how to find each other, talk securely, know what
the other agent can actually do across different
companies, different platforms. So it's like
building the roads and maybe the traffic rules
for this AI world. That's a great way to put
it. Exactly. These protocols, they're the missing
link between having powerful individual agents
and enabling, you know, true large scale collaboration,
working together effectively. And without that,
multi -agent systems just don't really take off.
Pretty much. Their potential is really capped.
The sources mention one specific protocol that
seems quite developed already, Anthropix Model
Context Protocol. MCP. MCP. Okay, what's that
one focused on? MCP is mostly about agents talking
securely to tools or external resources. Ah,
okay. Tools, not other agents. Right. It handles
things like, is the agent using the tool correctly?
What about data privacy when it hits an external
system, managing the technical side of calling
different APIs properly? So it's like the rulebook
for an agent using a hammer or database or something?
Kind of, yeah. A secure handshake, making sure
that interaction works reliably and safely. Okay,
that makes sense. Agents need to use tools. But
what about agents talking to other agents? That
feels even more like the core collaboration piece,
right? If one agent needs help from another.
Absolutely. You nailed it. That's the whole next
level. Agent -to -agent communication. A2A. And
the sources discuss several protocols being worked
on specifically for that A2A interaction. Okay,
so MCP for tools, A2A for agent -to -agent chats.
Got it. Right. And even within A2A, there are
like different flavors. The material mentions
one protocol actually called A2A, which came
out of Google. This one seems more geared towards
internal use. Yeah. Like within one company.
How does that work? It uses something called
agent cards. Agent cards, like baseball cards.
Kind of, yeah. More like a digital profile, usually
JSON. An agent publishes its card saying, here's
what I do. Here's the data I need. Talk to me
like this. Okay. Then other agents within the
company can look it up, find the right agent
for a task, see its card, and know how to delegate
stuff to it. Good for managing your own internal
zoo of agents. Right. Makes sense for a big organization.
Keep things tidy internally. Exactly. But then
there's another one mentioned, the Agent Network
Protocol, AMP. AMP, different. Yeah. This one's
open source, and it's aiming bigger. It's designed
for agents talking across different organizations.
Different companies, labs, whatever. Ah, so more
like the public internet, less like a company
intranet. That's a really good analogy, actually.
A2A, Google's one, is like the corporate intranet
for agents. ANP is trying to be the public internet
for agents. Yeah. Decentralized collaboration.
Agents that maybe don't know each other beforehand.
Okay, that's a key difference. Internal versus
external. Precisely. And the sources toss out
a few other names, too, just showing how active
this space is. There's Agora. which sounds interesting.
It's more user -centric. Agents kind of figure
out how to talk using these protocol documents.
Maybe even in natural language, less rigid. And
then you've got really specific ones, domain
-specific, like CrowdES or SPPs. Those are for
agents controlling robots, like in a warehouse
or something. Or PXP and LKA, which are focused
on agents helping humans directly. So they need
good human -agent interaction rules. Okay, so
it's not just one answer. It's a whole... ecosystem
of communication needs. Exactly. Different protocols
for different kinds of agent teamwork. Man, that's
a lot of acronyms. MCP, A2A, ANP, Agora. How
do people even compare these? How do you know
which one is good or right for a job? Yeah, that's
a really important question. And the sources
actually get into this. One paper lays out specific
criteria for evaluating these protocols. Okay,
like what? Well, the big ones they mention are
security, obviously. Is the communication safe?
Private. especially with sensitive data flying
around crucial yeah then operability basically
how easy is it for developers to actually use
this protocol is it clear well documented you
don't want it to be a huge pain to implement
needs to be practical extensibility ai changes
fast yeah so can the protocol adapt can you add
new features support new kinds of agents or tools
later on without breaking everything future proofing
kind of exactly trustworthiness Is it reliable?
Does it work consistently? Can you trust that
agent interactions will happen as expected? Makes
sense. And finally, integration capacity. How
well does it connect different things? Agents
on different platforms, maybe cloud versus local
device. Can it bridge those gaps? Okay, so it's
way more than just can they send messages. It's
about making the whole interaction solid, secure,
usable, adaptable. Exactly. It's about building
a robust communication system. These criteria...
are how the community is sort of weighing these
different approaches, figuring out what works
best, where it's complex, but super important
work. Okay, this is definitely getting technical.
And you might be listening and thinking, all
right, protocols, standards, why should I care?
How does this affect me? It's a fair point. It
sounds like plumbing, maybe. But the sources
really emphasize that these AI agents are getting
really capable. They're moving out of the lab.
They could become like basic infrastructure in
business, science, maybe even our homes eventually.
Everywhere. Yeah, it doesn't feel like sci -fi
anymore. It feels closer. Right. And if they
stay fragmented, stuck in those silos we talked
about, their potential is just limited. Massively
limited. Imagine trying to run, I don't know,
global shipping if every port used a completely
different incompatible system for manifests.
It'd be chaos. Yeah. Utterly inefficient. Yeah,
nothing would work together. So solving this
fragmentation with protocols, it's not just about
neat code. It's fundamental to unlocking what
these agents can do. It lets them team up on
big problems, share info securely, automate whole
workflows across different systems. That's where
the real value is going to come from. So they
become more than just clever tools. They become
part of a bigger coordinated system. Exactly.
Whether that's making businesses run smoother,
speeding up research by connecting different
AI analysis tools, or even just making our personal
tech work together better for us. Without protocols,
they're like brilliant specialists who just can't
coordinate on a complex surgery. With protocols,
they can potentially form that expert surgical
team. Okay, that makes the stakes clearer. It's
about enabling that next level of capability.
Definitely. And speaking of capability, let's
switch gears to that concrete example you mentioned.
The sources highlight this really interesting
breakthrough with an autonomous AI agent in medicine.
Yeah, this sounded pretty wild. What did it do?
So this was research from TU Dresden and collaborators.
They built an agent designed to help doctors
with clinical decisions in oncology. Cancer care.
Okay. High stakes. Very. And in their early tests,
using simulated but realistic patient cases,
this agent hit 91 % accuracy. Whoa, hold on.
91 % accuracy on recommending cancer treatments
or diagnoses on simulated cases. Yeah. based
on the simulated patient data provided. Wow,
that seems incredibly high. It is. And crucially,
the sources point out, it wasn't just accurate,
it was also grounded. It correctly cited official
treatment guidelines 75 % of the time. Ah, so
it showed its work, basically, based on actual
medical standards. Exactly, which is vital in
medicine. You can't just have a black box making
recommendations. So how did they build this thing?
Is it just a standard LLM? It's built on GPT
-4, but it's way more than that. They give it
specialized tools. It can analyze medical images,
you know, MRI scans, CT scans. It can help graph
radiology reports. It can even predict genetic
mutations from molecular data. OK, so it has
specialist skills. Right. And it uses search
tools, PubMed for research papers, OncoKB for
cancer knowledge, Google for general info to
pull in the latest evidence. It keeps itself
up to date. So GPT -4 plus a whole medical toolkit
and library access. That's a good summary. And
critically, the training wasn't just random web
data. They fed it over 6 ,800 official oncology
documents, protocols, trial data, guidelines.
So it learned from the best available medical
knowledge? Precisely. That grounding in high
-quality data is likely key to its performance.
And the testing, that 91%, was it on easy stuff?
Nope. They stress it was on 20 complex, realistic
cancer scenarios designed to be challenging.
And human experts checked the agent's outputs
for accuracy and made sure the citations were
relevant. Hmm. OK. 20 cases isn't thousands.
But still, that's genuinely impressive potential.
What are the implications? Well, the researchers
are cautious. It's early stage, obviously. Needs
way more testing, validation. But the potential
implications are huge. If this holds up. And
with the right safeguards and absolutely critical
human oversight, this is decision support, not
replacement. Right. Assisting the doctor, not
being the doctor. Exactly. This kind of aging
could become a standard tool. Helping oncologists
manage the information overload, consider all
the latest evidence, maybe improve consistency
in care. And you can imagine this extending to
other complex medical fields, too. Okay, see
that example really brings the power home. An
agent capable of that kind of analysis. Now imagine
that agent being able to seamlessly talk to another
agent that manages patient scheduling, or one
that finds clinical trials, or one handling insurance
paperwork, all using a shared protocol like ANP
or something similar, reliably, securely. That's
the future of these protocols unlock, right?
That's exactly it. You have these agents getting
incredibly good at specific complex tasks like
that medical analysis. But for AI to truly scale,
to weave itself into how things actually get
done, we absolutely have to solve that fundamental
communication problem. How do they find each
other? How do they talk securely? How do they
collaborate reliably? Protocols are that missing
foundation needed to build that interconnected
AI future. Right. So wrapping this up then, we've
seen the problem. AI agents kind of stuck in
their own worlds, fragmented. We've seen the
potential solutions taking shape. These different
protocols, MCP for tools, A2A and AMP for agent
-to -agent comms, each with its own focus. Yeah,
the infrastructure being built. And we got a
glimpse of the incredible potential with that
medical agent example showing what they can do
individually and hinting at what they could do
together. Right. And maybe the final thought
for you listening is this. Considering how fast
these agents are evolving, and how much we need
them to coordinate for complex tasks. Ask yourself,
as AI agents get woven more into our world, how
will their ability or maybe their inability to
communicate effectively reshape, well, everything,
the industries, the systems, maybe even our daily
lives? It's definitely something worth thinking
about.