Speaker 1: Welcome back everyone to another episodes of ventures and DevOps.
And I am really excited today because we're going to
jump into one of the areas that I find personally
really interesting, but also our guest has worked at a
number of companies in areas that I feel like lots
of companies get wrong. So I just want to welcome
to the show. Sylvan from from Rutley, who is the
head of veloper Relations. Hey, how are you doing?
Speaker 2: Thank you for having me. We're in all good.
Speaker 1: Good to hear, so, I said, head of velper Relations,
And I got to be honest, I feel like a
lot of companies have started to over utilize this term
to mean a wide variety of different roles and responsibilities.
Can you give me like a breakdown like what it
means for you today?
Speaker 2: Yeah? Absolutely, Actually to a great question because I have
just puts it on LinkedIn two days ago when I
was higher and historically the role of developer relations is
to empower developers to use a product developer tool by
providing education resources, by answering any questions they may have,
and then just overall marketing you know, the product in
a way that fits engineer, which is not product marketing, right,
it's all about what you get out of it. Don't
don't tell, show me, So it's tutorials, talks, you know
in the former article, YouTube, video, and so on and
so forth. But as I joinedly, it was clear that
AI and more specifically here llms are the future of
incident management. For those who don't know, rutly Routly is
and on call and incident management platform. So when something
breaks and you have people on call we need to
respond to the incident, that's where they go to manage
the incident up to it being solved. So I played
a big role in that, and we'll speak about it
during this episode and most of my time actually at
truly most of my AERG, I would say like maybe
seventy five percent has been dedicated to agent AI agent
relations because we can see II agent as just another
member of the team, and this agent also need to
be taught and onboarded, just in a way that's different
from humans. So I would say, while I'm the head
of developer relation, can also say I'm the head of
AI agent relations.
Speaker 1: Well, that's that's definitely a wide area.
Speaker 2: You know.
Speaker 1: I'm all interested because I found you mentioned this that
you're not doing product marketing, you're still marked in some
way to engineers. Uh, they're notoriously the biggest challenge to
get engineers on board with whatever you're trying to sell.
I mean I found of all groups of people, uh
even ones in the technology space, I feel like engineers
always want to do things themselves, right it.
Speaker 2: Is and and with already we are targeting SARES psyche
truly ability engineers who are even more skeptical and hard
to convince because and for good reason. Right, their job
is to ensure that the infrastructure is rolling smoothly and
in optimized fation. And so you want to be careful
with the tool and the frame. A new framework or
new tool or methodology that might include might bring chaos
or instability. And uh, you know, so I used to
be an SARE myself. Back then SARE was not truly
a thing yet. I was working for flight Chair as
a develops engineer. We were in the top fifteen months
visited website in the world back then, displaying about one
point five billion slide a day, which you know was
definitely large volume. And we got acquired by LinkedIn, where
I work as a senior SR. This time for three
year and you can imagine the volume. So I've been
on the side, on the engineering side, and so I
completely you know, get the personal and I think at
the end of the day, it's just that these people
they don't want to waste time with marketing copies. They
want to understand what's in it for them and their job. Right,
So for me, it comes, you know, very naturally. I
don't think it's a challenge, but I think for someone
who does not come from an engineering background and doesn't
have as good of an understanding as you know, an
engineer may have, it may be hard to communicate to
this audience where you know, I think this thing might
come from.
Speaker 1: Yeah, no, I totally got it. I know I'm gonna
I really want to dive into the core topic, which
is self healing systems. And we were talking a little
bit before the episode started about like this has been
your like a lifelong project area. How did you get
into this? I would say it's the first thing, like
was this, Like you always knew this is this was
the thing you're going to go into, and like how
long you've been doing this and what does that really
mean to work in self healing systems?
Speaker 2: Yeah, so it goes back to actually I started the
project when I was working for a slight share as
a develops engineer slate slash slate site reabity engineer, and
I was on call and I had to you know,
manage outages back then who are using puppet as a
way to ensure that unfraustructure was as it should And
you know, ultimately there were a lot of repeat incident
or a lot of outage or you know issues that
were coming from the same type of problems. I think.
You know, as you as engineer grow in their careers,
they kind of know what are the main failure types
and so and so, you know, like I think it,
it gets repetitive, and I think a great engineer wants
to automate itself and not do the same thing over
and over again. And so yeah, that's where the idea
of building a self feeling system came about. So maybe just.
Speaker 1: Sort off like you said, you would see the same
sort of regressions over and over again. Was there like
one in your mind that was just like the most
common where it was like every single time it happened. It
was like the driver for you to make a real
change in an organization.
Speaker 2: Yeah, you know, I think. I mean, I know it's
been like more than a decade, so you know, I
won't have like super sharp example, but I think the
classic ones are, you know, issue with lack of resources,
whether it's you know, storage or CPU or memory, and
you need either to increase decrease the lots somehow or
distribute or scale. Could be like a service that misbehaving
and you need to restart. Could be a lot of
things that I think, So I think the industry took
different throughout. I think now with Kubernetists, what we do
is that if if this is misbehaving, we just shut
it down, right, we get rid of it and we
start a new one. And obviously kubernets is great at scaling,
so I think this tool to like the self feeling system,
it still works in this way, right like by new
king thing or scaling thing, you can heal a system.
I think in my mind I wanted to take a
different approach where I was training. I was envisioning a
system that would actually address the root cause I think
in some of the cases, you know, instead of just
new kings, this thing like really try to mimic what
a human engineer would do. So, Yeah, that's that's kind
of the philosophy that I had back then.
Speaker 1: I mean, there's definitely a huge population of engineers who
think that the what they would do in those examples
would be for sure to restart the machine or the
container or the node if it started to run out
of memory or processing power. And I feel like that's
sort of the crux of one of the issues that
I've seen over and over again is that we do
build those systems that I say we as collective humanity
and not at my current company, that automatically restart or
you know, allocate more memory or processing power. And I
feel like the automatic scale scale out or scale up
for resources can make sense if it doesn't create a
negative impact on the feedback loop that you have to
solve the problem. And I feel like this is one
of the problems with automatic restarts is that it doesn't
really solve the problem. It's still is persistent there, It's
going to keep happening, and also you're delaying actually doing
the investigation, and you're also eliminating some of the evidence
that would allow you to identify the problem there. So
it's really great to hear that you know, you thought
that the appropriate process was, you know, go out like
why is there extra memory usage? Why is you know,
the machine getting stuck, et cetera, et cetera. And I
feel like that's sort of the thing that sets apart
the best esses from the ones that are just coming
into quote unquote do the job it is.
Speaker 2: I think then you need to strike the right balance
between achieving the end results, which is capability, stability, and
if restarting is the way to go, and you know,
you don't need to spend engineering resources. And obviously, I
mean it's walking, so you know, I don't think it's
it's a valid issue, but yeah, as you said, you
need to find a strike the right balance between just
doing this over and over and if it's repeat like
out edsure issue, you know might need investigation. And so
the idea that I had back in two Southern was
I think I started in twelve thirteen was too really
ingest as many data as you can from a distributed system,
you know, whether it's any lugs, any metrics application, lugs, traces,
and ingest all of this in the dallabas. Back then
we were using fluend, which is an open source message,
but we still exist and actually it's very popular. Back
then I was we were one with lecture of the
first main user big use there actually shout out to
the team if they are listening. And now they went
very far with this technology and still all of this
in unstructured database. Back then we I bet on on
Mongo DIB. It doesn't really matter the technology, but that's
what we use for the prototype. And then based on
that come up with like a state of a system
and and use and then like try to resolve this
issue by throwing at the system a bunch of actions
that would be safe. You know, I'm speaking like any
action like a like AREM action or something like you know,
drop database like you need to be careful. But but
but other set of safe action and then use machine
learning as an engine to learn basically what, depending of
the state of the system, what could solve the issue.
And so we designed this for a way distributed infrastructure.
Actually continued this work at LinkedIn and they asked me
to write a patent, which eventually was accepted. But yeah,
it never got a chance to build it, unfortunately, because
then I left to become an entrepreneur. But that's why,
you know, I was telling you have been swimming in
this topic for a little while.
Speaker 1: Yeah, I mean I still want to go further into that.
So just to summarize a little bit, the strategy is,
we're collecting tons of logs, maybe metrics, et cetera. We
maybe have access to the source code, and we train
on that data to identify based off what sort of
errors we're actually seeing, how to pinpoint potentially is it
a part of the source code or the infrastructure which
could be problematic, and then utilizing that on errors that
actually do come out of the system to help dive
in to identify the cause or does it go further
than that?
Speaker 2: Yes, I think that's a good point if we speak
about I think I think back then I was I
directly dive into resolution. I was a young engineer, you know,
I was like twenty five, twenty six, maybe even younger
than this, so I was not really mature. But I
think I think starting with the root cause analysis is
the right approach. Obviously, you know now with insight it
makes sense, but but my goal was really resolution, which
ultimately you know where you want to go. So yeah,
I was really really focusing on on direct resolution, and
I would be a mix of kind of run books
that that you know, we could feed and then but
then more interestingly set of safe action safe commands that
the system could run and could see if this solved
the issue, and then kind of do like a learn
from it, you know, maybe try something. It doesn't work,
it's fine, you know, we just ditch this kind of
pass instruction instruction set as an option, but sometimes it
will succeed and it will use this for the next incident.
And all of this would be based on machine learning obviously,
like you know, like the more success you have with
an instruction, the more you are likely to use it
next time. Back then, machine learning was nearly not as
advanced as it is today, you know, so it's it
was hard like to achieve this goal. And I think
the industry, I don't think anyone build this type of
system until like now you add one player that did
something similar, which is Facebook. They build a system that's
called f bar F B A A R that the
definers self feeling it was to manage Facebook that center racks,
so it was not system but tracks where it would
auto automatically perform action to solve some production issue. But
it was deterministic, so it was not the well, no,
none of machine learning used in it. And then Dropbox
in twenty sixteen presented no rout Aserican and this was
a self feeling system but for distribute distributed systems, this
time for web infrastructure, but same year it was deterministic.
So I you know, I think this system I've been
around for like more than a decade, and I've been
produce producing value. I know they've been in production, and
I think as area generally not up with having mechanism
or system working on their behalf and kind of in
the shadow. But the truth is that they've been around
for a while. I think. Now the main difference, the
big difference, which is a huge difference, is that we
are including this machine learning LLM part which is non deterministic.
And that's a big deal.
Speaker 1: So let's just dive into that for a second. When
we say deterministic in the history of self healing systems,
we're talking about like auto scaling groups or identifying specifically
based off of rules that some engineer wrote, what they're
seeing and then how to handle the situation very concretely.
Is that accurate?
Speaker 2: It's a great and also a one that was called
earths and it's exactly how you describe it, like a
runt book that's threaten by a human and then this
rent book is a trigger based on a specific signal.
But it's absolutely deterministic.
Speaker 1: Yeah, And I think the interesting thing with the deterministic
systems is that really require you to do the root
cause analysis so that you could write like if no
run book applied or the run or a run book
applied either by a human or through automation, didn't have
an effect that actually resolved whatever incident you had, you
had to actually still do the root cause analysis. And
now I feel like we're getting into you know, I
think everyone's waiting for us to talk about this how
to apply Uh, I hate to say AI to this concept.
So now that we're now that it's twenty twenty five,
what does it mean to deploy LM to be able
to self heal a system? What does that actually look
like in practice?
Speaker 2: Yeah? So, you know, I think you hit the nail
on the head with speaking about the root cause analysis.
That's obviously the first step that this system need to
do is to understand what's up. I think in the
past it was a mix of for this self feeling
system I think if part of this runt book where
automatically based on a very specific maybe anologue or you know,
something like very trivial, and then it would be automatically applied.
In other cases, a human would need to do the
root cause analysis and then you would push this rund book,
which would still save a ton of time, right because
this system would orchestrate all the action that needs to
be done. And when we are speaking about very complicated
infrasecure like at Google, Facebook or LinkedIn, you know, it
can be a lot of work. But here the idea
is that we can throw whatever broken system to this
LLM and it should understand what's up or at least
come up with hypothesis. And I think the hypothesis is
very trivial, very important. Sorry, I don't think we need
We should not consider this LM to be God or
to be the silver bullet. We should consider them just
as another human which can make mistakes. We make a
lot of mistakes, and so I think one key element
is to understand this that this ipothesis also kind of
a degree of certainty, and so I think a great
ais are will provide as part of the diagnosis what
the degree of certainty that it has about the diagnosis,
so human can say, hey, if it's fifty percent, maybe
I should not pay too much attention attention to it.
If it's ninety five percent, okay, maybe I should really
look into it.
Speaker 1: So what sorts of source data are you utilizing in
order to feed into the l I'm going to ask
you questions about about that afterwards, but specifically right now,
like is it you know a list of like source
code and some other things like what does the sourt
settle look like?
Speaker 2: M So I think with as with many tools in
the LM air space today, context is king, and I'm
going to speak about what we are building at Rutely,
which again is an incident management platform. And why I
repeat this because it really matters in the sense that
engineering teams will all the signals that are associated with
an incident will flow through their incident management platform. It
just makes sense, right, And so these platforms such as
routely have pretty much all the context that is available
generally to solve this incident. So it can be monitoring,
logging traces, it can be more than this. It can
be slight conversation, right, it can be a zoom call
here you know, you can do a transcript as a
zoom call, so you can fit this in the l
l M. But I think there also are very important
data such as the history of post mortem incident resolution
report right where everything is documented from what happened, how
it happened, how did we solve the incident, how this
incident like how it do I solve? Who solved it?
And all this data is like super important for for
the AI agent to to to find a root cause.
And and the last one I I forgot you mentioned
is obviously anything that's linked to changes, which you know
takes the form of cod So the list of commits
is often you know where you'll find the issue.
Speaker 1: So you've actually got a system that and justs all
this information and that's out. You know, here's a here's
some action items take And then I imagine some companies
are actually automating based off of that to remediate the
problems in production. Or is this like you need a
human to review this before you do anything else.
Speaker 2: Yeah, I think the safe this field is extremely like
the auto healing mechanism around a LAMB is extremely young.
You know, the oldest company in the field maybe two
year old, perhaps even less, there is a lot of
competition in the space, seen at least twenty five products,
and I've spoken to a lot of engineers who are
building this internally at large companies, so you know, I
think everybody's doing differently. The maturity of the product is
also very different. But I think for s ARE is
obviously a really ability is ultimately the goal, and not experimentation,
right its secondary goal definitely Right, starting with just investigation
is the right way to go. And then I think,
as the space meteor and perhaps the model, what's work
great with this technology? You can teach it right, It
becomes better over time, so you can train models on
your data, you can tune it right. So as the
technology mature and the model is learning, and perhaps we
are learning as humans, we can go more towards allowing
this tool to do the resolution. But I think the
first step in is just fun our root cause analysis.
Speaker 1: Yeah, I mean, at least from my personal standpoint, I'm
scared to hand over the tools to make changes to
production infrastructure automatically without involving some sort of review process.
And I mean, I guess, I guess it's fine to
have like another LM review. The first llm's work in
some way, but I don't know if the direction matters.
Like I think you need someone to review the context
of what's happening, just like you probably want multiple engineers
on call to actually validate any sort of code changes
that would have to go into production, because I mean,
otherwise you're in a situation where there's a critical event.
It's three, something's going wrong with the database. You log
in and accidentally drop the production dB. I mean, I'll
pause there because that this has actually happened to more
than one company, But I think there's one in our history.
I think it's almost ten years old now, a major
source code get server company had a production incident, very
famous with their I think it was Postcress at the time.
So engineer is definitely not infallible when it comes to remediation.
But I guess my question is going to be do
you find with all the data that you're collecting that
the set of incidents all point back to some like
as far as you're concerned, repeatable or already seen problems
like oh yeah, this sort of software development issue or
some syntax problem like no reference exception or dynamic module
loading or you know, memory exhaustion or something like that,
or are there like minor differences as time goes on,
like oh well it used to be this said, but
the next thing is sort of something that we haven't
discovered yet, and so you're still discovering sort of new
failure modes.
Speaker 2: Yes, just picking like very briefly on what you said before.
I totally agree with you that ELMS should you be
considered as another gument So can review, doing canary deploy,
you know, passing the change through the CD basically making
sure that the change is safe is just a must do, right.
I don't think we should treat treat what the AI
say as the resolution pass as any different as a
human would say.
Speaker 1: I mean it goes further than that though, right, Because
if we were able to confidently take the output from
LLMS and feed it back in, LMS should be able
to develop increasingly large solution of any size. And we
see that no company has a software an automated software
development or agent engineer that can just continually push out code.
Even ones operating of very small scopes have utterly failed
in their release and their push out of their products,
let alone larger companies that have been trying to build
stuff up. And the recent craze on vibe coding. Yeah,
I mean, and for anyone who's not aware, it's this
idea where you just you don't even look at the code.
You just have the LM produce all the output, and
whenever there's a problem, you just say, hey, here's the issue,
try to fix it. The problem is that the context
window will have to keep growing indefinitely. Every new feature
you add will continue to grow. And so as long
as we have these two failure modes A the LM's
finite context W and B, companies who have made it
their sole goal to make money off of automated software
development aren't making money off of that, you know, aren't
like wildly successful. The likelihood of you being able to
do it, lust being able to trust them fundamentally tells
us that, you know, we're not at that point yet.
Speaker 2: For sure. I think the this vibe coding is variable
in many situations. If you want to prototypes, maybe if
you are very young startup, you know, I think it
makes a lot of sense. But when you get to
the stage where you hire a necessary or you need
stability in your product, or you are pushing a product
that is crucial for your customer system. You know, I
don't think this type of engineering practice, if we can
call it this way, makes sense, but I think this
technology can bring a lot of value. You mentioned do
we find patterns in the type of incidents that with
see through the system? And that's a really great question.
So one of the initial leading at rutely is the
rootly Air Labs. It's a community driven initiative where we
hire software engineers. We have the head of platform engineering
at Venmo and the former head of AI at Video
and other very smart student PhDs from Stanford and whatnot,
and we pay them to create open source prototype leveraging
the latest air innovation to see how can this be
applied to the world of free the ability and system operation.
And one of the projects that we're working on is
exactly what you mentioned, is to create a graph of
the incidents and see if we can find patterns. So
it could be an area we are infrastructure, or a
part of your application, or perhaps a type of failure.
You know, let's let's say we speak about we spoke
about resources. Is the resources often something you know that's
that's fading our system and maybe because our skating rules
are not aggressive in US, and and alms are helping
us to to create this graph because they are you know,
they are great interesting unstructured data and make sense of it.
And so then we can create this graph that can
and power a sorry team to understand where and stability
come from.
Speaker 1: I mean, that's something I'd be super interested to find out,
like where statistically are the most problems coming from, and
how that maps are, or like what the confounding variables
are between maybe the culture of the company or software
languages that they're utilizing, or the frameworks or the industries. Right,
you know, maybe these industries have these common incidents. Like
I think that'd be super interesting to say.
Speaker 2: Well, yeah, so we we're building it. You can check
it out. We have a gid up GitHub space if
you look for rootly AI labs. Everything is open source
and we're always welcoming people to to join, just giving
ideas or or contributing. Again, we're paying people to do that,
and so it's kind of a side job. But yeah,
I think AI is breaking like and and you know,
I would say it's kind of a side thing. You know,
it's it's not it's not as an ambitious goal as
like self ining system. But I do think that's where
you see that l M can can allow you to
do other things that are interesting. I know there are
prototype that I think it might be interesting in two
other prototy, but speak about it very briefly. One of
them is to create a diagram out of a post
more tem showing where things went wrong. And post more
TEMs are actually kind of painful for engineer to write
like you know, no one wants to do that. You
need to remember what happened and bring all of this together. Actually,
that's what's good with lelamps and that's something we have
in Rutley. Rutley will draft a pastmare tem for you
and then you just have to review it and chances
are that the post more time is going to be great.
And then the next step that we we tried with
the Routly air lab is how about trying to offer
another way to consume a postmare tem and a visual
way may help, especially I think non engineering audiences to
understand where the failure happened and why the other service
that may seem totally enerated was done as well. So
the way it works is that it will ingest the
post mortem makes sense of it as a geson and
then ingest you know your your code base, infrastructure and
code and make a gson out of it and then
merge this to and and create a knockdown graph. So yeah,
that's you know, another way to leverage LMS, which which
can ultimately help a very team to do their job
more efficiently.
Speaker 1: I like how you called out that after you are
you've pushed out a post mortem that someone actually has
to review what way you've created, Like, no, don't just
don't just take that and you know, start sending it
to people as the official thing are Like, if you
take an LM generated post mortem and you put that
up publicly, you will for sure get harassed on Blue
Sky and asd on very quickly about how you spend
zero effort and then making sure that that was accurate.
It's very easy to identify LM generated stuff like that.
Speaker 2: And the second thing that we've built that maybe of
interest to the audience is is an on called Burnard Detector.
I think that's particularly interested for companies that are distributed
where manager may not be in touch as much with
what the team is doing, and especially for large companies.
So what we do is that we feed all the
associated data about incident responder can be how long was
there shift over the last week, how many incidents they
had to travel, shoot, what was the severity of this incident,
how long were they working during the night, and so
many things you know that are all instructured data. Right,
So we again like Elem's are great at this, And
then from this an elelant can come up with kind
of a burnout level, you know, and see, hey, like
you know, this person was like smashed very hard with
a bunch of hard incident, like you may consider giving
them a break. I'm sorry, So as.
Speaker 1: Long as it doesn't also suggest the therapy that should
be necessary and try to provide that. I think you're
on the right track there, Yeah, I mean, yeah, it
can be. It can be difficult to see the differences
between individuals. Like some of them are way more interested
in actually jumping in and you know, diving in and
trying to identify those problems and solve them, and others
are you know, care more about the routine. But I
don't think in my in the history of my engineering career,
I ever saw someone jump up and down and say yes,
I would love to be woken up at three a m.
And jump on a call with other people and try
to justify what was what was happening. So, you know,
I think you're definitely onto something interesting there.
Speaker 2: Yeah. I was when I was young because I wanted
to learn. But I'm definitely not into that, but go
for it.
Speaker 1: Yeah, I mean, I know. I think that's an interesting
point because you know your career, things change for you
over time, and maybe at some point you are willing
to make some sacrifices, you know, But I don't know
if it was the case for me, Like I remember
my first job out of university, there would be incidents
in the middle of the night, and I never had
to deal with that sort of thing in my life
up until that point. Like I didn't run my own
data center in my home, and even if I did,
I don't. It was not at the point where you'd
be like getting alerts to be woken up to deal
with one of your virtual machines failing. And the university
wasn't a thing. You're you're definitely awake while you're causing problems, right,
things aren't happening while you're sleeping. And so my first
job like this would happen, and I definitely came away
from that with it, with the idea this is wrong,
Like I don't ever want to be woken up in
the middle of the night, Like you don't have to
be it's not a requirement. And since then, like I've
really been on the path of highly reliable systems. And
I think the part that really stumps a lot of
people is they focus a lot on the preventative nature
that they can try to prevent every problem. Oh, get
one hundred percent test coverage, or you know, have a
highly reliable solution by duplicating the infrastructure in multiple regions.
And I mean the thing I think you said at
the beginning of the episode, which is that it will
go down, Like you cannot have one hundred percent reliable
system and so at some point you have to optimize
for recovery and not just prevention. And this is where
I think a lot of people get stuck because like
at our company, we have a five nines reliability SLA,
and that means that by the time so one gets
alerted and they get online, we've already violated the SLA,
let alone identified and fixed the problem.
Speaker 2: That's a great point you bring that I think this
system well. First, first of all, getting woken up at
three am is never a pleasant experience, and it takes
time for your brain to get into it. And you know,
maybe you were in some deep sleep and you are
waking up and kind of having like little panic attack
or you know, something like tough on your body, and
then you need to you know, get time to ingest
the data and so on and so for so we
know how hard it is for your body and your mind.
I think that's where aiser is. You know, which are
like self feeling system or tools that can lead to
that can help. Is like, hey, this tool can ingest
so much data in such a small amount of time
and give you something to get started, like an initial
root cause analysis. Then by the time you get to
your computer, you already have something ready to look at.
Hopefully it's ninety five percent confidence and you know you
just have to push the fig that they suggests. I
think it's such a great tool. I think that we'll
have a great positive impact on the on the health
of our people. The second thing I think that's that's
interesting with this tool is that you mentioned you know
five nines, and you know, we know that it's possible
to get five, nine or six nines. But the companies
that are achieving that, like the Google of the world,
are investing huge amount of resources, uh you know, human
and financial to reach this level, and for the rest
of us, the rest of the businesses is simply not
possible until today. I believe that these self healing tools
will allow companies to reach this type of you know
SLA without spending the budget that that Google does. And
I think that's truly I think that's going to already
find the sory space.
Speaker 1: Yeah, I mean, I will say that one of the
biggest struggles we have is actually customer perspective alignment. Like
it's a challenge for us to know what the status
of our system is like it's subjective. Is it up
or down? Is not like you can look at some
chart and have the answer there. And what's even more
important is that if we believe that our system is up,
that our customers also believe that our system is up,
because this mismatch is really what you're trying to solve
for If customers always like one hundred percent, reliability is
not what whether you think it's up, it's whether or
not you know the people that are paying you money
to you know, run some system believe it is and
the customer expectational alignment like that's actually a really that's
a huge challenge and I'm not sure you can fundamentally
all that problem. But yeah, I do things as a
huge gap with a lot of companies being able to
get for from where they're at, which is like their
software is going down like at least once a week,
to something much further than that.
Speaker 2: Yeah, yeah, so, I you know, I do think the
LM can can help with that in some capacity. Maybe
I can jump also and share a little bit about
what are the challenging of building these type of tools.
Speaker 1: Yeah, please, I'm dying to now, right.
Speaker 2: So, I think one of the hardest things, which you know,
I think it's a big week LACE is obviously the
non deterministic part of the system. And here I think,
you know, the old adage you cannot improve or fix
what you cannot measure is you know, works very well,
right like for l elms, even if you provide the
same input, the output will be different. And so it's
very hard for engineering team to ensure that one, you know,
is my system running well as you say, it's subjective,
and I think here it's even more subjective because it's
not a matter of just hey, am I getting a
two hundred or five hundred or maybe it's a two hundred,
but which is too much you know latency. We're speaking
about an output which is natural language. And second is
my output better or worse? So that's that's like a
big challenge in in in building this system. And and
another point is that this system don't have sking in
the game. And elms are like dream machines. They are
designed to put together chain of tokens that are, you know,
using statistics, the more likely to be pleasing, you know,
and sometimes this what the assembly is not rooted in reality.
But they still did their job as they should. And
so if we compare this to a human when you know,
if let's say were you're my manager and I'm working
on trouble shooting this incident, and I'm like, hey, I
think that's the issue. I think this is where we
should you know, Look, I have skin in the game, right, like,
like I'm putting my skills on the line, and so
you know, when I share this with you, I have
a certain degree of certainty that this is a probable
cause for ALMS. There is none of that, right, So here,
what we've done at routely is that we we have
two types of agents. We have the master agent, which
is orchestrating sub agents which are in charge of doing
the work of gathering days trying to understand, like doing
the grain walk and then coming up with an answer.
And the master agent will make sure that the overall
narrative mix sense. And there is anothern agent that's like,
you know, coming up with something that doesn't make sense,
like a manager would do. So what's funny with with
LMS is that it kind of mimics a human narrative,
a human dynamic. Yeah.
Speaker 1: No, I mean I feel like the most common questions
I end up asking are how do you know? And
why now? And Alan's not so good at solving that one,
especially when like a bunch of changes all stack together
to then cause the problem. Right, you know, you look
at individual changes and they all seem fine, and then
only together do they cause the issue. So I mean,
I do see this sort of interaction is necessary. I
do want to ask you about your models, though, so
are you taking some fundamental like some foundational model out
there that's available open source and mind too, it are
you building it up from scratch? Is there like one
particular companies models that you like more than others? What
does this look like for you?
Speaker 2: Yeah, So I think the assumption that I think the
you know, anyone not not like deep in the space
would assume is that you need you need to train models,
you need to tune it using in our case, you know,
like your customer data or like you know, if you
are building this internally, your specific data. What we found
is that this is actually not needed for most of
the incidents, Like out of out of the shell like
model like work perfectly fine, and we'll find most of
the issues. Training model is actually really hard, really costly,
and we haven't found so far. You know, we're still
early you know in the space. So I think we'll
get to this eventually. But I think for now we're
finding the most value by not doing it. I think again, like,
it's it's difficult, it's expensive. And then there is a
lot of skepticism and I think issue with privacy and
security companies on one their data going into l LMS heaven,
you know, if we would do this only for their ALM.
So what we found matter of the most is ready
the context that you provide. And I think what we've
found is the most valuable is the non technical stuff.
But what I mean by this is the human generating
generated context. And when we link this to roughtly, it's
two things. The first one is the former postmare terms
like this is a gold mine of information. Most of
the time your system is unstable and it's gonna you know,
this area will remain unstable at least for some period
of time. Generally you have action items that your team
is supposed to implement. Sometimes it's actually items are done,
sometimes not. You know, there is always a priority issue
with we need to release this future, just fix this
potential bug. And the second thing is all the communications
that's happening on Slack or teams or Zoom or Google Meter,
you know and whatnot where that's how incidents are sold, right,
it's human communicating between each other and sharing so much
information that's business specific, right Like Ellen's are trained on
a ton of data that's online, but it's not specific
to a company obviously, right, And and and so we
found that this data is that really boosts the results
that we get out of these tools.
Speaker 1: Yeah, I mean, I think you you said it a
different way. I like the context of you have to
pull in the business criteria, understanding and context in order
to have a valuable output. And I think it's you know,
it can even be more than that. It's the fundamental
nature of l ms that we have today, Like it's
not a it's transformer architecture, which you know, is fundamentally
lacking the reasoning piece, Like they'll never be able to reason,
which means they'll never be able to make a decision
based off of the business context. But they'll be able
to do a little bit better of pulling that in
and combining with the output that it would normally get. So,
you know, my one of my questions is here, Okay,
so building up a foundational model, and I think we've
heard this before auntaventures and DevOps, and that's that it's
incredibly expensive. Also, the industry is moving quicker that the
new foundational models are are just as good, So spending
money on building a new one doesn't make sense. Actually,
I think we heard one time that even fine tuning
models doesn't make sense because the next generation while like
say anthropics three point seven cloud versus three point five,
it's not it's not really that much of an improvement,
but you are getting up to date data. If anything. Rather,
you know the time stamp has changed, and if you
spend time training it, refine tuning it. Rather then by
the time the next one comes out, all you're fine.
Tuning well, first of all is a waste, second all
is expensive, and third of all, like you may be
able to throw your quarries at your prompts at the
new model and get the right answer out anyway, So
it's good to hear that. Does that mean you're using
some sort you're using like something from Olama or deep
seek or something like that.
Speaker 2: We found that what entropy provide is generally, you know,
the best performing. Ultimately, we are integrating a number of
different model providers and we use different model a different
step of the process. You know, I cannot explain in
detail because it would be too long, but you know,
when we're basically like the the agent will come up
with an initial probe that you know, we compose and
I would say, like different model will for instance, coming
up with the let's say the master thesis of what
you know we need to look for might be better created
by some model, and then you know, the actual technical
part maybe better than by another model. So it's and
it's a moving target, as you said, like the industry
is moving fast. There is a constant flow of new
models and so so you know, I don't think it's
something that really set in stone.
Speaker 1: Do you do something to validate model changes? So for instance,
when three point seven came out, are you still using
three point five before? Or you can, like you have
some sort of ender templates or system prompts that you
can throw out and validate that the answers still makes sense,
that the RCAs and post mortems that you're doing still
are understandable on match and somehow validating the outputs. Like
what does this process work for you?
Speaker 2: Yeah, lung Chain as a bunch of open source tools
that can allow you to do this. So we are
constantly you know, tracing every like all the different you know,
it's like kind of a tree right with different nerd
and paths, and we keep track of everything that's being done,
the reasoning, the output, and we constantly measure you know,
the performance. So that's definitely something that we do. That
being said, I think it's a challenge. It's still a challenge,
you know to really understand the the quality, how the
quality is shifting. They were a talk at Aserican in
Santa Clara a few weeks ago. I think it was
the AI director at ASIA who were speaking about one
of the mobile based product that they are using, and
it was saying that it's very hard for them to
understand how to measure that, and they are relying on NPS.
So it's a neutral promo Neutral Promoter Score, which is
basically an industry standard rating, which is like would you
recommend this to your friend and family to assess how
their model are doing? And I think that was like
really shocking to the audience.
Speaker 1: I mean, we do not We do know from experience
that like NPS is like totally wrong from the net standpoint,
because you should never ask from a human psychology standpoint,
you should never ask one what they would do, but
metrics on what they have done. And I think that's
often the problem. But I mean, I think it really
goes to show that there is no good way of
adequately measuring these things. You have to do it within
context of like what your business is doing, you know,
for instance, really being able to do the incident management.
And I do think that at least I know I
have this question, so I'm sure someone else does that.
There you're getting to the point where you don't want
to have to make the code changes to like go
into GitHub or get lab or heaven forbid a bitbucket
or one of the other ones to actually put up
a poor request to fix the problem. Wouldn't it be
great if there was another LM out there that had
the context of the source code and everything, and you
could just give it the output from routlely and have
a different agent do that. And that for me means
you need to somehow integrate with other agents. And I
can't believe I'm saying this, but MCP model context protocol, Like,
how do you feel about that?
Speaker 2: I'm a huge fan of this. I think you are, Like,
that's exactly the architectures that you need to have in mind.
Is not an agent is the collection of agents, And
it can go as deep as like, let's say, like
you are doing work with GitHub. You can have an
agent working on commits one, on pull request one, you know,
like on each type of different resources that you may
have with gitub you really need to tailor the agent
too for them to do their best job. Because again,
like they don't always have the business logic and understanding
that we do as a human and bringing this context
in each of the small like sub set of agent
is critical for MCP. I you know, I'm a big
fan of MCP. I've been wearing an MCP badge at
Aserican and Cuicon because I truly think it's it's the
technology itself is nothing, you know, amazing, Like at the
end of the day, it's just to get away to
an API.
Speaker 1: I mean, I really want, I really want to stress
that enough, Like anyone who's not cut off on this,
like it's nothing special, Like just imagine you deploy a
new API or verse proxy CDN in front of your
existing software and you're mapping from one protocol to another
one like from TCP to UDP, from h GDP to
or from rest to g RPC. It's really just another one.
And I think the joke right now is that the
AS and MCP stands for security.
Speaker 2: Yeah, you know, I think I mean that. Yeah, I
think we go back to the you know what we
discussed at the beginning of the conversation. You have the engineer,
we will be an athayer and obviously there is a
lot of things that are wrong with this, with this protocol,
it's not stable, it's full of bug securities. Absolutely not
to concern. But I think what's interesting is the concept
of breaking this world between UH this AIG and and
all the resources that are out there, whether it's data
or system and and m cps just think it as USB, right,
it's facilitating the conversation between between these two entities. It's
it's open source and and it really unleash a lot
of power for for AI agent and data sources, which
as you say, most of the time is going to
be race API to communicate in a way that's very
optimized because one there are many issues with agent to
consume most of the time it's it's race APIs as
we say, like they may not have the business logic.
So getting an information from an API main equest you
to do multiple calls to multiple route and you know,
like the LM may not know what if it's feasible,
they may not use the best way to do that
may get lost. And so MCP is really enabling this,
removing all of all of this complexity. And for instance,
I built an m CP server for Rutely and what
it allows the developer to do is to when they
get paid and stuff opening, you know, the web up
going to rut looking the incident and you know it
taket time context switching, which we know is bad for developers.
They can just ask into their favorite power, I get
me the last latest incident is going to put up
in their chat and assuming it's simple in US and
there is in US data in the payload of the incident,
you can ask in this case, I use yourself to
fix the incident. And so you go from production incident
to resolution in a matter of a minute. Again, it's
as you said, it's you know, some people were like, yeah,
it's a joke, it's it's not truly, it's not revolutionary,
it's not. But I think what's great is that it
allows workflows to be done and it reduced a lot
of friction. And we see a lot of companies in
customer like Canvas and Bricks. They are huge engineering organization
and they're like, investigate so much into MCP because they
want their developer to remain where they produce the most value,
which is in their idea, and so they are trying
to bring as many you know, ICP server and then
it doesn't matter if it's ICP. Actually, IBM really is
a competitive protocol which is called ACP, which does the
same thing. But you know, they're trying to bring all
the contexts and the context that engineers need to do
their work into the idea, and MCP is allowing just this.
Speaker 1: I think I'll be remissive. I didn't bring up Randall
Monroe's comic on the we have fourteen competing standards for this,
you know what, we need one universal standard to do this,
you know, and then time later we have fifteen competing
standards for this. I mean, because there really are. There's
like a Tobos came out with not long ago Smithy
for u h GDP services design pattern for documenting their APIs.
We had open API specification. It's on version three point
one right now, so that's you know, three versions later,
and there's a whole bunch of these that different companies use,
and I think the biggest trouble a lot of them
have is that, like we have open API specification for authors,
is that even if getting a human to understand what
was written there is quite challenging, and so like feeding
that into a model is you know, nonsensical, Like it's
just not going to get you the word. As you
pointed out, often the pattern is multiple things. I mean,
we have things like graph ql, which you know has
its own problems and whatnot. So I think we're just
going to keep seeing more of these and I don't
think we're ever going to really be able to settle
on one. It would be nice if we could have one.
The thing about MCP is, even if we pretend for
one moment, does the worst thing in the world. As
you pointed out, like I think Azure, GCP, and ABS
all released MCP servers for they're built in like AI products,
so you can interact with AWS better rock through an
MCP server and like, so irrelevant what you think about that.
It now exists and large companies have put some effort
behind it, and maybe they're just trying to capture some
of the market share and later things can evolve. I
do think that, especially if a lot of companies are
going to speed over quality that we may not get
for like that many more iterations of a protocol to work.
I mean, any I'll take this over the using sound
high frequencies to communicate between devices that have you know, LMS,
like I don't need that. It can go over the internet, please, Like,
that's where I'm comfortable with my security. I'm not comfortable
with things going through the airwaves because otherwise it's going
to be the Alexa. Please order me, you know, another
twenty four roll of toilet paper from an advertisement running
on my television and actually have it happen and like
this is recorded, that doesn't happen. Like, so I I
don't need that to happen. People will have this happening.
So I think MCP is still a little bit more
secure than some of these other protocols that are out there.
Speaker 2: It is. Yeah, you know again, I'm not you know,
I'm not an MCP evangelist. I think I'm not vouching
for the technology, but not the concept. I think there's
some serious limitation, a lot of issue with it. I
think one of the security I think we've already discussed.
We won't let that. But I think one issue is,
for instance, you spoke about open API, so you can
fit actually your open MPI and MCP can use this
as a reference, which is great because you know, if
you if your API is constantly updated with the latest
you know state and translated into an API, then you
make sure you're m CPCR is always up to date.
What we found out at Rutely is that because we
work with large corporations like LinkedIn, Canva and Cisco and
so on and so for, they have like very specific
requests in how they want to run their internet management.
So our API is very verbal. We have a lot
of routes to please our customer, and if you expose
all of this to MCP, it's going to get lost
in it, even though it's supposed you know to do this,
So you need to restrict the amount of route that
you expose. And the second thing is even at the
next level in the MCP server chain is within the
client in the editor like what people recommend, you can
have up to five, two ten MCP server. After that
your local agent is going to get lost because again
too much context. So you know that this technology is
I don't know if it's going to mature or something
is going to replace it. You know, then you need
to envision maybe something that centralized this MCP server into
the central hubs so you don't have to configure like
fifty of them. But I think it's on the right
track and I think we see adoption and but but yeah,
we will see where this move open AI recently and
now that they are supporting MCP, which you know is
is interesting because they're competing with Entropy. So yeah, I
think there will be more of this for sure.
Speaker 1: But I actually my pack at the end of the
episode will actually related to that. So I think it's
really interesting that you brought that up. Yeah, I mean
there's a lot. There's a lot there realistically, and I
don't like, unless you need it, you probably don't need
to spend any time looking at the MTP uh. You know,
it's highly specific here for for agents talking communicating with
each other. I think the hard problem that will get
to very quick is at scale, being concise and meaningful
and focused on what the business value is is going
to be even more important. And arguably it has always
been important, but it's very easy to add another route
to your open API specification or your you know, your
your web service or whatever you're running and having users
should be like, oh, they'll deal with it, right, they'll
deal with the problem. And I think realistically, you know,
you want to be as clear and concise about what
you're offering and what your business is and what the
product is offering, but still give your customer's freedom to
utilize your product how you want. And now you are
almost required to make it happen because of limited context
windows for for LMS, for agents. For MCP is going
to be even more a problem. I mean, you scared
me by saying two to five. I feel like if
you have any more than one, I think you really
have to question, you know, what the thing is that
you're fundamentally offering. I mean I do see platforms like
at LASTI ends, where like you have may have one
for Gyra and one for Confluent, you know, because that's
like a knowledge base, and there's like the day issues
and one for maybe the GIT server. Each one of
those could potentially be a different server. You say you're
not an evangelist, but you are the first person on
this episode, on this podcast to come on and say MTP,
so that I think, by definition makes you the evangelist.
And I think there may be a good a good
moment to switch over to PECKS. But before we do that,
I'll ask you, you know, is there any one last thing
that you want to share?
Speaker 2: Yeah, if you are curious about MCP, and you know
I've been to Cube con Asserica and the vast majority
of people still don't know about it. We are organizing
an event on April twenty fourth at guitub in San Francisco.
We love speaker from brother Bays, Entropic, Open the Eye, Guitub,
Factory I, and a lot of other companies. We have
demo and a panel. Well, we'll go over what the
heck is MCP and I think mab broady, you know,
as we would chart it, like where what does this
mean for the industry and where is this going? So yeah,
if you type m C P Rootly even guitub on
Google or perhaps we can share this in the description
of the episode.
Speaker 1: Yeah, for sure, they'll be a link. Okay, then I
think it's a great point to move on to our
our picks. Uh. So I'll go first. My pick is
this short article online by ed Zeitron. He has a
blog and it's called where the Money. Uh. It's he's
arguing that there's no AI revolution. Uh. You know, if
you look at companies like Anthropic and Open AI, they're
funneling tons of money in to it and they're not
getting the value out and so in a way they're
doing the nice thing of subsidizing all our great AI usage,
you know, so get it. While the fountain is going.
Really's got a great one, it seems you know there
are ones out there. Uh, it's just it's a really
great breakdown of you know, how companies are supposed to work,
how where the money is coming from, you know, where
it's being spent, and challenging some of those assumptions. So
if you are only optimistic about everything related to AI,
I highly recommend reading the article because there's there's a
bunch of really good points that are made that are
are hard to argue against.
Speaker 2: Love it. Yeah, that's an interesting question. Yeah, I think
the you know a gi and and you know the
goal of great getting to this great intelligence, so you
see that, you know, that's why the money is just bringing.
Speaker 1: Yeah, I mean there is this theory that basically we
can spend literally all of humanity's resources to achieve this
because once we have it, it will produce so much value.
That's you know, that theory hasn't been proven yet, but
I'll leave it to people to read the article. Who
he's articulated this much better than I have. Okay, so
you've got for us today.
Speaker 2: Well, I'm going it's going to be my pig that
I wrote, and I know it's going to be controversial,
which is why I want to share it even better.
You know, we spoke a lot about this. We didn't
speak a lot about this episode, but online everybody is
speaking about vibe coding, and so I think what's coming
for us SA is is incident vibing, because the amount
of incidents that is going to come our way is
going to probably going to increase. And more importantly, I
think a lot of the fundamentals that makes an engineering
organizations solid are going away. A few things. For instance,
I think a team that knows their code base very well,
it's kind of going away because humans are not doing
the coding anymore, right, they are merely like reading it
doing coveryview. Perhaps they will, you know, use another l LM,
another model to do the could review of another model.
But anyway, I think in general we know that the
knowledge of the code base is going to go down.
The other one is having matter experts in some fields,
especially as your company grow. You know, let's say maybe
you want someone with like very sharp on database or
website or whatever it is. And this again is going
away because of what I've just mentioned, but also because
I think it's going to be increasingly harder for young
professional to gain this experience and this flair that senior
engineer have. And so what's the solution. I think it's
incident vibing. And I think it's one of this story
where if you cannot beat them, you should join them.
And so in this article I speak about what some
of the ways that the companies can can get ready
with incident vibing.
Speaker 1: I love it, well, we'll share that like an OPEC
section of the episode. I mean, I I both love
and hate your pick honestly, because, like I am, I'm
so with you that vibe coding is terrible. And if
we look at the door Report or the episode we
did on the Door Report from twenty twenty four, we
see that the LAM sacrifice speed for quality. We also
know that there's a huge problem coming and companies are
still adopting it. So you have to live with the outcome,
Like even if you are using lams as best as
you can, you're gonna that means you're gonna get more incidents.
And so I'm totally with you. I hate that this
is happening, but Uh, there's no avoiding it. And so
the next level is also viving the incident resolution. Okay,
it is.
Speaker 2: And and we've seen companies, you know, hiring people engineer
and and they cannot cut, they only have, they can
only prompt And Yeah, whether you like it or it's,
it's happening, it's coming. It's the future of software engineering
in some capacity. And so I, you know, I just
think we need to get ready for it. That's the
only thing you can do.
Speaker 1: I mean, I love the per respective. You know, it
doesn't matter if you agree or disagree with with utilizing it.
It's it's happening. Uh. And that I'll say, thank you
Silvin so much for coming on this episode and sharing
your perspective and what really has I've been doing.
Speaker 2: Thank you very intriving me
Speaker 1: Yea, and thanks for all the listeners and viewers of
this podcast.