I'd love to really have like morning summaries, that are, that essentially read
to me by either a British or Australian accent AI that because I gotta have a
the killer feature.
Let's let's strip this.
I got a French accent over here.
I got a South African accent over here.
I want all the robots to have more personality so that I, I recognize their
voice when we're in a group call with nothing but me and the agents, and one
of them knows my infrastructure and one of them knows my GitHub situation and
One of them's cooking my, my breakfast downstairs.
I don't know what's gonna happen.
Welcome back to the Agentic DevOps podcast.
I am your host, Bret Fisher., I'm excited to talk about anyshift.io,
the co-founders Roxane Fischer and
Stephane Jourdan are creating a AI SRE, and I think I said at the beginning
of the year, I predicted somewhere around the beginning of the year that
in 2025, it was the year of agents, it was the year of Claude Code.
It was us figuring out how to move beyond chatbots and LSP tab completion
of AI to actually having conversations with AI and generating, through
agents, generating code, right?
And also generating YAML and HCL and markdown and all the
things that we do in DevOps.
But this is the year that we figure out context.
And Anyshift is an perfect example of that.
They're a year old, roughly a year old startup that is dealing with the context
problem of infrastructure in that if you want a current sense of the entire
infrastructure, and if you're gonna shove that into the context of an AI,
you're gonna need to do some things.
You're gonna need to have read only keys to a w, your cloud, AWS whatever.
You're gonna need to have access to GitHub.
You might need access to monitoring solutions, logging solutions.
Uh, you might need access to git ops and deployment solutions.
And you need to gather all that data together.
You need to create summaries and memories and basically a bunch of
tokens that you're gonna have to give the AI to help it understand what's
going on in infrastructure, because AI is coming for infrastructure too.
It's coming for DevOps and SREs and platform engineers.
And that's why we started this podcast a year ago was we, we kind of predicted
that it was coming for developers in code first because that, that was sort of the,
the nature of what the model companies were providing us was they were focusing
on code, but now people are taking that and figuring out how do we shove the
context of operations into the context window of these AI LLMs and get out some
usable data like troubleshooting, like predicting different sorts of outages
that might potentially happen soon.
And like how to ensure that we're fixing things so that errors
and issues don't happen again.
All the stuff that's the concern of SRE and operators.
So, uh, we dived in deep as basically we talk about the little bit of the
features and the, the reason why Anyshift exists, but I was much more interested
in how they're trying to solve the problems of context management, of
memory management, how do we avoid hallucinations, how do we protect
ourselves from any sort of mistakes?
And we get deep into all that in this episode.
So I am glad to have them on the show and let's get into it.
welcome to the show.
So on the right over there we've got Roxane Fischer, no relation, the CEO
and co-founder of Anyshift, anyshift.io.
And there in the middle.
Stephane Jourdan, I'm gonna, I'm horrible at my French accents.
I'm gonna try to do better next time, who's the CTO.
So these two co-founded Anyshift.
welcome to the show.
Thank for.
Yeah.
Roxane, so when did you all start this?
when, how, what's the born on date of Anyshift?
Ah, it's a long story.
let's say that the premises were two years ago when I met Stephane
in a small coffee in Paris.
We are both at the starting point where we wanted to create a new journey.
Stephane with his production background in AI and me with my AI background on a
deep tech problem to solve, and from day one, we knew we wanted to do something
about data and context, even before like the context engineering trend.
And that's how it started.
Nice.
Did you say coffee shop?
Very cute coffee shop in Paris.
So, Stephane, tell me what's the elevator pitch for, like, let's say
you're a platform engineer, or SRE type, you've got a managed production.
You've gotta maintain Kubernetes and the cloud stuff.
You're luckily not someone who's settled with this as a part-time
job amongst many other jobs.
Like you're a dedicated ops engineer.
What is Anyshift gonna do for me?
Yeah, so what I would say to this person is that in 2026, our jobs did
not become easier and they actually became like much more complex.
We are managing now, like probably much more services, everything, the
architectures became more complex.
We have like many different clusters, auto scalers, we have serverless things, we now
have like agents taking decisions for us.
So things became very, very complex.
Our teams did not, become larger, probably to the contrary actually.
And so we really need, like that knowledge.
We need to offload a lot of things to very capable agents or services.
And luckily, we have like a set of products that we can build,
a lot of things around knowledge and solving our problems and
managing the complexity for us.
Yeah, a year ago, I think maybe it was like a year and a half ago, I
started seeing companies showing up at KubeCon or just in the operator
space talking about managing agents.
And I was not, I was naive and I was new to all of it, like I, a lot of us,
but I was like, everybody was using this word agents and overloading it.
And I still feel like it's used for just talking about a dozen
different unrelated things.
But at the time I felt like there these companies that were letting us
host agents that we were gonna run in our infrastructure, run in production.
And maybe not managing production with it, but just agents running
somewhere in, in our infrastructure.
And then around the same time, probably even up to three years ago, at KubeCon,
we started hearing people talking about, even in the keynotes, I felt
like we were joking that the keynotes were all about AI when no one was
actually using a or running AI yet.
But they were really focused on the inference side and the model building
and managing GPUs and the sort of running of AI infrastructure, which I didn't
have, I didn't have a dog in that hunt.
Like I wasn't really doing any of that.
None of my customers or clients were managing their own GPU infrastructure.
They were just sort of using the SOTA models out there and all of the
APIs that everyone else provided.
And so it, I felt like it wasn't really until about a year ago that we started,
I started to see startups at the show that were saying, we're not here to
help you manage the AI infrastructure.
We're here to help you use AI to manage the infrastructure.
Which is a subtle difference of words, but completely separate jobs, right?
What gave you, what was the initial like, obviously it's hard.
I feel like this is a hard problem to solve, otherwise we wouldn't have
entire startups dedicated to it.
What is one of the biggest challenges to using besides just running your
AI and your local harness and having it fill out your HCL and YAML files
and, you know, executing CLI for you?
Like what's the hard part about it seeing my infrastructure and making
intelligent decisions about that?
I really believe it'll be the context.
It took us one year, even a little bit more than that to build this context
graph that is underneath Anyshift, which is a reconciliation between
dozens of different sources of data.
Today, where we really specialize would be how do we make one
unified source of truth, full time and history, of your context.
Context means cloud providers, Kubernetes, clusters cut basis infrastructure
as cloud basis, monitoring host.
And when you do, you need to do day-to-day task, related to your production,
you need to have the understanding of which configuration had an impact and
a blast radius impact on the service or like on this specific error.
Everything that happened full time and needs to be.
And so how do you manage to get these dependencies, this connection full time?
Today, there are a lot of like AI ops solutions, a lot of them are plugged
through different sources of data.
What was really hard for us, yeah, you can see this graph that we're building, is how
do you make the connection between those different universes, we call it universe.
here you can see AWS GitHub Kubernetes, to understand when you
make a change, what the blast radius.
When you need to deal with production at scale, this context is booming.
It's like 10 of billion of context token.
You cannot put that in one single LLM.
And also if you would put all this data, like imagine like you could put all this
data in one context window, you would only get the correlation between data sources.
But when you need, you need to deal with incidents or like make a change,
you don't need the correlation.
You need the causality.
And building this causality graph is what was really tough,
and to get some time to build.
Yeah, I feel like right now we've got developers very much focused on
the local harness and we, I just, in our Agentic DevOps Guild call this
week, we were talking about, like what goes in the repo now, right?
It seems like we're like, one of the early answers that we have is just dump a
bunch of markdown of every documentation, plans, intent, schemas, specs, like just,
it's all going in the repo with the code.
And I, I know that that's not really scalable for those of us on the ops side.
Like, we have things everywhere and multiple systems.
MCP isn't gonna solve all those issues for us.
And we don't all have, like, like there isn't a 10 million
token AI model that I'm aware of.
Like we, we don't have infinite abilities to just throw everything at an AI prompt
and say, okay, now figure all this out.
Right?
And so I think the problem isn't as well defined for those of us.
Like, there's not as much people besides as many people talking about solutions and
architectures on how they've solved this.
I expect that to probably be solved in the next, like, next year where we're
gonna see a, like a ramp up of a lot more content of people telling stories.
But even right now, I feel like there's pretty, there's not a lot of
stories out there of people talking about how exactly they used AI to
automate their infrastructure or to accelerate their troubleshooting
or to, you know, and reduce the number of pager duty midnight calls.
Right.
Like that kind of thing.
And I'm curious, like if you've already got customers, like what's the, what
are some of the, the callouts, what are some of the big stories that you can
talk about in terms of how this helped teams or gave them like an aha moment?
that's definitely, our customers, have different value in the different features.
One of the, one of the value is in gathering all this, complex data
very quickly, like in, in seconds.
There's this pager duty alert ringing and sure, as a human, as a trained
human, you are, I dunno, you are a senior SRE and you know all the things.
You've been there on the job for the past six years, for example, and great for you.
Everything is easy for you.
And in, I dunno, in 15 to 20 minutes, you can query the logs,
look at the Datadog dashboards.
You, you, you can query, you know, who created this pool request that
changed this specific thing, et cetera.
Guess what?
It's not the reality out there.
We, we don't, we are not always like senior engineers.
We don't know everything.
We probably have like this specific deployment late at night on a Friday
evening, and nobody knew about it.
And having all this complexity brought to you automatically by knowing the
changes, knowing the deployments, knowing the past errors that led to,
maybe a, a causal chain of problems.
The small, the, the slowly increasing rate of issues that, that was driving in
the end to another issue that actually, I dunno, created this database downtime
that in the end was the actual issue for your 500 errors and on the API.
And that's all of this is so complex today that having a system that really
gather all this information, for you and present it to you as a, as something,
that you can digest and they can, you can really process as a human and
you can just act upon it and resolve that, that problem very quickly.
Yeah, the complexity.
So, how am I interfacing?
This is a question I've been thinking about a friend of mine, Viktor Farcic,
who runs the DevOps Toolkit channel.
And we were talking a couple weeks ago, and he has this theory that
I think I still agree with, that we're gonna be so used to talking
with the chat bot in our harness.
Like we're essentially writing our writing e everything, whether it's code
or YAML or you know, documentation.
We're writing all that through our local harness and that that needs to
become the primary window to everything, rather than us having 20 different
chat bots in 20 different siloed places where none of like, one doesn't
know about the other and there's no shared context or shared memories.
Do you see that as like the future of like, is this plugged, is this
something where I can plug it in to my local harness so it can interface?
Or am I using this like through Slack or, I'm just like kinda thinking
what are the different ways that I can interface with this system as a,
the human to AI interface, I guess?
Yeah.
I'm smiling a lot, so let me take this one because like I, I
have the conversation very often.
today you can use, our like Anyshift for various forms.
We have a CLI, we have an MCP, a web app, and a Slack app.
And it really depends on your needs.
The web app, you can plot some diagrams about your infrastructure.
You can create reports about anomalies and proactively go
fetch from information for you.
You can view the root cause analysis being performed live
for live graph of hypothesis with all the fact that we gathered.
You are a visual person.
But I need to say that I guess our poor users really love us in the CLI and MCP.
People love Claude Code and we love it too.
And so like how do we bring this context into your day-to-day workflow?
As you were saying, what's kind of the future like, are we still like
in a person to agent interface?
So like, I am a person who works in, and I'm going to ask in the chat bot
on one specific product, on a web app a question, or do I want my agent to
actually interface with another agent to get this context to perform a task?
And this is what we see at the moment and how things evolve.
We have some customers who asked us to have the agent to agent protocol, like how
does any, so our agents who has the entire context about the different deployments,
commits, all the changes full time?
So this agent that has a context to decide how to trigger the
Dynatrace, agent, for instance, to be the master of other agents.
I don't know how fast this transition from like completely automated agent
to agent will happen, but there, there are some automat automation
happening already, and so we need to be at different places depending of
different needs to provide this context to the different like agents arguments.
So yeah, it's like meet you where you are.
If you're used to using the website dashboard, if that's
your thing, you can do that.
If you're someone who wants to use a CLI locally, I guess I, I would maybe
like, I'd create a skill or something that would know about the CLI, so that
I could just tell my local harness, Hey, go find this information on Anyshift.
Is that kind of the, the idea there with the, the CLI?
Exactly.
Yeah.
'cause that, that maybe we haven't stated that on the show before, but
I feel like in 2026, a lot of what we were trying to use MCP for a year ago,
you know, when it was the new hotness and everybody was MCP-ing everything
and we had way too many MCP tools.
I think at some point I looked at my copilot inside or I looked at my VS
Code, which all the extensions are still now shipping with MCP tools and
I was tinkering around with Copilot and I just clicked on the MCP tools and I
think I had like 150 tools loaded in my context, and of which none I actually
put in myself or knew about because they all came with the VS Code extensions.
And so then I had to like basically uncheck them all because I, I didn't
realize they were all filling up my context window essentially.
And now it feels like we're leaning towards, you know, actually the
AI's really good at just having bash like, just give it bash, tell it
where some tools are maybe CLI tools and that's the way it can access.
So I've completely shifted my workflow.
I don't know if that's what everybody's doing, but a lot of things that I was
trying to use MCP for before, I'm now just f downloading the CLI tool and
making sure that the AI knows that it can use it when I'm asking it a question
rather than expecting it to use some MCP.
It, we just recently had, I think Google launched their Workspace CLI,
so now I can have it access my email and my Google Docs all through a CLI.
And that kind of for me now becomes the default, first thing I do is if I'm
looking at a tool, if I'm signing up for something like Anyshift, whether
it's AI or not, I'm looking for them to have a CLI, so I, I can give that to my
harness to, to do the work rather than trying to find out if they have an MCP.
Do you see that same trend with your customers and you're part of the industry?
Yeah, yeah.
One, one of them, I have in mind one, one specific customer.
He's supercharged his workflow, he has so much to do, many, many
different products to manage.
So many problems to tackle with such a small team.
So he supercharged himself with all the different CLIs he could use.
He created skills very dedicated to his way of working, et cetera.
And it's gathering every single bit of information of context that
he can bring to his workstation through his Claude Code, so he can
like, tackle more, pro more, yeah, problems, project products, et cetera.
So that's really something.
One, one case.
We, we have, so it's, it's huge, for him on the laptop and another use case,
we, we actually learned, recently from one of our largest customers as well.
they actually use the MCP to build something around the Anyshift MCP
so that, that knowledge, all that information they use it to, to create
something very specific for them.
So like it's super agent based on the, that source of information and it's
central to help them fix problems earlier.
Because in the end, when you are managing product and systems, you really need to
solve the problems as fast as possible.
And today it's, it's possible if you have the right information at the right moment.
And just querying logs right now, it's probably not gonna help you.
It's probably something that happened like five years ago, or
five days ago, or five hours ago.
That was the culprit.
So let me get, let me make sure I understand.
So you're saying they built their own custom agent running somewhere
and it has access to your MCP, is that what you're saying?
And so that agent is pulling data out on its own from your graph.
Yeah, and I think I read on your website that your graph, like you're
doing this all read only, right?
Like these are, these have read only credentials.
We're not really talking about this thing going and tearing down my infrastructure
automatically for me, right?
Like, this is something that's really meant for investigation and analysis
rather than command and control.
Is that the goal here with Anyshift this just
well, well, well,
taking?
Well, let's roadmap.
actually, we were probably too cautious.
I was probably the, the most cautious, between Roxane and, and I, and I was
like, no, you can't, you, you can't do like, destructive things so, so early.
And actually, our customers are, asking for it now, as, as really like they want,
they ask for like, okay, we have this specific runbook, we have this issue.
It's waking up like every other Tuesday at 3:00 AM and we know that when this
rings, we have to do this action, and we have no way of knowing when it's gonna
ring, but it's gonna ring at 2:00 AM.
If Any could just do this specific run book, this action right now, it
would save one guy's night's sleep.
And so we are actually working on making Any do actions, because people
are actually ready, like it's April 2026 and people are ready for having
agents do actions on their production systems under control, obviously.
But it was the same when you hired someone new in your team.
Like, you want them to be ready as fast as possible and with all that information and
you can do pretty accurate things already.
Yeah, actually by the time this podcast comes out, the Mendral one with Sam
Alba will come out because we recorded that, I think last week, and we had
a similar conversation where I wanted him, I'm using their product to help
manage my GitHub and automate some of the toil of GitHub management.
And they didn't have any, right, they didn't have any, it wasn't
taking actions yet, right?
It was just incident reporting and analysis.
And then would, give you sort of.
The ability for it to do something, but you had to approve, right?
You had to let it, you had to go in and read and make sure you wanted to do that
thing, and then it would go do that thing.
And the, and the thing it was doing at most was a PR, like, it wasn't
even, it wasn't even committing to main or anything like that.
And I wanted him to go faster.
I was like, when are we gonna have the ability for it to just auto fix, auto fix?
And I feel like there's, like, I'm imagining this as sort of, like a linear
progression, a maturity model that's really just this line that I'm dragging
on one side, it starts in the blue, and then as I drag it to the right,
it's going redder and redder to like, basically to the end is YOLO mode.
And I feel like all of us over the last six months have gone through that similar
process with learning Claude Code and some of the other ag agentic, harnesses where
I know enough people now that I know some people that are very much in full safety,
they still want copilot to ask them about every command before it does it.
And then there's others that are going full off on safety
and they just let it run.
I mean, there's obviously the OpenClaw people that are just,
you know, a whole nother level.
But everyone is in their own comfort level and I tend to find that, we
all just like a junior engineer, we all need to trust the model and the
harness, and we're just gonna call that the AI agent, and we need to trust
it, and so we just need time with it.
We just need to watch it not hallucinate for months on end or weeks on end before
we're willing to give it more work.
And then we give it a little bit more, and then we maybe adjust our skills
and our Claude file or whatever we're doing, and we get, we get a little bit
better and we realize this thing hasn't made a mistake in a couple of weeks
other than just, you know, like little innocent mistakes that a human would
make that are not even really mistakes.
They're just a different choice than what I would make.
And eventually we get to that point where we're like, okay, I'm turning off safety.
I don't even care about sandboxing.
I trust this thing implicitly.
You know, go Terraform apply.
And, and I feel like that is, eventually, I think, gonna happen to most or all
of us, if at least we're allowed to by organizations, obviously certain
organizations are gonna put a lot of re restraint and restriction on that.
But I feel like we're all on that path somewhere.
So it's somewhere on that.
I need to just have a graph I put up on screen and just
basically allow people to choose.
Where are you?
I just wanted to speak about trust because like you're mentioning like how
do you progressively trust the agent from like, doing more and more action.
And this is something which really believe as well, like you need to give like human,
and human in the loop as a capability to understand exactly what the agent did,
what type of queries this agent performed to like build this progressive trust.
And like later give the agents like the capabilities of making actions.
So like how do you actually like exactly, with like a junior SAV joining
your team, you can of understand the progress and the type of capabilities
this person is doing to then allow widely selection, pull requests from
being done and then like complete out the loop kind of capabilities.
Yeah.
The, to me the auditing is like, really, it feels really important.
Like if I'm having a, if I'm having a robot take actions on my behalf,
it's I have lots of things I think I want, and I think that's all gonna
completely change the minute I actually start using systems, like Anyshift,
because I, what I perceive to be what I wanted to do, you know, I wanted to
have like read only credentials until I manually granted right in the moment.
And then I wanted to swap out tokens or, or keys so that it has the right pat now.
And then I then kinda like we have build and plan mode locally.
Like I, I kind of want that in my infrastructure where in
certain moments I'm, for certain activities I'm gonna give it.
Right, but other ones, it doesn't have that ability or doesn't even
have access to Kubernetes API.
Or if it does, it's read only.
Like, I'm just sort of imagining how I'm gonna slowly onboard this thing.
One of the ideas I was talking with Sam about was the idea that, if things are
recurring or recurring types of incidents where it's a similar type of problem
or maybe the, the tool that it needs to fix is something that I've, I've already
approved for, right, or permission for it to actually do something on, on a
right access that it, it now knows that there are certain things it's allowed to
do and I've given it, we've established those permissions that it can have.
And then there's other things that it's still not ready for.
And we're, and maybe as a team we're not ready for, it may be because
we have crappy documentation, so it doesn't have good context.
It might be because we haven't given it full read access to all that
particular part of the infrastructure.
Maybe it's hybrid and, you know, there's missing components, so it tends to make
the wrong decisions or hallucinate.
Do you see that spectrum happening in Anyshift?
Is it, is it gonna be like this messy world of like some things are this and
some things are that, or is it just gonna be like an on and off toggle?
How do you see that sort of happening.
a great question.
Like we really see like Anyshift as a new member that you're onboard within
the company, you give this person some accesses and depending of the
accesses this person have, she or he will be able to debug an incident or
like to perform a day-to-day task.
It's also like super important to mention that when we speak about
context, you need to have memory as well.
So our agents, all have like self reinforcement memory.
They will learn from past patterns, past incidents.
All the data, all, everything that they have seen very similarly to
someone, new team that begins and then will evolve within the team.
it'll learn that day one, oh, I thought like this is an incident because like
twice a day I have huge scaling events.
And two days later actually I understand that this is a normal behavior because
this is a pattern of the production of the company I'm working at.
That's the type of context that you need to have and that our agents have
access to, to be able to learn from connection that you only find at one time.
We spoke about the graph that we're building.
So this is like a time machine of your production, all resource and
dependencies, how things connect.
But sometimes you don't have all this connection, all this data from just
like, integration and like raw hard data.
Some of those connection can only be found at one time.
Like how does the service pick another one?
We learn it as well through the memory at one time will
upgrade the memory of our agents.
They will do it by themself.
And this also is part of the context we're building.
The resources, all the dependencies, all the changes that you need to have
between dozen of fragmented tools, Datadog, AWS, Kubernetes, like, many ones.
How do you bring back this context in one unified view to debug incidents
and also like prevent some of them?
Prevention also like as important than as to just react on the fire.
I wanna ask about prevention, but before I do, the concept of memory is something
that I actually spent a lot of time in the last week kind of understanding
where the industry is going and like even for local harnesses now, I just,
I think it was yesterday actually, I learned that Claude was explaining to me.
I basically asked it list to me all the harnesses that you can find and whether or
not they have built-in memory components, you know, or features inside them.
And it was like it listed, Claude Code has it and Copilot has it now that where,
like Claude Code stores it, it's very, it's a, I think it's right now, I don't
know about team accounts, but I know it, it stores it for you individually
and they have this new auto dream thing that also enhances memories over time.
And for those of those not familiar with this stuff, I think we're kind
of all centering around the idea.
We understand context, we understand like skills and the agent files and sort
of these different parts of the puzzle.
But this memory, this thing we're labeling as memory to me is about
summarizing previous conversations or previous sessions with an agent
and storing those long term so that they can be whether basically
searched and injected dynamically.
I think they're all kind of doing something a little bit different
on how exactly they bring memories into the current conversation.
And this is just, to me, a natural extension of how we're going to take
this beyond the current session.
I've been writing a newsletter this week around the bad habit that I, I see myself
doing and others doing, where we start a conversation with an agent and we just
continue that conversation rather than starting a new one because there's so much
information locked up in that session that becomes almost like an AI tribal knowledge
at that point because it's only in there.
My team doesn't have it.
My other AI models certainly don't have it.
My other sessions don't even have it, and it's only in that session.
And so I, it's like, it's like a precious little snowflake of a conversation,
and that I need to have like almost a personal discipline to somehow get that
into, whether it's the agent's file or documentation in the repo or something
else where the agent, the future conversations can access those memories.
It turns out that maybe all the tools are gonna manage this for us, and
it's all gonna be eventually a solved problem where maybe there'll be central
memories for the team somewhere.
I'm, I'm not sure how it's all gonna shape out in terms of just the harnesses,
but it's cool that you're doing that.
I know that Mendral with Sam, they're also doing the same thing where they're
adding memories that, they're incidents of creating memories, but also you
can add in your own memories, which I almost, I was telling 'em like,
this is the tribal knowledge area.
This is where I'm just typing in, in my case, for him it was, I needed,
I specifically want GitHub Actions, reusable actions to not pin the digest
if the, this is very technical, but the calling action and the reusable
action, if they're both managed by me, I don't need to pin one to the other.
I just need to have the reusable action pinning any actions it's using.
I know that's, that's if you're not using enough actions, that
just sounded like gibberish.
But it was something where I have a very specific workflow for me
and, and the people that I work with, and that's how we do it.
But the linters and the sort of LLMs think that there's another
way that they should be doing it.
And I need to give it very explicit instructions that it maybe won't
be seeing because not all these tools are looking at a repo, right?
They're not all harnesses like these SRE tools and these GitOps, these,
DevOps tools aren't necessarily looking in repo for instructions.
So where does it find all of the lore of my infrastructure?
All of the preferences that we made over time, the architecture
decisions that we made over time.
Are, I'm assuming that you have a bunch of a series of plugins or a series of
integrations, that aren't just looking at my, your infrastructure today, but
are also maybe looking at like, are you looking at Linear, at Confluence, at Jira?
Like does it pull that kind of information as well?
It's even better.
I will let Roxane be more specific maybe about the mechanism of the
memory itself, like she is much more, compared than me on this one.
but something that Anyshift does.
So, yeah, you're right.
If you give, any access to your Confluence, your Jira, et cetera,
it'll crunch all that data.
She will learn about all, all that data she'll learn as well, about
all the different chats you had.
And if, if you tell, Annie, for example, during a chat, oh, you
didn't know that this service was actually connecting to Redis, then
she will, remember that, This service is actually using Redis as a backend.
So maybe next time you want to deprecate this Redis to, to switch to Val Key.
and you can ask any, oh, is it safe to remove this Redis like no other
system, I know of, are not using it?
And she can answer you, oh, but I know about this specific s service that
is still using it because, I dunno, your colleague told me that it was
still using it like two weeks ago.
And that's the type of things that Annie has for the memory.
And she also does something like super cool.
She spends 24 hours a day exploring the graph, exploring your infrastructures,
exploring your logs, exploring your metrics, exploring new data,
exploring your new Jira tickets, your Linear issues, et cetera.
And she build her own memory, her own feelings about, maybe not feelings, but
but feel like feeling sus.
Yeah.
yeah.
But yeah, she creates some sense of things happening how?
Half a hour, like 24 hours a day.
And she builds internal reports for her and when she explore something,
let's say, there's an incident and she explores different hypothesis.
And the, during one exploration, she discovers something new,
something she didn't know.
She will automatically remember this.
And if this specific thing she discovered during an investigation
that was useless at that moment, but maybe tomorrow you have a, an issue
and this is the root cause, then in seconds she will remember it.
I cannot do this as a human.
Like I cannot remember all the crap I'm saying like, every day
by, exploring the logs and stuff.
So it's, it's really, it's, it's not even about tribal knowledge.
It's really about like being exploring 24 hours a day.
So, I don't know, Roxane, if you want to tell more about the memory,
feature we have, like how it works and.
Yes, the memory is like still a hot topic and it's hard to handle
because you need to understand like what is useful and what is not.
Right.
You can't put every memory of every situation all day long in,
in the context window, right?
Like you've gotta have some sort of searching or, I'm guessing
it also summarizes them so that they're smaller and they can fit in.
Exactly.
So we have like different mechanism.
So we could publish, like if you want to read on our blog, like it's a
state of the art with like Stanford, we're super proud about it, how our
agents actually learned and able to implement the self enforcement learning
for like the latest research paper.
And the thing is, so what it's gonna work is that when you see something,
let's try to compare it to a human.
New data on new information, you're first going to reflect.
So this is called the reflector on what you've just seen,
and do you know it already?
And you're going to try also to know is it something that is useful?
So this is the first phase that the agent will do by itself.
And the second one, which is, do I have some memory for that?
And where do I put it?
And so how do I update my memory in a structured way?
Because very often today, like memory can just be markdown files, but like
with just information all over the place, and you don't know where to
fetch it and what's the most important.
So this mechanism is how do you actually rank the information?
It'll be done automatically by the agents, but so how do you update and
fetch the right memory at the right time?
The second thing which is tricky, is how do you handle
short term and long term memory?
You have short term like preferences, things you have seen yesterday.
I want to remember it.
But you have also like long-term memory, like very rare incident, very rare
pattern that you want to have somewhere in your brain, kind of a cold storage,
that you want to store somewhere.
And that if at some point it gets really tricky, you want to go as well
fast to the long-term memory, this cold one, to be able to find the right
context and then to solve the incident or to answer to a tough question.
So that would be the kind of principle in place.
I can go deeper if you want.
Yeah.
but
it's,
I was gonna say like, that sounds like that's like the magic of a SaaS, right?
Like that, that there's a hard problem.
And this kinda reminds me of like the things that people aren't talking
about is, okay, sure, I can give it access to infrastructure and have
it go look at the Kubernetes API.
Like I can have any model go do that.
I can tell it, it has access to kubectl and Terraform, and it can look at things
in that moment, and it might even be able to look at APIs and live infrastructure.
But capturing the history of all of our Jira tickets and all of
our pager duty outages and the Slack channel conversations.
Like when I think about the big picture of, we're really trying
to just replace engineers, right?
At the end of the day, we're trying to have like a, not necessarily
replace people, but we're trying to, we're trying to have an additional
AI buddy engineer that's going to replace all the things that I forgot.
And in this world where nobody keeps the same job for more than a year
or two, it's really hard to maintain all that team knowledge, that tribal
knowledge that isn't maybe documented.
And none of us really enjoy writing, handwriting, documentation.
I think I speak, I use one of the whisper apps.
I don't have to even type as much of this stuff anymore when I'm just
sort of going off on a tangent.
But we have this, we had this, I have this theory that like, if these things
are gonna eventually be someone who is just as smart as anyone else in my team
in terms of knowing the current state of things, the current plans of the
company, the current budget restrictions, the current, you know, what do we just
talk about in the standup this week?
That at some point these AIs are either gonna have to be connected in, either
it's a two A or something, or we're like, they're gonna need to be in the
meetings, they're gonna need to know, have the planning documents, they're
gonna need to know the new budget restrictions that we have on cloud
compute or whatever in the company.
Like, they're gonna need to know all these things so that they can
make these decisions in real time.
Like, oh, well, you know, we have a capacity issue, so I'm gonna spend up
some new servers, but I also have a budget concern, so I'm gonna use Arm,
you know, or whatever to stay cheaper.
Or I'm gonna, I'm gonna reduce my capacity over here so like an increased capacity
over here, 'cause this is optional and I can slow that down for later.
'cause it's just job management or whatever.
Like, it's gonna need a ton of context about the business, because
that's when I walk into teams, that's what's happening in real
time, is people are making decisions.
It's not docu, it might be documented in an email or a Slack message or
in a document from a Zoom call, but that's not in my infrastructure.
That's not in my Terraform plan.
So how does this agent, you know, maintain the intelligence that another team
member would maintain in the real world?
So I'm guessing that you're thinking that far out.
Maybe you're thinking like a couple years in the future when all this stuff
is there, but you did mention already earlier, we don't have to go back over
it again, but like, the whole idea of agents talking to agents, and maybe
you have, you, maybe you have like a meetings and email agent or something
where you're literally CCing some sort of agent that's digesting emails so that it,
you know, so the epic long email threads or slack threads are all being assessed
in some sort of agent over here, but then that agent over there is managing
infrastructure and has the context of real current status of infrastructure.
And the two have to meet some in some way so that your decisions
are made in with the same awareness that a human would have that week.
I don't know if you're thinking of those kind of hard problems that far out,
but that's something where I'm trying to imagine how we're gonna get there.
Like, how do, how do, I'm not the smart one that's gonna figure it out, but
I'm just imagining that's the problem.
I'm so excited about that because it's really like our vision, that context
when you speak about production, and let's speak about only production,
it's not only like infrastructure data.
You also need to have like security kind of integration,
with for instance, identity and ownership, business data as well.
And when you connect all of this data together, you cannot
just like call different MCP and APIs to get this context.
I cannot insist on the, of the fact that there's a difference
between correlation and causality.
When you need to make a decision, like either, like to understand what
is the root cause of an incident, I like to make a change and understand
like the best pages you need to have the causality of events.
How, what is the source and what is the symptom?
And this data, if you just take it as MCP or APIs, it's only
going to be like correlation between different data sources.
And when we speak about context in production, you need to have
the causality of event full time, so that's the tough part.
And not only for like Kubernetes, AWS and code basis, but also for
Slack, for Jira, for conferences, Okta, duetta, like everything that
makes your job like daily job kind of interesting in terms of the context.
You need to gather and connected for nodes and dependencies.
And this is the type of context we're excited to build.
It's not something that you want to build yourself.
You want to be able to make some actions or to make decisions based on that.
We really like focus on the context.
How does this context look like?
Yeah, so I guess at the end of the day, the, Annie, right?
Annie is the name of the AI at Anyshift.
Like, Annie's working on the causation while I'm sleeping, hopefully.
Like looking at the repetitive incident a decade ago, over a decade ago, I was
working, with a platform that was kind of like a Netflix platform where they were
Argo CD in a video all around the world.
And we had, and I was managing, this is like pre Terraform, so we were using Salt
Stack, I think, and we were doing, using a lot of cloud formation and we had a bug.
we weren't, we were the ops.
We were the ops team, so we weren't writing the code and the code, I think
it was PHP code, had a a memory leak so that we knew about once a day, this
certain series of servers all around the world, were gonna need to be restarted.
And we didn't really have full like connection failover, so it needed
to be, you know, ideally in that part of the world in an off hour.
And so we had to come up with this whole plan.
And so for the longest time it was just human toil.
It was whoever was on, whoever was available in the team at that moment was
gonna be the one that was gonna have to kick off a job to, for that part of the
world and those regions to recycle all these servers and basically reboot them.
That was the strategy for like three months, that was the strategy, because
we were waiting on this supposed magic p magic PHP fix that was gonna fix this bug.
And this is the kind of thing where I feel like this is the kind of thing that
one, the AI should be just detecting, like researching the problem and, and finding
the fix much faster than a human would.
And two, like I should just be able to tell some, you know, SRE bot that,
hey, this is the problem, automatically create me a schedule of recycling
these servers, until we can get the fix that some other AI is gonna fix.
'cause maybe the developers have their own, you know, their own Claude
and they're gonna, they're gonna implement a fix eventually with that.
But I need this solution now, and the solution means someone's got
to cron job this out worldwide with all these different regions.
And that seems like a plausible job that I just wanna give an AI, I do not
want to have to get up in the middle of the night and reboot servers myself.
And since I can't, maybe I don't have an automation system yet,
like, an Argo workflows or something that can automatically schedule and
spin things up and reboot things.
So, I'm excited to see how this progresses in terms of your AI being able to do,
you know, do operations on my behalf.
And maybe it's deterministic, I think you mentioned earlier, you were talking
about a script that one of your customers needed to run, and that's also something
recently that I've been seeing more people experiment with as essentially
using the AI, the non-deterministic, crazy texting robot to write a
deterministic workflow that it executes.
And then eventually, you know, I think for those of us in ops, like we, we are
scared of the non-determinism because we feel like that's just a crazy, no,
not letting a crazy bot loose in my infrastructure tore wreak havoc, havoc.
But if I can get the AI to write a deterministic program or workflow or
something and then implement that, that reduces, I think, I feel like
the risk of the AI, because the AI is not deciding that today I'm
gonna do these steps out of order.
Because that might happen, but if it can write the code or write a
deterministic workflow for me, that feels like the right thing to do.
That feels like something a human would do.
And I, I don't know if you're seeing those patterns, of like people are
using Anyshift to detect the problem and then they're, they're not using
AI to automate or solve the problem, they're may be using AI to write the
deterministic, fix or deterministic workflow to fix that problem.
Do you, you gave that example earlier, but I was just curious if you had any stories
on how that is continuing to happen or if that's your strategy or you, how you
recommend this to people to implement.
Yeah.
We actually have.
We had a very nice story, maybe it was two weeks ago, with that one customer.
He so that customer got a report by Annie saying, oh, I'm, I discovered on this
a account, like a lot of EBS snapshots, and it amounts to a lot of money.
And that, that customer didn't stop here.
He asked Annie, okay, gimme a plan.
Like, can we do cold storage?
Can you suggest you have access to our Terraform stuff, so can you
just help us just do it like now?
And so it came, it started, by a report, and it ended up just in in a matter
of, minutes, to having a full fledged solution, like a lot of money saved
and a much better backup plan in place.
So it's really, it's really about like yeah, exchanging, talking with your
agent and just having that agent the right context to take the proper decision
and help you in your already, hard job.
Nice.
I feel like we could talk about this forever, but we, because I'm super
interested in like the patterns in the future of how, how we're gonna
interface with this AI, how we're gonna keep this AI on the rails, like how
we're gonna keep it safe in production.
I think there's a lot of people that are concerned about that.
And I think the, I'm glad to get this episode out because I feel
like, you know, getting the word out on how companies are implementing
these technologies in a reliable, reproducible, safe way, so that we're not
constantly tearing down infrastructure.
You know, we all see like the hacker news story of someone who
had the AI run Terraform apply and had decided to delete everything
before it ran it again or whatever.
That was a recent one.
And we see those and I think sometimes some of us were like, that's what you get.
That's what you get for doing that with the AI.
Like, shame, shame on you, you shouldn't have done that.
But the reality is this stuff is coming and people are experimenting
now, like you said, you've got some customers that are like, we want to
go full on, like, give us the right access, give us the implementation.
So I think it's definitely coming and it's exciting to talk about the different
models, not the AI models, but the different workflow models of how we're
gonna do this together and safely, and how these kind of tools are gonna integrate
with the rest of our infrastructure and the rest of our AI agents.
What's, what's next?
Like, gimme, gimme a hot take.
What's, what's the next thing that you're gonna, you're excited about?
If I take this one and I think like we have different excitement.
Stephane and I,
Okay.
I would say like in term of context, how we make it like grow even more.
So like we want it into much more information for the teams to like, have
everything at hand to perform a task.
That's, we're going to add like quiz, Notion, like as notes in the
graph and people are asking for it.
I'm excited about like how big we grow in term of context.
Yeah.
A giant, giant graph.
Excited about the giant graph as well, but I would like, like more agentic
Annie, I want Annie to do things.
I want Annie to proactively like, do things.
I will, I want my, I, I want AGI, I want to sleep at night.
So I want the, I want Annie to detect problems, fix problems, and just
send me an email tomorrow morning.
That's, that's the shot at roadmap.
I'm really excited about this.
Yeah.
I do like the idea of like getting up in the morning, I'm brushing my teeth
and I've got my Annie app on my phone, and we're having a voice chat, kinda
like, I just did the, this morning with ChatGPT, where we're having a
conversation and I'm like, okay, what happened overnight while I was sleeping?
Like, gimme, gimme, like, did we have unusual spikes in traffic?
Did we have any sort of hiccups?
Was there any cloud outages while I was sleeping?
You know, like we all wake up and GitHub's not running today.
You know, that's the kind of world that we live in right now.
And instead of me having to like peruse Hacker news or check status pages
of things or, you know, everything might be fine right now, but there
might have been a ton of stuff that happened in the last 10 hours.
And instead of me having to read Slack, like just infinite slack messages and
channels that just are giving me alert fatigue all day long, I'd love to
really have, like morning summaries, that are, that essentially read to
me by either a British or Australian accent AI that because I gotta have a
the killer feature.
Let's let's strip this.
I got a French accent over here.
I got a South African accent over here.
Like, I just, I want all the, I want all the robots to have more personality
so that I, I recognize their voice when we're in a group call with nothing but me
and the agents, and one of them knows my infrastructure and one of them knows my
GitHub situation and, you know, whatever.
One of them's cooking my breakfast downstairs.
I don't know what's gonna happen.
But it's exciting to see this future unfolding in front of us.
And I think, the last thing here is like, when is that a year from now?
is that five years from now?
Like where do you, can you even imagine where Anyshift is gonna be in a year?
Do you even have a vision board of where this is all going in a year?
I don't know how fast the transition from agent to agent will happen.
For sure, like today, like incident management and resolution
is still an unsolved problem.
For instance, it'll not be manual in the future.
Six now, six months, a year from now, it should be solved.
And like how agents actually also like perform action in production
should be something that I believe in the six months, a year from
that, will, will be done as like the models get so much better.
We made a bet from day one that we would be the best type context
and that agents will improve.
And we have seen the leap of like, progress, like Sonet 4.6, for instance.
It's only gonna get better and we are the best at providing this
causality chain between events.
And then our agent will be able to be the best to solve it with
the latest models improving.
Yeah.
A new model to me.
I mean, I feel like we're all in the lull right now waiting
for the, waiting for whoever's gonna release their next version.
And it feels like right now, every time we have a major Sota model release a
new version, it makes things that we thought we needed to do in our tooling,
irrelevant, because the model can now do that now, or, you know, the model's
been updated so that I don't need that.
I don't need for it to read the documentation every time because now
it's been trained on that documentation.
before it was, a lot of the models I think we're using now, were built,
were built in September last year.
So, a lot of the tools I'm using barely even exist.
I mean, OpenClaw wasn't even a thing back then.
Like, there's so many things.
Docker sandboxes didn't exist.
Like, we have so many new things that, that models don't know about.
So I have to constantly point it to websites, give it documentation.
So I'm excited for that future too.
And and it looks like, even though I can't predict what's gonna happen
in six months, I feel like the,
it's all gonna be amazing and I'm here for it.
So that's why we had these podcasts.
it's great to have you on the, podcast and I'm looking
forward to what you all do next.
Thank you so much.
Thank you so much,
Alright.
You can find Anyshift@anyshift.io.
I'm assuming both of you, should people follow you on LinkedIn?
I guess where are the socials that people should find you all?
Twitter and LinkedIn.
yeah.
Twitter, LinkedIn.
Nice.
All right.
Ciao everybody.
Thanks for joining us, and I'll see you in the next episode.