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The information economy has a ride. The world is teeming with innovation as new business models reinvent every industry industry. Inside Analysis is your source of information and insight about how to make the most of this exciting new era. Learn more and insight Analysis dot Comside Analysis dot com. And now here's your host, through Eric Kavanaugh.
All Right, folks, hello, and welcome back once again to the only nationally syndicated radio show all about the information economy. It's called Inside Analysis or truly Eric Kavanaugh is here and I'm very pleased to have Arun Baradarajohn. He is the founder and chief commercial officer for a company called Assendian, and we're going to talk about a new development at Paradigm,
So just to give some context for that. As anyone who has worked with professional services firms know, they typically make their money by the hour, and if you want to do something new, they're like, oh, we need more people, more money, got to check out the price, so we'll
make more money and give you new stuff. And then there's the other side of the equation, which is that a lot of times if you want to go faster or for less money, they're like, well, it's going to cost you in terms of quality, So you have to give up something either quality or price, money or ideas, like even don't even start your project, as maybe you can't afford it. And I think Arun has come up with a pretty clever way to address that, and it's an end to end engineering platform.
They figured out.
There are all these friction points one hundred and fifty I think is what I heard him say, friction points from idea to production. So what are those and how do you solve them while we live in a very interesting world these days where you can have teams work together on projects without holding each other up. I give an example all the time about how Google blindsided Microsoft with Google Docs allowing multiple people to work on the same document at the same time, which was an absolutely
brilliant innovation. Saves tremendous amounts of time and effort. You don't have to throw things over the wall anymore. You can also be looking at the same document, typing, editing, and the technology tracks who did what. So you have all these wonderful guardrails and safeguards built into the technology, and that's what you want. And I think they have something like that for a development platform. But with that,
I run, I'll throw it over to you. Tell us a bit about ascending and what are these friction points?
Absolutely so so, Eric, The Cindy On journey started about four years ago, and we had a group of us who came from the the overall engineering and software industry, and we had realized that there were fundamentally three issues that clients were facing. There was there was a crisis of trust, there was a crisis of speed, and what I call is a crisis of capital.
The crisis of trust was you.
Know what, these guys will show up at my door and say, hey, don't worry, I'll get this done for you.
This will be the cost.
I've got these accelerators, I've got these really good talent.
I can get this work done for you. And then they show up at the door and say that.
And then when the reality you know what do I say manifests, they find that, oh my god, I'm being charged more than what I was told.
You know, I'm not.
I don't have full transparency into how things are being done. I don't know what your engineers are doing. It's a black box. I have no clue. The second is a crisis of speed. Everybody comes and says, yeah, we will accelerate you. We will drive more more value to you. And then lo behold, when you say I want to accelerate, the first thing the service provider says is you need to add another hundred people right that cost.
And then listen.
One thing I can tell you is we add more people into a software engineering project, it actually slows you down.
It doesn't.
It doesn't increase your speed because you add more humans into the software process, which is very it's very nebulous, not superly defined. You will find that there are more errors creeping in and you're busy fixing those errors. Right, Yeah, So it's a it's actually a it's actually a negative move to add more people to projects. The third verse crisis of capital. Now, what is the crisis of capital?
Over the years, because of the first two issues, clients have been building software with a lot of technical problems, and we call it technical.
Debt, a lot of debt.
So what happens is your capital is locked on these legacy old code and you're spending a lot of money just maintaining it, and you don't have enough money to do new things and innovation.
And I call that the crisis a capital.
So all this was creating, in my opinion, a logjam and a guardian not as I call it. Okay, which the cord is yes, And I said, it's time to untangle this. So the thesis statement for Cyndion was, let's go back and understand what is causing this problem. And to me, I went back to the manufacturing world, and I saw how lean manufacturing transformed the entire manufacturing world.
Right.
Otherwise, earlier, if you had to do a line change, or a die change or a product change.
It took you days to do it.
You had issues with product quality, you had a whole bunch of things with lean manufacturing, where automation and to a large extent, AI fundamentally transform manufacturing. And I said, if manufacturing can do it, why are we lagging behind?
Why can't we do the same thing with software. So we went back to the drawing board and really looked at the entire engineering value chain, right from ideation through to production, through to even post production, and said, what is causing these what is causing this friction and problem? And to a large extent, I'm sorry to say that it is the humans.
Right.
If you give a human, if you give four humans one requirement saying hey, I want to do this very simple requirement. It could be a very two to three lines of description of what you want the software to do, and you give it to four different engineers, you will get four different code.
Bases for the requirement.
Sure you will also get ten different bugs that each of them will will come up with. And I said, this is ridiculous. It's time for us to change. So that's when Sindion we was started with the notion that we are going to build a platform. The platform is fundamentally going to have the ability to support the different actors in the engineering value chain, use machine learning and AI and try high degree of repeatability, standardization, and really get
the process. See if you look at the software process today, it is all in documents. With the platform, what we're doing is we're taking the process and instantiating it into a physical platform that will drive the process and ensure that there is standardization and scale. That is what we have and thanks to all the AI work that has been going on over the last two to three years, we've been able to take this to a different level and I'm happy to talk more about it.
Yeah.
Well, so standard components, that's one thing you can focus on, right, making making solutions modular. Maybe walk through what are some of the foundational components in the platform and maybe explain how it is. I'm just guessing here that developers can work on different parts of the process without causing each other trouble and without delaying each other, right, because that's one of the huge challenges is when one group has to wait for some other group to finish something and
then they get delayed. So now these guys are off the ranch basically just twiddling their thumbs.
You don't want that.
You want everybody working at the same time without disrupting each other. What are some of the component parts or buckets if you will, of development that you've isolated.
So we have gone even more radical, right, So our radical thinking is that why do we even need humans to actually do any of this? And we are thinking differently actually saying that. So if you look at our industry, we do different things for our lives. So sometimes we'll come in and build a new platform for a client where we have to take it from idea to production.
As you mentioned.
Earlier, all the client will say is and I have an old platform. I want you to modernize it. You know, the old platform could be written on something like Cobol or pl one, which is forty fifty years old old language, and they want to move it to a modern construct. So there are several such asks from our lives and my view was why can't I get AI to do
all of this? So today, if you look at our platform, our platform is what is known as an agent tick AI platform, So I literally have an agent that can do anything you wanted to do, and I can configure this agent to say, okay, agent read this. So a requirement in our world is called a user story, basically saying this is the story that you will present to the user. Right this, this is how the user will use that use the software.
So the user story is a very important input.
Now I have an agent that will read the user story and create the code. I have an agent that will read the user story and create the testing that needs to be done on the code and execute the testing.
Right.
So what we are doing is and with this, what we're fundamentally doing, Eric is now when you write a user story. If I'm a product manager, my job is to write a user story.
That's my role to write user stories.
But the funny thing is if I again, just like I mentioned earlier about developers or engineers, if I give somebody an input to write a user story, and the input isically typically.
Called an epic.
So if I give you an epic and I say, please write user stories for this epic, and if I give it to four different product managers, I will get the user stories written in four different ways and will not cover all of the needs of that epic. Because
the human can only think so much. Right, But what I'm doing with AI is I tell the AI A here's the epic, generate user stories, and I have live examples where I've taken I've done this time and motion study and comparison between what the human output is and
what the AI output is. Right, So when I give a user, when I give an EPIC to one of my AI agents and say generate user stories, it may generate fourteen fifteen user stories that cover all of the aspects of what the system needs to do in the first iteration, Whereas if I give that to a human, I sometimes see them coming up with seven or eight. They're not able to think about the edge, the edge conditions,
the limit. You know, when you think about software, you've got to think about all the eventualities and all the possibilities that you may have to encounter as a user, and many times as a human mind, we have these limitations. Right, that gets taken away, and then I can do this at scale instead of having you know, each time, let's say I need I have tons of epics, and I need to attack all these epics. I need to add
more product managers. Right here, I just add more agents, and I still need product managers because I will have them eyeball what the agent creates, taking the human away from the loop. But I'm fundamentally saying, hey, if you're if I had ten product managers doing the job, maybe I just need.
To m hm and more.
These product mats are really.
Improving the improving the agent's ability and efficacy to do this even better.
So if you don't mind my asking the the agents that you've designed, how what what language did you.
Use to design them?
How do they run?
They do they run in containers? Is this a containerized environment? Tell me a bit about that.
So our platform is completely containerized. Okay, follow all the modern methods. It's a platform. So our platform allows you to create agents, and I can create an agent for different use cases. So somebody may come to me, Like one of my clients came to me the other day and said, listen, I've got these Pearl scripts. Now peerl is again one of the old scripting languages that nobody uses anyone, right, So he said, I've got Pearl scripts. I really want to convert them into Java. And he said,
I'm trying to get my engineers to do it. It's taking way too much time. And I've got tons of Pearl scripts. I've got these four hundred applications running in my data center and I want to move them to the cloud, and I can't really use PELS scripts if I move into the cloud. I want to move it
to the new language Java. In the old days, what we would have done is we would have tried to use these code converters that will take the peerl scripts and converted into whatever language that is not working for us anymore because we want to move to micro services and containized component based applications. What we're doing is we
have changed the process. So I have an agent which goes and interestingly I put two of my principal engineers with our platform and we were able to get this done in weeks for our clients who actually took months to try and do this. So what we did was we had what is known as a reverse engineering agent that came in and read the Pearl scripts and understood. And these Pearl scripts can be very messy. They can
have routines. I mean, I'm not going to get super I don't know how technical I can get here, but fundamentally, when you have these scripts, these they call each other there's a lot of linkages here and there. Right, So we actually put an agent that went and read all of this and generated what we call is a simple ascendion language, that's what we call it, which is just English and nim on that describes the Perl script's logic.
And then I take that, and then I get another agent to come in and convert that language, and I go and tell the client, does this logic make sense to you? Right? Forget about the Pearl scripts, none of us know how to read it. But does the logic of what is getting done in the Perl script make.
Sense to you?
Says yeah, this part makes sense. This one I don't even think I use. This part looks good. So I'm able to also say, you know what, I don't even need to waste money converting these Pearl scripts because they.
Are no longer used.
So I'm now a document that is super efficient that I reverse engineered, so I can now forward engineer it whichever way you want. So I can take this document convert them again into what I call as user stories, which is the input, as I said, the requirement input for development. And once I create the user stories, I can I have an agent that converts one user's story into Java. I can have one agent that can convert it into c SHAP or whatever you want, and moving
completely into micro services architecture. And while I'm doing that, in parallel, I have agents that can create the test scripts and I can automate the testing too. So with what is happening, Eric is, I'm my teams and I we are completely reimagining how.
Work gets done right. There's no need for rework. Now. The biggest I'll.
Tell you thirty person of project costs is rework because the developer didn't understand the user story. The used story was not written correctly because the product manager did not understand the user needs and did not elaborate the scenario. As well, the testers did not test the software correctly. These are all all the pitfalls we are eliminating by running these agents and bringing in high degree of standardization and scale.
That's very interesting. You know, I understand conceptually what you're talking about. Did you use jen Ai for this reverse engineering? Is that how you loaded a Pearl script? You said, hey, what is this thing actually doing? Was that a Genai application? We got one minute left in this segment, Go ahead, we use.
A combination of GENI and other AI. So we use machine learning, we use you know, deep learning, and we use GENI depending on the type of problems we're.
Trying to solve.
With GENAI, we've been able to take this to the next level.
Mm hmm, well I can imagine. I mean I remember playing around with it whenever it came out a year and a half ago or so, and immediately drawing the conclusion that if it can write in French and German and Spanish and English, that you can probably write in cobal and C sharp and C plus plus and Java
and these other languages. And guess what it can. You know, my understanding, having researched this a good bit is that it'll get you eighty percent of the way there typically, and then the last twenty percent is the mile you have to do fine tuning, manually checking things.
You do want to make.
Sure that, as you've already suggested, you have a human being monitoring things, making sure that the outputs are accurate. But the beautiful thing about software that works is that it works, and if it doesn't work, then it doesn't work. And you know very clearly in a binary fashion, is this accomplishing the task or not. And if it's a no, you have to go back to the drawing board. But
this very interesting stuff, well, don't touch one, folks. We'll be right back talking with ascending about a new way of doing software. It's a lot faster and probably a lot more efficient. Will get into the details in our next segment.
We'll be right back.
You're listening to Inside Analysis.
React.
Welcome back to Inside Analysis. Here's your host, Erica.
All right, folks, back here on Inside Analysis talking to Ruin Varadarajan. He is the CCO and founder of a company called Ascendian. It's just like it sounds ascend with io n at the end. And they have developed a do a platform for software development that is agentic. They have all sorts of AI agents out there.
Now.
This is the talk of the town in Silicon Valley and around the world quite frankly, because if a machine can do a job better than a human, let the machine do the job. I mean, humans make mistakes. The machines make mistakes too, Jennai we've talked about famously makes mistakes. It generates things, so you have to do all sorts of work to train it and to ground it. Basically, but that's not really what we're talking about here with this agentic stuff, though, you still want to monitor what
they do and guide them. And frankly, this is my big question about agentic AI is how do we monitor their behavior, correct their behavior when they're wrong? How do we orchestrate their behavior? Because you've got a bunch of little guys out there doing stuff? Now do the overlap?
Is it log.
Files that gets spun out of these things? How do you actually know what they've done to where you can correct them or optimize what they're doing?
Amazing, amazing question, unbelievable question.
So what we are when we first of all, let me explain to you what is an agent architecture?
Okay, yeah, complete, Right.
So what we have done is in our we can build agents for anything under the sun. Right, but we decided as a company that we're going to focus on building this agentic platform around one value chain, which is software engineering, because we said we want to be super specialized in this area.
Mm hm.
So if you really look at an agent, an agent has got five elements to it. First element is it has what is known as a prompt template. Okay, that's where you tell the agent, What, what is your goal? What is your purpose? What am I expecting you to do? What should you? How should you operate? How should you how should you write the code? How should you write the user story? I'm telling it a lot of things, right, So I've started defining the framework around which it needs
to operate. The second element of the agent architecture is the model. In our platform, I can literally use any model. I can use anthropic, I can use you know as your open AI, I can use lama from from you Know, from meta. We can use different models, and we have our own viewpoints on what models work best for what type of use cas So our platform allows customers to say, hey, I've got a relationship with AWS, so they can use aws's bedrock services to choose the model, but we help
them with model selection. So that was That is a second element of the agent. The third element of the agent is what I call as its memory and its knowledge.
Right.
That is where I feed the agent with all of the knowledge it needs. For example, when I'm writing code for a client, I may be writing a code on authentication the client. My client may already have an authentication framework saying that go get the entitlements from this server, go get the access rights from this IDP or whatever. I can teach all of that to the agent so that the agent does not go and generate generic stuff.
I can also teach the agent on all of the technical standards, the kind of language that they use when they write something, so it has this what we call as in context learning or its knowledge base. That is the third part of the agent. The fourth part of the agent is where we define guards because that's very important. If you're writing, for example, an application we're using things
like social security number, et cetera. You want to make sure that the application does not expose the social security number, right, it still maintains the format social security is, you know, three four three, So it maintain the format, but I don't want it to be exposed. Or you may have certain ethical standards. You may say listen, when I design, I don't want to think about race, or when I think about design, I want to be inclusive. All of those things can be put in the guard rails, and
we use a guardrail framework. And the fifth is every agent can be associated with tools, because I may have an agent that needs to use a tool to get something done right. It may have to use a testing tool, or it may have to use a webscraping tool. So this is the architecture of an agent. Now what we do is we have a we are when I am building an agent, we actually have an agent that helps me build an agent.
Nice.
And we are also starting to use genetic algorithms to really improve the efficacy of an agent for a given use case.
And what was that again? You use what genetic algorithms or evolution genetic algorithms?
Yeah, yeah, So what we are doing with genetic algorithms is if I have a use case and I have two let's say agent candidates, I input it into my into my GA algorithm, and it will generate children.
And you know how GA.
I don't know if you have presuming that you and your audience know how generic algorithm works. But it works just like human evolution, right, It creates, it creates multiple strains of the of the agents, and multiple versions of
the agents, multiple generations and children's and whatnot. And what we do is we have what is known as a fitment function, right, or a fitness function that we have defined for that use case, and the fitness function will decide which of those children go to the next generation literally like you know, evolution, and lead that method to come up with a high quality agent candidate for a given use case.
Okay, I mean you're so. Just to give some context to the listeners, what you're talking about is kind of what they have in the in the database world of a query optimizer, right. A query optimizer figures out how to do the job better, faster, more accurately, for example, pulling data from various systems, and you can do There are all kinds of different machine learning algorithms out there, so you can do You can use different means to
determine how efficient is this could be more efficient. To what you're saying is that basically these genetic algorithms, you have a sort of a fleet of them. You use them, see which ones work better than others. The ones that work best, you continue that one, you don't continue the others, and thus you are incrementally improving these individual agents and the algorithms that they use.
Is that right, correct?
And this is in the beginning when I'm building an agent, right, So when I'm right off the bat, I have a very high propensity for being accurate in the agent's capability to do something. So right off the bat, we are starting with good quality agents through our process. Then we
also run a whole range of analytics. So every time you know, when the agent does something, Let's say the agent writes a piece of code for a developer and the developer looks at it and says I like it and clicks says like that goes back into my platform and I'm capturing it. So we call it answer relevance. So if an engineer says, test this code for me, or optimize this code for me or whatever, and if it likes, and if the engineer likes the output of
the agent, then that goes back as analytics. And I'm constantly monitoring the analytics of these agents to.
Improve either the prompt or.
Give it more learning and improve its knowledge base, or fight you in the guardrails, or create or implement new tools. And this is an evolving process that we fall right.
And that's largely through user provided feedback. Is that correct or is some of the feedback also agentic?
So very good point.
What we also are doing is because we have an so literally what we're doing is we're mimiking the workworld in our platform. So we have two types of agents. We have a manager agent and we have co worker agents. So the manager agent is actually managing all the co workers, and he or she the manager she may deside that the same piece of work I'm going to give to two agents and I'm going to see who comes back
with a better result. Right, So we can even have two agents doing the same piece of work and find out which is more effective and take that output too. So that is something that we so we call them workflows in our in our platform. So I can literally create a workflow where I can concatenate multiple agents and put a master agent on top and say, okay, go build this piece of software. So one so the first agent may just take the epic, understand the epic, generate
the user stories the mask. The manager agent may say, I don't like that. I'm going to get another agent to come and do the same and see which one is better. And then once he or she is happy with the work that was done in terms of building the user stories, she can invoke and we can use she and he interchangeably, because these are you know, yeah, agents, I presume, So it can go and then kick off
the next set of agents. It and if it finds that the work needs more agents, our platform allows the mask the manager agent, to spawn more agents so that more agents can attack the work.
So this is how the whole thing.
Can So we foresee at some point in time, as we improve the efficacy of solving different problems for different clients, we could potentially get to a true factory model like you see in a process chemical plant where the process is running and there's one guy or two people sitting in a in a in a room with a cockpit, tuning this and turing that and tuning and looking at gauges and letting the factory run the process.
Sure, sure, no, Well you're reminding me of these transformers, right, the whole concept of the transformer. And I actually have a thread going with Jurgen schmidi Huber to hopefully come on my show someday, and we were talking about this, and it's the same kind of thing where you have an array of agents or automatons let's call them, and
there's one that manages them and thinks through. It's like, huh, because like if you think about how these large language models work, from what I understand, it's still next character basically next token, what is most likely based upon the prompt of my training, et cetera, which is a very linear process. But what the transformers. Now, you've got a sort of supervisor who can kind of look over you go hmm, I think I'll go with you this time and you that time, which I think is very clever.
And that's I think also reflected in mistral right with this mixture of experts, that that whole architecture, yes, is very similar, and that you have some agent that is basically orchestrated in the behavior of a group of agents and they all work as a team. Is that about right?
Exactly exactly? Now.
The interesting thing is now think about what I told you in the in the initial part and our thesis statement of why we started doing this. If my client comes to me and says I gave you three files, three Cobal files to move to Java, I'm now going to give you one thousand files. All I need to do is to run parallel workflows.
Yeah, right, sure, and I do.
These GPUs are made for right.
Right, And I don't need to bring in more humans.
I just need to have, right, you know, maybe ten maybe five ten humans who are looking at the process so I've told so, if you're a let's say you're a you're a developed let's say you're a senior software engineer, what does this mean to you, because your role is going to change. What I'm telling my senior software engineers and my principal engineers is earlier you would work on writing the code for your client and optimizing the colde and right.
Right, that's what you were doing.
Now what I want you to do is to make these agents more effective in doing that for your clients.
Right, Well, I'll throw a theory at you. I think you're going to agree with this because I think a lot about AI and the impact on workflow, on day to day lives, on how we actually build things in particular software, and as a general rule, I think we're at a massive inflection point. It's an inversion really, because in the past we push the machines like think a lawnmower. I'm pushing it along the way, using my strength and
guiding it myself. Well in the now, in the near future, it's turned around where the machines are sort of pushing us, but we're still guiding them. We're still trying to shepherd them. Say no, not this, or yes, more of that. So but the point is that the impetus is now coming from the machines. They're creating these things, and we're just sort of shepherding and guiding them, almost like a gardener does in pruning a tree.
What do you think totally?
So?
In fact, I'll give you a very interesting example. There is a role in the software engineering world calls it a tester or a quality engineer. So his job is to test or her job is to test something. So think about now, what's going to happen to her role. So I've told her now I want you to stop thinking yourself as a quality engineer. I want you to think yourself as a quality systems engineer. What does that mean? You are going to run the quality system. I'm not
going to do the quality engineering. You're going to run the quality system. So you should know how to engineer your quality system so that you can ensure and if you know, you know, I still go back to manufacturing, because these guys have cracked it.
We've got to go back to manufacturing.
They have this notion called total quality management where they go to your supplier supplier all the way through to the raw material to make sure that every part of the inbound value chain is bringing in the quality for you to build the right kind of ball bearings.
Or whatever you're doing.
Right.
Sure, so with this quality system, So today, most of my testers are only trying to test what the developers are done. They're not testing to a large extent, what the product.
Managers are writing. You're not how the users are thinking.
Oh interesting, now this is this is very important because what you're speaking to is the criticality of understanding the whole process end to end. And we talk a lot about this on these various shows. Things like error propagation. If it gets all the way upstream, it just spreads to the entire environment. And now it's like tainted water. For example, if you get some poison in the water, if it's in the distributions system, then it's everywhere and
you've got a huge problem. Well, if you stop it at the source, that's when you really get make some Hey, but folks, don't touch a dewel.
Be right back.
You're listening to the Inside Analysis.
Welcome back to Inside Analysis. Here's your host, Eric Tabanac.
All right, folks, back here on a fascinating Inside Analysis episode. We're talking to our own Baradarajen. He is the chief commercial officer and founder, and they have built an agentic platform that allows their clients to leverage the power of agentic AI. What is that the little AI agents? There are little semi autonomous mini applications that can do various things. And I'll throw this over to you. What I've heard from lots of companies and who are doing something similar
to this. Variant for example, has automatons. They have like I think twenty of them now and they plan to have another twenty soon. And what their CEO told me is that they want each agent to do one thing very well, because I had asked them, do you want the agents to learn to do multiple things? And he basically said, not really unless you're talking about the orchestrating agent, sort of the manager agent that has the bigger picture
in mind. And if all this stuff is declarative in nature, that's great, right, because you have an end state you want the agents to achieve. The manager is watching to see how that gets done, is instructing the individual agents to do their thing.
Go get this.
Data, you process this data, you check the data, you double check the data.
You push it to production.
All these different agents get their instructions right, and you can parallelize that stuff like for go get that data, spin up one hundred agents to go pull in a big large amount of data and then start analyzing it, acting on it, but pushing it production, et cetera. That's all declarative, right, And then again my question is how do you know, how does the real person manage that stuff? What kind of levers can they pull to change what's happening?
So to me, actually I don't fully agree on that notion.
Okay, that's fine.
I actually think while I agree with that, you need to have an agent kind of focused on an area.
I wouldn't use the word task alone a space.
You want to use the generative capabilities of AI today because I can tell you all we want to do is to create some boundaries and say, listen, thou shall operate in this space and let me give you all the knowledge you need to be effective in this space. So we expect actually our agents to bring in a high degree of creativity and I'll show it. I'll show it to you when I show you the platform. Because what's happening is when my manager agent tells, hey, you
agent write the code. When she comes back with the code and says, here's my code. I'm ready to ready for you to move to the next step, he says, no, I don't like some of this.
Change it.
You have not addressed some of these things, and it goes and explores that and improves it.
So interesting.
So what we are.
Thinking is that these agents need to use reasoning because there are decisions that need to be made. Should I write it this way versus that way? So we want the reasoning abilities, which is what agent Kai does. It really leverages the reasoning ability, the reasoning abilities, because you need that.
Mm hmm.
I mean that's what humans do best, right, I mean, right now, that's our I think our leg up on the AI agent's at the moment is that we really can think through you look at this story about was it Apple's new chip? They have a new quantum chip. I think it was that solved some problem like a like a million times fast than it ever would have been solved before. And it's because you have sort of
multiple layers of things, right. I mean, I've given the example in the past of Carl Sagan when I was a kid, blew my mind because he talked about two dimensional characters and a two dimensional world and how they can't see past each other. But if someone came along and picked one of them up into the third dimension, now they can look down and see everything. And that is huge. I mean, think about getting through a labyrinth.
If you can get up in the air and look down at the labyrinth, now you can figure out exactly how to get out of there, whereas before it would have been a very painful trial and error type environment. And I think what you're hinting at here, or even openly articulating, is that you know, if you have the right array of agents and the right architecture, then they can all sort of check and balance each other and much more strategically solve problems. Is that right?
Very true?
We have an agent's In fact, we have the pure agents talking to each other already, and the manager agent comes in and just kind of draws some boundaries and says, hey, guys, you know, let's not go crazier, right, say within this back. If I find that you're not giving me the output that I want, I want you to go back and nerate again. Now the thing is as we are, so
in fact, when I sit down with my team. We are not trying to solve the simple problems that you know these RPA type of companies are doing right, because robotic process automation is very deterministic in go fetch data, my agents can do that, but I'm not really I want to solve the tougher problems. So for example, if you take I'll go back to testing one of the starting points, and testing is I will go to the to the development team and say, hey, guys, what are
you going to be building in the next round? They call it sprints. You know this agile world. By the way, Agile is also going to go away with all of this. I will tell you at another you know, it's called the point. It's going to die.
Now.
Now what's going to happen is I'm going to go as a test manager or whatever and ask the development team what are you going to build in the next round? Because why is that important? Because I need to now figure out what should I test? Where should I test? Right, it's important because I may be building some things that are new, I may be changing things that are already in existence.
Right, so I'm getting it.
I actually have an agent now where I can get your your sprint plan. I can get you know, what are you planning to do? I can get your schedule that you have. I can get a whole bunch of things, and then the agent is able to generate saying this is the testing you need to do. That's not like going and fetching a piece of data. That is the reasoning power.
Right.
I'm saying over time that things like design architecture. So when I used to when we used to build systems, there's a lot of time we used to spend around architecture and design because there are a lot of decisions that need to be made that have implications on various aspects. It could be performance, it could be you know, user experience,
it could bee hundred things. And for the human mind to do to really do trade offs beyond four or five dimensions is not possible do right, I think like that we're not some of us are lateral thinkers, but we may miss some of these things. So those areas where you really need to think about design and and really come up with what works best.
I think these agents are going to do better than us.
Wow, No, I can. I can say that because when I think about how computer systems are working now. I mean, first of all, we have just wild, crazy innovation in all directions. I mean these deep learning modules like you see Gemini and Claude and all these various things. I try to explain to people there is no limit to the number of permutations for how these things can be built, and they can even be dynamic and change over time and sort of readjust you know, dynamically adjusted software defined
software development. That's kind of what it falls down to, right, And that's what you're what you're getting at. And because so whenever we talk next, I want to get into the details of the reasoning of these things and how you score and manage the scores of all the agents in certain environments for certain tasks. Because if you are incrementally inching closer to more and more efficient design, that's
very very very interesting. I mean, right now, if you I could just make a blanket statement and I'll give you ninety seconds to comment done, and if you think
about the compute that happens in any large organization. I mean VMware came along and optimized that to a large extent, right, optimize the use of CPU and things of this nature, which is important, but all in all, if you think about all the unnecessary crunching of data and processes that are not generating value, not even needed, we're probably at
like twenty percent efficiency, I think. And so if you get this right, you can save Like we're talking trillions of dollars at scale, we've got a minute, thirty seconds. What are your closing dots?
A one hundred percent?
Let me tell you the first market I want to disrupt is a there's a low hanging fruit. And I know I'm going to piss off some of my peers in the industry, which I'm fine doing because that's why we're here for. They are making oodles of money and literally, you know, taking the clients to ransom around testing.
You will be amazed.
These companies have thousands of people testing software e to day and some of these companies, I mean, I don't want to name them because I'm not here to necessarily shame them, but they have to really reflect some of these large firms and you know the names. I don't need to tell you. They make billions of dollars in testing. And you know how do they do the testing? They just put butts on seat butts on, butts on, butts on seat.
That's all going to do.
And let me tell you with this model, I think in fact, when I started essenting on, my notion to the team was I think CIOs are spending let's say Seattle's are spending one hundred dollars. I don't think they should spend more than fifteen dollars.
Yeah, I'm with you. I mean, I totally see this, and we're going to pick this conversation up at a future show. I am sure, wo folks, look these folks up online. Ascending in I think is ascendant. I'm pretty sure about that. They certainly have the right idea because you can optimize the freaking daylights out of what is being done in the world of computing these days. I'm talking absolutely massive gobsmacking savings if you do it right, and it'll be vastly more efficient.
We'll talk to you next time.
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NBC News Radio. I'm Lisa Carton. Jimmy Carter, the thirty ninth President of the United States, is dead at the age of one hundred. Carter's death came after a February twenty twenty three announcement that he had decided to enter hospice care and spend his remaining time at home with
family after a series of short hospital stays. Carter served a single tumultuous term and was defeated by Republican Ronald Reagan in nineteen eighty a landslide loss that ultimately paved the way for his decades of global advocacy for democracy, public health, and human rights. Former President and Missus Carter worked with Habitat for Humanity in communities throughout Georgia and globally for nearly forty years. More from Liz Kennedy.
In nineteen eighty four, Jimmy and Roselind Carter created the Carter Work Project, working alongside volunteers with Habitat for Humanity, building and avocating for affordable housing. It was an experience that fulfilled the couple.
Every time we've ever been out as volunteers leading a project, no matter where it's been any in this structure oin around the rest of the world. At the end of the Habitat project, we always feel that Rose and I got more out of it than we put into it.
The former President One said, Habitat provides a simple but powerful avenue for people of different backgrounds to come together to achieve those most meaningful things in life. A decent home, but also a genuine bond with our fellow human beings. I'm Liz Kennedy. Carter is widely revered for his champion of human rights. His brokering of the Camp David Accords with Egyptian President and War Sadat and Israeli Prime Minister menach And began nineteen seventy eight, remains central to his legacy.
Carter also received the Nobel Peace Prize in two thousand two for his efforts to push for peace across the globe.