Goutham Rao on AI-Driven Site Reliability and the Future of Enterprise IT Operations | AWS re:Invent 2025

Hey everyone, it's Alan Hummel, and we're back here live with our AWS Reinvent coverage. Hope you've enjoyed what we've been putting up so far. Um, we've got our first non-text on live guest here today with us.

Let me introduce you to Gotham Rao before we get in, and actually, it's a great segue to talk well, what exactly is New Bird? New Bird ai? Uh, we, we build an agent at SRE.

Uh, what that is, is a site reliability engineer. Um, so basically, you know, IT, operations for enterprises have been, uh, a difficult, challenging, um, you know, uh, part of their operations for a very long time. And the modern, modern compute stack and infrastructure stack is gotten way complex.

I mean, anybody here at Reinvent, which is where we are right now, can, can attest to that. So we live in an age of ai. What new Bird AI does is we build, using ai, we solve the problem of complex IT operations.

That's a great, that what a great use case. I'm gonna come back to that in a second. But before we do, I always like to give our audience a sense of who they're listening to, who they're watching.

Sure. We didn't even talk about what your role at Newburg is, how you got here. Let, let's hear, let's hear the Gotham story.

How far back do you want to go? Well, you mentioned you're from Brooklyn. I am, yeah.

Which is, that's points in my book, right? Right. Yeah.

From Brooklyn too. But we could, we could probably skip ahead till after college or Something. Sure.

Yeah. Let's do that. No, I, um, so I did grow up in Brooklyn.

Um, my, uh, dad bought me my first computer when I was, um, barely a teenager. So I got into computers early on. Short of the long story is I'm an engineer.

Um, I am the CEO of New Bird ai, but I'm an engineer. I write code. I'm passionate about, uh, ai.

I am passionate about data science. Um, and really, uh, the integration of those two things is what new bird AI does. Um, about me, I, uh, did my master's at, uh, the University of Pennsylvania.

I grew up, uh, um, well, I grew up in Brooklyn. Um, grew up in India for a little bit. Moved to Philadelphia for my, uh, graduate, uh, school after that, moved out to California.

Um, this is now my fourth startup. Um, I've been in enterprise software for most of my career. Um, what else can I tell you?

I, you know what, I, if you, if you stopped right there, I think everyone would be out there saying, I wish my kid would grow up to be like that. Right? Uh, I think you Want that.

So, uh, well, it is good and bad with everything, but you know what's interesting? You were almost apologetic about being a CEO. Hey, I'm not just a CEO, I'm an engineer.

I could code, God darn it. Yeah. Um, But, you know, there's something, so I, I've done four or five startups myself, venture back startups, and I, I think there's something about being a founder, co-founder of startups that transcends, whether you're an engineer or a sales guy or a business person or what have you, you, it's passion.

You gotta have passion for what, what it is you're doing. You don't just start a company to start a company. You start a company because I see a problem.

I see something that the market hasn't addressed adequately yet. And I think we could build a better mouse trapp. I think we could build, we could do something that's gonna make someone's life somewhere easier.

And that passion, I think, is what separates the engineer from the founder, from the CEOI think. So I think, um, you know, look, it, it took me a couple of times to figure this out and, um, first of all, uh, you have to do what you're, uh, passionate about. And, um, look, what we're, what we're doing here at Newberg ai, and I'm here to talk about anything you want to talk about, but specifically in this company, it's something that I could use, like I've, um, you know, for your audience out there, um, what is an SRE or why are it operations hard?

Well, what, what the, the truth behind the matter is that, um, you know, you guys use, um, you know, Twitter or Instagram and the complexity of the software and, uh, the hardware and the infrastructure that goes behind all of this, not just delivering the, the content to you, but the AI behind it. These are very complex systems, and when things break, engineers have to be up. And you're looking at what's called telemetry logs, uh, metrics traces, uh, and you're trying to figure out very com a, a, a solution to a very complex problem.

The short of the long story is I've done that. I've been up at two in the morning, um, beating my head against the computer and trying to figure out how to fix these things. So, um, I'm passionate about what we're building because it solves that problem.

It solves a problem for me, that's the most important thing. Like, if, if I can build something where I'm happy with it, and I'm like, whoa, this is awesome, then, you know, I hope that there are other people that will benefit from whatever it is that we're building. So, yeah, it's fun building this, um, you're right that I am passionate that I'm an engineer because, um, you know, I'm having fun building what we're doing.

And it's, it's, it's like when you're tinkering with something and your hands are dirty and you're figuring out what it looks like and everyday changes, it's a, it's a fun journey. Absolutely. You know, that that's not an uncommon fact pattern.

Right. And I call it, I can't be the only one, I can't be the only one with this problem. Right.

If I could solve this problem for myself, I could solve this problem for everyone who has this problem. And there's gotta be a business wrapped around that, that, That's right. Um, you know, you, you solve a problem, and if there are enough people that, um, align with the problem you're solving, then there's definitely a business around it.

And Yep. And, you know, the, um, look in, in what we're, and AI is so, um, um, prevalent right now with, or the, and so many startups here at, at least at Reinvent, that are focusing on taking Gen AI and applying it to a very specific domain. Now, what it, what, once you start solving the problem, you start realizing that there are other people also solving the same problem.

Okay. So there's competition out there. And then, um, how do you differentiate yourself from the competition?

Well, not two people aren't gonna solve the problem the same way. The solution is going to look different, and it'll align with people that are, that nuanced in how they want the differentiation to look like. And so, um, then that drives your passion, like, am I, uh, subscribing to a very specific set of customers that want the solution delivered in this, this, in this kind of way?

And you engineer toward that, and, um, hopefully you acquire customers that are, uh, aligned with your, uh, mentality and how you wanna solve the problem. So you're still having fun doing it. It's, uh, it's been a while ride, uh, for the past two years.

And, you know, just like, um, looking at, uh, talking to all the customers at Reinvent just makes you, um, that much more energized. I love it. I love it.

Let's talk a little, let you know, we, we were up here talking about it from a business point of view, but let's dive into agent ai, AI for SREs. Sure. Right.

com, another one of our sites, SREs have become key members in these communities, right? The role of the SRE, I think is more clearly defined now than it's ever been. Right?

People don't question, is it, is it real? Is it, you know, what exactly is it? My question to you though is are we ready for an agent?

Is it to replace the SRE to supplement the SRE now, augment, Augment, augment. It's, uh, it's you, you're not gonna replace humans. And people ask this all the time, like, is AI here?

There's when the industrial revolution happened. Mm-hmm. Right?

And I'm sure I wasn't alive back then, but, you know, I'm sure people were worried about what will happen to our jobs. And look, we just learned to live with technology. And this is not no different.

I mean, it's faster. It's, um, um, the change of, um, yeah, innovation, the curve is probably a lot more steeper, but ultimately it's something that we will live with. It's something that we augment ourselves with.

And, and, and so actually before I answer that question, let's talk about what an agent itself is, right? Good. Um, Gen ai, you know, past couple of years, everybody, you know, Chad, GPT came around and people were wowed by it.

And how did, uh, AI make gen AI make its way into the enterprise? And by the way, let's also acknowledge that AI ops itself has been around for a long time, that there's nothing new with AI ops. So why is this wave different?

What you asked the question about, um, agentic systems, or you made a point about agentic systems. So let's talk about what an agentic system is first. The way the difference between traditional AI and gen AI is that machine learning based, traditional AI based on machine learning is, is engineered for more probabilistic outcomes or known outcomes.

So you're engine for an, an example, when you're going to design a system for, um, uh, credit card, uh, fraud detection, right? You have well-known patterns and you'll engineer for those well-known patterns, gen, ai, the, the, um, the, the number of variables or the space, the complexity, the number of parameters is so large that there's a little bit of uncertainty in what, what its outcome will be. Keeping that in mind, what an agentic system is the following.

So we now, we know we have these very large language models, which is the brain, which is the whole core behind gen ai. How did, how do you use that in enterprise systems? Well, gen AI on its own is generic, has been trained on so much web data out there, not necessarily applicable to enterprise information.

So the way it made its way into enterprises is people started with this thing called rag retrieval, augmented generation. Sure. So I'm gonna provide my enterprise content and let's see what the AI can, um, you know, determine out of this, summarize it, create marketing documents.

That's not an agentic system. That's re an agent. That's re So now, and this has really taken off over the past, uh, 12 months, maybe 18 months, what an agentic system is understanding that these models have a lot of knowledge in them.

You can't just take content and throw it at it for a certain class of problems. An example, we're talking about SREs, right? Site reliability.

Engineering relies on complex telemetry, enterprise data, enterprise application data consists of a lot of logs, a lot of metrics. These are time series data traces, which are very complex graphs to short of this long story is there's just too much information to apply rag. You can't take this information, throw it at the LLM and say, help me.

Mm-hmm. So what is an agent system an age? And I'll get to how this helps SREs not, not replace, okay.

I'm letting you run with it. Uh, an, a genetic system allows the LLMs to figure out what information they need to a access. It's the converse.

Instead of you throwing data at the L LM and saying, help me, you're asking the LLM, you tell me what information you need. I have this ailment, my website is crashing, or this feature is not working. LLM, apparently you have been trained on so many different IT scenarios.

You tell me what to go access. And that's called context engineering. Okay.

And that's where companies like New Bird ai and there are other people solving this problem come in. The point here is that we now believe that these LLMs are so smart, have so much information in them that now the problem that needs to be solved is not making them smarter, but it's about context engineering, garbage and garbage out. If you ask, you can ask an LLM any question and it all come up, always come up with an answer.

And that's the problem. You can't do that with IT systems. You have to be surgically accurate, a hundred percent.

Uh, identifying the problem relies on the right context. So how does this complement SREs? SREs are sitting there under the gun.

They have a problem to solve. If they have a good context engineering solution, they can ask the LLM, I have this problem and here's my context engineering platform. It will help you find the needle in the haystack, and then you, uh, help me solve the problem.

This will help make the SREs and the engineer's life a lot easier. They can solve more problems in, in shorter amount of time. And more importantly, they can focus on not firefighting, but innovating and building better product and solutions, which is the bottom line for an enterprise.

Love it. You gave us a whole bunch of stuff here. You can, you guys need to go back and re-listen to this after this.

You watch this, it'll be up in a couple of days because there's so much, I don't want to use the word bedrock 'cause that's a big word over here. Yeah. But there's so much foundational information here between what is an agent agent ai, what is rag, what are all these things?

Let me pivot a little bit con 'cause we're running on time. Um, AWS announced a whole bunch of agents or mm-hmm. You know, they announced three real key agents.

Yeah. One of them though is they're calling it a DevOps agent. In my mind.

I, I don't know if it's really a DevOps agent, but it, it seems to do some of the SRE kind of stuff. Yep. Can be competitive.

Generally the AWS products, you know, or the 80 20 rule, right? They're 80% of the functionality, 75% of the functionality. How do you view it?

Is it, Hey, use the AWS tool and then when you find out what's missing in your life, come to us, Or No, no, no. Um, look, um, I I tell my, uh, customers the following, uh, a story too. We were talking about agentic systems.

I, uh, I probably rambled on about what it takes to build an agent and context engineering and, and this and that. Um, a follow up to that story is dealing with an agentic system is different from purchasing software. Um, where in software you kind of have an expectation of what it does, and it's either or.

You're either using software from vendor, vendor A or vendor B to solve a problem. But in dealing with agentic systems, you should approach it differently. It's how you hire people.

It is part of your workforce. It is rooted from a deep, um, um, uh, you know, machine learning. But, um, a, a deep understanding of a variety of different, uh, problems that humans have solved.

So what, what do I mean by this? When you are hiring an employee, do you hire the same type of person again and again? Or do you hire different kind of people?

It's about diversity, because you hope that when you ha hire different kind of people, they ha they come with different ideas, different backgrounds, um, different ways of solving a problem. And so overall, your enterprise is richer. Why am I saying this?

I believe the same thing will happen with agentic systems. You are not going to settle on just one agent. There will be agent diversity agents will work with each other.

There are already projects around eight oh, a agent to agent protocols and how agents can access external systems through things like MCP. So it's great that everybody's has their own agent, and these will solve very nuanced problems. And there potentially there'll be a framework that unifies all of these things.

And we don't know what that will look like. And I think as the industry matures, we're gonna figure this out. That's my way of answering your question of which is, um, AWS will have an agent, Microsoft announced its agent about, I don't know, uh, eight, nine months ago at their conference call.

Um, you know, the Azure SRE agent? Yes. Datadog, which is a huge partner of ours, has their own agent.

And that's great. And these agents should work with each other. We already have customers that deploy multiple agents.

We, um, did, um, uh, at Microsoft Ignite a couple of, um, uh, weeks ago, we demonstrated how, uh, our agent, which is known as Hawkeye mm-hmm. That's our, uh, agentic, SRE solution, can, um, you, uh, cooperate with the GitHub copilot agent and the SRE agent. And all these three things together solve close the loop.

What loop is that developers push in code. Invariably things can break. Our agent can pick it up, uh, at the operations end and saying, I'm seeing this problem submit.

Um, a, um, uh, a request to the GitHub co-pilot agent to go in and write some code to fix it goes all the way back to the developer to say, this looks good, and I'll accept a fix. And that loop shortens how long it would've taken to fix that problem, end to end. So, um, having, um, an enterprise purchasing multiple agents, or working, not, purchasing, working with multiple agents and tying these things together will be the future.

Excellent. You know what, we, we didn't mention the URL, how people can contact. Yeah.

ai, NEU brd. Okay. AI and our agent is known as Hawkeye.

Um, we've, um, uh, we G eight actually last year at, uh, reinvent. We have, um, a lot of customers that have been using our systems now. Um, we have, uh, results on our website that you could go look at in terms of how we have reduced what's called the meantime to incident resolution.

Um, in some cases, 90%, uh, time savings. Wow. And what does that mean?

Well, it's, uh, bandwidth and time that the enterprise can get back to work on, um, what they actually want to do. Right. Building their core products and services as opposed to firefighting.

I love it. Gotham, we're outta time, man. But thank you so much.

You know what? Thank you. Appreciate it.

Again, I'm gonna tell you guys something. If you want, when this is up on Tech Strung TV or the YouTube channel or the OT channel, go back and listen to what he said. Again, it's a great primer for some, you know, basic concepts that we all bandy about these words and you may not truly understand what they are.

So go check that out. Thank you for that. Thank you so much.

We're live here at Reinvent. We'll be back in a little bit with our next guest. Stay tuned.