How to turn your 1000x engineer into a 10x everyone | LinkedIn’s Karthik Ramgopal

**Andrew** (0:05)
Joining me today is Karthik Ramgopal, Distinguished Engineer at LinkedIn. And Karthik, LinkedIn is obviously becoming the, or has been, the primary platform for the professional world to understand how the world around us is changing.
It's a place where we can share ideas and collaborate, and behind the scenes is an engine that keeps it all humming. It's defining even the next generation of professional tools and professional people and what they expect out of the networks and communities that we build at work. And at Dev Interrupted, we've been talking a lot on the show about an effect called the Agentic Halo. This is a transformative effect where an AI-powered leader or individual up levels and up skills those around them. Instead of being a singular 1000X engineer, they turn everyone around them into 100X engineers. And those types of leaders are really redefining the future of software. And I know that you've been at the center of this at LinkedIn and their Agentic platform. And so today we're going to dive into how you've been exploring these concepts in a massive production environment. And what it's been for you to be like to scale that effect beyond just raw compute. Also addressing it with other parts of the AI stack, including the people part, including the technology parts that sometimes get neglected when all of the other buzzwords are flying around. So as you can tell, Karthik, there's so much spinning through my head, ready to talk with you about. And it's so great to have you here on Dev Interrupted.

**Karthik Ramgopal** (1:45)
Well, I would say that, you know, like thanks for the opportunity. And let's jump right in. I'm excited to talk to you too about all of this.

**Andrew** (1:53)
Great. Well, let's start at the top. How about we learn a little bit about what has been happening at LinkedIn, with its agentic platform, for its engineers that are building and servicing and developing the tool that, the platform that we all use.
When you've been shifting into not only being an agentic engineer and having agentic engineers around you, but also now providing AI features through the LinkedIn service to your customers and users, what have been some of the fundamental architectural shifts that you've made in your day to day?

**Karthik Ramgopal** (2:26)
So, I think the word agent platform is overloaded. We effectively have, I would say, two incarnations of it. A lot of technology is shared, but the usage is obviously different.
The first is the agent platform for internal productivity for engineers, but not just for engineers, for a variety of other job functions as well. Product, design, legal, marketing, business operations. A bunch of people use this stuff, internal productivity.
And AI is pretty universal in that way. It's like a rising tide which lifts all the boats up. It's the same way for AI. It's not just engineers, it's everyone else. So there's that. And then you have the agent platform which we use as the framework for building a bunch of our production agents, either on the consumer side or on the enterprise side, to serve our members and customers. Again, as I said before, a lot of the technology is shared.
Some of the core building blocks here are stuff around basic stuff, prompt management, how do you do inference, how do you abstract away common orchestration operations when building agents, how do you do context management. Again, we started off very simple. We had chain-based systems, we had graph-based systems, and we have more harnesses right now. But one of the things we have realized through this entire journey is that a very important thing for an AI system, which is very different from a traditional software-based system, is agency and personalization. And personalization, especially over the long term, requires investment into a form of memory, right? So that it remembers, it gets better over time and truly understands you. So we've also been making a lot of investments into our cognitive memory stack.

**Andrew** (4:16)
Right. So I love how you frame the problem, the opportunity even for teams that are working with these tools, that it's not something that's locked within an engineering world. What it does is that it allows you to bring deeper action, insights and leverage to all kinds of roles within your organization. And in doing so, bring them closer to the problem space that y'all are all working in. It's not just the engineer's responsibility to be customer obsessed and be really close to the conversations that their customers understand. We try so hard as organizations to bridge those two very often, very gapped parts of the org. But it's also everyone else's responsibility to understand how they can have a role in this. What are the new skills they need to develop? How can they share their best practices with folks around them? So you saw this as an opportunity of if I can create this internal ecosystem where people can come together and share, then I trust that the coming together and sharing will happen. And then it did. It was like, if you build it, they will come deal. And how did you assess that problem and even start? Because I know a lot of leaders, they maybe see, they get hungry for like, I wish I had that ecosystem pin, that innovation thing.