How Will AI Enhance Platform Engineering and DevEx?
The customers of Digital.ai, an AI-powered DevSecOps platform, include large enterprises: financial institutions, insurance organizations, and gaming companies. One of the biggest issues they face is scale.
“They all are adopting, of course, modern development methodologies like agile DevOps,” said Wing To, general manager, DevOps and vice president of engineering for value stream delivery platform and DevOps at Digital.ai, in this episode of The New Stack Makers podcast.
“But as they organize that across large organizations, like thousands of developers, the challenges change to really is how do we scale to have the benefits of the fast delivery with velocity, but intimacy with the end user, and then still be able to do that at scale.”
In this episode of Makers, To and Heather Joslyn of TNS explored the latest in platform engineering and how AI is helping to enhance automation.
This Makers conversation was sponsored by Digital.ai.
What’s the Value of Increased Productivity?
Alongside the challenge of scaling DevOps practices, is another dilemma, To said: If these practices are helping your developers write more code and release it more frequently, is that automatically a good thing?
And, he added, there’s an emerging challenge. “As I’m sure everyone’s talked about around AI-assisted or AI-augmented development, and especially with the large enterprises, they’re seeing this promise of productivity gains in the development organization. But how is that being realized across the organization?”
What if a company has highly productive developers, but can’t match them in terms of what happens to their software after it’s built?
“As we all know, delivering code is not just about writing code. There’s a whole lot of process after that,” To said. “That has to match that same cadence.”
Marrying Automation to AI
Platform engineering, a set of practices and tooling aimed at freeing developers from having to worry too much about Kubernetes and infrastructure, and operations engineers from having to shoulder repetitive tasks in serving those devs.
“As teams scale, we have this challenge that new junior developers [and] medium developers are not so skilled, and we don’t want our senior developers spending all their time working on infrastructure,” To said. “So then it’s a case of how do we do the scaling? How do we put things in place to help do general orchestration that are reusable?”
Digital.ai, To said, is focused on including AI in automation that both helps developers create and deliver code and helps organizations generate more business value from their software in production.
Among the things Digital.ai is doing: using templates to capture and replicate the “opinionated” parts of an organization’s process for software delivery. But it’s also using AI to help automate setting up developer environments quickly and creating tooling for devs.
These and other features in the works are helping Digital.ai round out “the whole idea of the internal developer platform, which interestingly, is not just one thing,” To said. “It’s actually multiple sets of tools, and bringing those together, like creating pipelines, individual tasks, or setting things up.”
Check out the full episode to learn more about the latest, AI-driven approaches to platform engineering.
