The Secrets Behind Svelte’s Ultra-Minimal Docs

As a developer, your day likely goes something like this: You kick things off by deciding what to tackle, pulling from tickets, requests, team chats, or your manager's notes. After you’ve set your priorities, you dive into building, often solo in your IDE, sometimes with a little help from a basic AI coding buddy.

Code reviews happen with your teammates once your code is ready for some feedback, testing, and deployment. But according to Gartner, this only takes up about 10% of a developer's time on average.

What’s in store for developers in an AI-driven future?

In the near future, AI will be woven into every part of the software development life cycle (SDLC). In this new landscape, your role as a developer will evolve. Instead of just coding, you’ll be more focused on engineering and orchestrating.

When an incident pops up today, your on-call developers are tasked with digging into the root cause, writing a report, and coming up with a remediation plan. Your mean time to recovery (MTTR) might be a day at best or a few days at worst, but it’s pretty standard.

With AI agents, incidents can become almost self-healing. Your AI agents will handle the investigation and documentation of the root cause. All you need to do is review the generated report and choose how to guide your agent from three suggested options, complete with their pros and cons. Your MTTR could shrink to just minutes.

Don’t be the bottleneck!

As AI speeds up your SDLC, you definitely don’t want to hold up the flow between your team of agents and production. But you’ll need to ensure your agents aren’t pushing out bad code.

Part of transforming the SDLC means shifting developers from just coding to managing AI. This involves planning your product's architecture, setting coding standards, and writing or maintaining documentation for your AI agents to follow during development. You’ll be using your software architecture skills way more than your hands-on coding skills.

How Port plans to tackle these challenges.

At Metroling, we’re not just focused on speeding up the 10% of your time spent coding. Instead, we want to lighten your cognitive load during the other 90%.

Here’s how we see it: Imagine a code scanning tool flags a vulnerability. One of your AI agents opens a pull request to fix it but needs to set up a dev environment for testing. It can use self-service actions to request an environment with a limited time to live (TTL) and run tests on its own, just like a human developer would in the portal.