AI is supercharging enterprise development! GitHub’s vision highlights the power of AI and mature DevOps practices. Revolutionizing software! #AI #GitHub #EnterpriseDev
Explanation in video
Hey everyone, John here! And with me, as always, is my trusty assistant, Lila.
Lila: Hi everyone! Ready to dive into some AI news with John!
Today, we’re going to talk about something super exciting that’s happening behind the scenes in the world of computer programs. You know all those apps and websites you use every day? Well, AI is starting to play a huge role in how those are actually built, especially for big companies.
Imagine building a magnificent skyscraper. You need architects, engineers, construction workers, and lots of specialized tools. Building complex software is kind of like that, and AI is becoming one of the most powerful new tools in the toolbox.
AI: The New Superpower for Building Software
According to Martin Woodward from GitHub (that’s a huge company where programmers store and share their code, kind of like a massive library for software blueprints!), AI is becoming incredibly important for how companies develop their software. He talked about this at a big conference called GitHub Galaxy.
He mentioned that AI tools aren’t just one-trick ponies; they can help throughout the whole process of creating an app, from the very first idea to the moment it’s released to you. It’s all about making teams work faster and smarter.
The Secret Sauce: What is “DevOps”?
Now, here’s a crucial point from the article: Woodward said that a big reason why some teams are really good at using AI is because they have something called “devops” in place.
Lila: “DevOps”? That sounds really technical, John! What in the world is that?
John: Great question, Lila! Think of it this way: building software usually involves two big teams. One team, the “dev” team, is the development team – they’re like the architects and builders who design and write the code. The other team, the “ops” team, is the operations team – they’re like the maintenance crew and facility managers who make sure the software runs smoothly and is available to users.
Traditionally, these two teams sometimes worked in separate silos, like two different departments in a factory that don’t talk much. This could lead to delays or misunderstandings.
But DevOps (short for Development and Operations) is a way of working where these two teams combine their efforts and communicate constantly. It’s about breaking down those walls, using automated tools, and having processes that allow them to release new features and updates very quickly and reliably. Think of it as a super-efficient, well-oiled machine where everyone is on the same page, constantly improving the product and getting it to users faster.
So, teams that are really good at DevOps already have smooth processes, clear ways of working, and technology in place to help them ship new software quickly. And it turns out, these are the teams that are best set up to take advantage of what AI can offer.
Beyond Simple Help: Smart AI Assistants (“Agentic Models”)
Many of you might have heard of tools like GitHub Copilot. At first, these AI tools were great for “autocomplete” – meaning they could suggest code as you type, just like your phone suggests words when you text. It’s like having a very smart spell-checker for computer code. But the article says we’re moving beyond that.
Lila: So, if “autocomplete” is like a smart spell-check, what’s next? The article mentions “agentic-based methods” and “AI-native ways of building software.” What do those mean?
John: Excellent observation, Lila! This is where it gets really interesting.
Imagine your current AI helper can finish your sentences. That’s “autocomplete.” Now, imagine an AI helper that can not only finish your sentences but also understands what you’re trying to achieve and can go off and do a series of tasks to help you get there. That’s closer to what an “agentic model” is.
Think of it like this: instead of just suggesting the next word, an “agentic model” can be given a goal, like “fix this bug” or “create a small part of this program,” and then it can figure out the steps, interact with other tools, and even try different solutions until it achieves that goal. It’s like having a mini-robot assistant that can think a few steps ahead and execute small projects on its own.
And “AI-native ways of building software” means that instead of just adding AI as a little feature to an existing program, companies are now designing software from the ground up with AI built right into its core. The AI isn’t just a sidekick; it’s fundamental to how the software works and delivers its features. It’s like building a car where the navigation system isn’t just an add-on, but an integral part of the engine and steering, making the whole car smarter from the start.
So, programmers using platforms like GitHub and Visual Studio Code (which is a popular tool for writing code) are getting access to a whole new generation of these super-smart AI models.
How These “Agents” Make DevOps Even Better
The really neat thing is that these “agentic patterns” are also making DevOps even smoother. The article mentioned a slide that said, “Teams will get better at devops with agents.” What does that mean in practice? These AI agents can help developers with tasks like:
- Fixing bugs: Finding and correcting errors in the code. Think of an AI agent as a super-fast proofreader for your computer code.
- Targeted refactoring: Cleaning up and tidying specific parts of the code to make it more efficient or easier to understand, without changing what it does. It’s like having a meticulous organizer for your program’s internal structure.
- Doing code reviews: When one programmer finishes writing code, another programmer usually reviews it to catch mistakes or suggest improvements. AI agents can now help with this, checking for common issues or suggesting best practices.
- Performing dependency upgrades: Software is built using many smaller pieces of code from other sources, like building a house with pre-made windows or doors. These “dependencies” need to be updated regularly, which can be a tedious task. AI agents can automate this.
Basically, these AI agents can handle a lot of the routine, time-consuming tasks, freeing up human developers to focus on the more creative and complex problem-solving parts of their job.
The Future of Software: Smart, User-Friendly, and Always Available
Woodward also shared a vision for the future of software. He said that future applications will be:
- Cloud-native: This means they live and run primarily on the internet, not just on a single computer or company server. This makes them more flexible and accessible.
- Centered on customer experience: The primary focus will be on making the software incredibly easy, intuitive, and enjoyable for the person using it.
- Intelligent, with AI being built into customer experiences: This means AI won’t just be helping developers behind the scenes. It will be directly integrated into the apps and services you use, making them smarter and more personalized for you. Think of apps that truly anticipate your needs or learn your preferences to provide a better service.
Leading companies are already heading in this direction, putting customer experience at the very top of their priorities. And AI is clearly a huge part of making that happen.
John’s Thoughts
It’s truly fascinating to see how AI is evolving from a helpful tool for individual tasks to becoming a foundational part of how entire software systems are designed and maintained. This shift towards “agentic” AI really feels like a game-changer, not just making programmers faster, but enabling a whole new way of building truly intelligent applications. It’s a testament to how quickly this field is moving!
Lila: Wow, John! So it’s like AI is not just writing code, but helping create smarter software that works better for us, the users? That’s really cool! It makes me think about how much easier everything could become.
This article is based on the following original source, summarized from the author’s perspective:
AI is powering enterprise development, GitHub says