Agentic Coding is creating more code. More code is creating a bigger need for Code Search

Most enterprise teams have already adopted AI code generation tools. They’ve moved beyond the trial phase. These tools are in production, developers are using them every day, and leadership is on board. Now the big question is: how do we measure the real value these tools bring?

This is the challenge teams are facing. AI tools are generating code faster than ever. But with that speed comes complexity. More code only helps if teams can understand it, search through it, and keep it maintainable. That’s the piece engineering leaders are still trying to figure out.

What we see in our customer data

We looked at data from our large enterprise customers who’ve adopted AI coding tools. The pattern was clear. In fact, 84 percent of those accounts showed a steady increase in lines of code after bringing AI tools on board. More code led directly to more search activity. Developers needed to understand what the AI was producing, how it fit into the bigger picture, and which parts of the codebase were safe to modify.

That trend is now consistent across customers. AI tools speed up code creation, and that speed creates more complexity. Teams respond by searching more and relying on code intelligence to keep everything under control.

What Ox Security found in their research insight

Now, let’s bring in another piece of the puzzle. According to a recent report from Ox Security, there’s an interesting pattern that goes hand-in-hand with what we’ve observed. Their study highlights that AI-generated code, while incredibly efficient, sometimes behaves like having an army of junior developers working at super speed. This isn’t always the case, but it’s certainly a a pattern to watch out for. .It’s not that the AI is inherently insecure or faulty; it’s that it can replicate certain coding patterns, including bugs or less maintainable code structures, at a scale and speed that’s hard for human teams to catch up with.

What the Ox Security report shows is that this rapid code generation can lead to what they call “hidden complexity.” It’s like adding more layers to the codebase that might carry over old bugs or create structures that are trickier to refactor. In other words, it’s not that AI codegen is a problem, but it does mean that teams need strong tools to manage and understand the growing complexity that comes with it.

Why this matters for enterprises

So why does all this matter for enterprises? Well, it’s all about staying in control of a codebase that’s expanding faster than ever. AI codegen is a fantastic accelerator, but that acceleration brings new challenges. Even the cleanest AI-generated code adds more files, more functions, and more surface area to your codebase. It’s growth on a scale that many teams haven’t had to deal with before.

This puts real pressure on developers. They need to understand code they didn’t write, and they need to navigate a codebase that’s growing in complexity. This makes code navigation, exploration, and understanding absolutely essential. It’s not just about keeping up; it’s about making sure that developers can quickly find the exact piece of code they need without getting lost in the weeds.

That’s where search becomes the anchor. With powerful search and code intelligence, teams can manage the complexity, see patterns, understand dependencies, and keep their codebases secure and maintainable. In other words, search isn’t just a nice-to-have anymore; it’s a must-have for any team adopting AI codegen tools.

This is why Sourcegraph is more valuable than ever

Here’s where we come in. At its core, Sourcegraph is the source of truth to find, navigate, and understand your entire codebase. We ingest all your git data—files, functions, repos, references—respect your code host permissions, and build a rich graph of your entire codebase. And now we offer Deep Search which lets developers use natural language to explore their entire codebase. It’s designed for humans to quickly understand the new code their AI tools produce. Plus, our core search engine is already used by AI agents themselves to read and navigate large codebases. So as AI tools write more code and companies move toward agent-driven workflows, Sourcegraph is the partner that helps both humans and AI keep everything running smoothly.

In short, AI adoption creates more code, and more code creates a bigger need for search. By combining the strengths of AI tools with Sourcegraph’s search capabilities, teams can tackle complexity head-on and stay in control of their fast-growing codebases.