Zencoder’s Repo Grokking - Your AI coding unlock 🔑

CV Deep Dive

Today, we’re talking with Andrew Filev, Founder and CEO of Zencoder.

Zencoder is building AI coding agents purposefully designed for professional engineers working on real-world codebases. Unlike lightweight copilots or prompt-based assistants, Zencoder operates as a deeply integrated agent inside your IDE—supporting VS Code and the full JetBrains suite—with full awareness of your dev tools, repositories, and workflows. What sets it apart is its focus on the entire software development lifecycle: not just writing code, but debugging, unit testing, recovering from failure, and navigating complex repo structures. 

Zencoder’s Repo Grokking enables agents to retrieve and reason over thousands of files, generating context-aware solutions using just a single reasoning pass. The team has also launched integrations with 20+ platforms including Jira, Sentry, Monday and Asana. , while improving agents’ autonomy with features like “coffee mode” (ability to apply changes without confirmation) and self-healing capabilities that learn from failed outputs. Zencoder is already used by teams across startups and enterprises, with customers leaning on it to reduce engineering bottlenecks, ship faster, and tame complex codebases. 

In this conversation, Andrew explains how Zencoder is building an end-to-end AI assistant for the development lifecycle, why context is the most important unlock for coding agents, and how dev teams are using Zencoder today to ship faster, with fewer bottlenecks.

Let’s dive in ⚡️

Read time: 8 mins

Our Chat with Andrew 💬

Andrew, welcome to Cerebral Valley! First off, introduce yourself and give us a bit of background on you and Zencoder. What led you to start Zencoder?

Hey there! A bit about my business background is: I built a company called Wrike, which was an online project management software. It's still one of the key players in that whole market. I built it, scaled it to over 2 million users, and eventually sold it for more than $2 billion. It was a good run!

While building Wrike, I also got deeply interested in AI and ML. About a decade ago, I got introduced to the concept of AI agents through my interest in robotics. I had a hobby team that competed in the DARPA Robotics Challenge, and that’s when I started learning about agents. So once I sold Wrike and saw the massive progress being made in Transformers, I started thinking: we can take this intelligence block and place it into a more agentic setting, and it can do something really powerful. You give it tools—which are basically force multipliers for intelligence—and you put it into an agentic loop with feedback and context, and it becomes really interesting. That’s the technical side of the Zencoder story. 

On the mission side, I’ve always been a product guy at heart. I’d say maybe 2% of my ideas ever saw daylight because there’s never enough time or resources. I’ve always been passionate about how we can ship more, faster, and better. So the mission behind Zencoder really came out of those two threads coming together. Our goal is to help the world ship better software products, faster. That’s the story.

How would you describe Zencoder to a developer new to building with AI?

Zencoder is an AI assistant and coding agent that lives directly in your IDE. You can use it in VS Code with no forking, or across the entire JetBrains family. The agents are powerful, benchmark incredibly well, and are designed for real-world use.

We’re consistently among the top performers on benchmarks like SWE-bench. On the multimodal version, we doubled the best published result. We also ran our agent on OpenAI’s SWE-Lancer IC Diamond set—SWE standing for software engineering, and Diamond for hard tasks—and beat OpenAI’s own published results by 23% relative. The key here is that we’re running single-trajectory agents, not throwing 100 attempts at the wall like some large labs do. That makes the performance much more transferable to actual engineering work.

But performance isn’t just about benchmarks. Zencoder is built to be deeply useful. We have agents that run in the IDE, code completion, and full integration across the stack—we launched integrations with 20+ tools on April 1st, including Jira, Sentry, Asana, and Wrike. The vision is to support the whole software development lifecycle. It’s not just about writing code. We’ve built agents for unit testing, with a “coffee mode” that lets you walk away while tests are generated and run. The agents can also self-heal—when a test fails, they can use the feedback to fix the issue.

Under the hood, the reason our agents perform so well is the context pipeline. We’ve built what’s basically RAG on steroids, specifically for code. Out-of-the-box RAG struggles with codebases, so we built a custom approach—using best-in-class AI embeddings, sparse search, graph traversal, and our own re-ranking layer, and using LLMs in that pipeline. It’s all designed to give the agent the most useful possible context.

If you take a strong foundation model, add great context, equip it with tools and a structured framework—that’s when you get agents that deliver actual daily value. That’s what Zencoder is all about.

Who are your key users today? Who is finding the most value in what you're building at Zencoder? 

We see our primary target as professional engineers. That framing is intentional—they come from startups, growth-stage companies, and enterprise teams. But the key is they’re professionals. There are a lot of tools out there aimed at non-engineers looking to spin up a prototype, and while our agents can help with that, it’s not our focus.

We’re more interested in the harder problems—where the codebase already exists, whether it was written by a human or an LLM, and the challenge now is scaling and iterating. That’s where things get tricky for LLMs. You start hitting context limits, and you need much tighter collaboration between humans and AI. No agent today is going to fully autonomously produce a scalable, enterprise-grade system. It still requires a human in the loop, and that’s who we’re building for.

Things like integrations really matter to that audience. They know the value of their tools and want everything to work seamlessly in their dev stack. IDE support matters too—if you're writing Java you are likely using IntelliJ, if Android - probably Android Studio. So we meet people where they are and make sure our agents can work inside the tools they already use.

That said, people always find ways to push boundaries. We've seen people build games with our agents. We've seen people create entire CRM systems from scratch. It's wild what folks are doing once they get that level of power in their hands.

Walk us through Zencoder - what use-case should developers experiment with first, and how easy is it for them to get started? 

Starting is super easy now. Mastering anything in life takes some effort. AI agents are incredibly powerful tools, and with any powerful tool, there’s a proper way to wield it. “Proper” doesn’t mean there’s only one way, but there are definitely scenarios where the tool can be incredibly helpful—and others where you might be better off not using it at all.

Right now, I see a big demand in the industry from VPs of Engineering and recruiters for software engineers who can master AI coding tools. It’s starting to show up in job descriptions and interviews. So beyond just making you more productive in your day-to-day work, learning this stuff is actually great for job security—and in some cases, gives you leverage in the job market.

As for practical tips: like with most things in engineering, the main rule is simple—you just have to start using it. Get a feel for what it’s good at, where it struggles, and build intuition around those boundaries. For example, if you’re working on a large architectural redesign, the AI can be a helpful thinking partner, but you should still be making the decisions. You have way more context than the model—about your repo, your company, your product, your experience. The AI just can’t compete with that.

But if you’ve got a small, well-scoped bug that touches a few files and has a clear fix? That’s a great job for an agent. It might only need ten classes of context, and you can offload that to the model and focus your brainpower elsewhere. And if the fix isn’t perfect, it’s just like pair programming: you re-prompt it, tweak the inputs, give it feedback. You iterate. It’s like working with a junior dev—one that’s learning fast.

Which existing use-case for Zencoder would you recommend trying out? Any developer stories you’d like to share? 

I encourage engineers to start running agents on all those small features and bug fixes. Once you’re comfortable, the next level is learning to break down bigger problems into smaller ones the AI can handle. And along the way, you’ll build an intuition for what context is helpful to the model.

It reminds me of when I worked on a fully distributed team years ago. You couldn’t just toss vague specs over the wall—you had to be really clear. Otherwise you’d lose days in back-and-forth trying to clarify things. That skill—of figuring out what the other side needs to succeed—applies directly to working with AI. You start thinking about what the agent needs: what’s easy for it to find via tools or RAG, and what context or tribal knowledge only you can provide.

And then there’s the next level: building your own custom agents and prompting scripts that embed that tribal knowledge. I’ve seen people build really clever setups. One created a custom MCP server that pulled mocks from Figma. Another built one that tested the output in the browser. Now you plug those into your coding agent, and suddenly it’s building, testing, and debugging complete features—end-to-end. It feels like magic.

But that only happens if you roll up your sleeves and start playing. It’s not hype. It’s not about replacing humans. It’s about learning to use a force multiplier. And the people who really embrace that mindset? They’re doing some incredible things right now.

Tell us about Repo Grokking - one of the key innovations you’ve mentioned is key to 

Context is extremely important for LLMs. Otherwise, it’s like 50 First Dates—they wake up with no memory of you, your project, or anything relevant. They’ve been trained on a ton of open source code, so they’ve got fuzzy global knowledge, but not sharp, specific knowledge—especially not about your codebase. I like to think of it like quantum superposition: lots of broad information, but no exact details.

That’s why finding the Goldilocks context—just enough information, not too much—is critical. You want to give the LLM everything it needs and ideally nothing more. Because just like with humans, adding noise to the signal makes reasoning harder. Instructions get misinterpreted, and performance drops.

So the traditional information retrieval challenge of optimizing for both precision and recall becomes key. This is where repo grokking shines. At the foundation is strong code-specific information retrieval: we index everything, leverage code graphs, and layer on different techniques to surface the best candidates for context. Then we run a powerful re-ranking pipeline to produce that Goldilocks context—even across thousands of files, which would never fit into the context window of any current coding LLM.

And now we’re introducing additional tools into repo grokking, like project info. These tools are built to find high-level insights about your project—what a component does, how different modules interact. Because solving real engineering problems isn’t just about finding the definition of a function—it’s about understanding the architecture and intent.

Our vision is that repo grokking should know more about your codebase than anyone else on your team. At scale, a repository can get so big that no single engineer can hold it all in their head. It’s like Wikipedia: not smart on its own, but packed with knowledge you can tap into. Repo grokking is meant to act like that. It’s there to power intelligence—whether that intelligence is your coding agent, or just you using our chat interface to ask, “What does this repo do?” It’s useful both for AI and for engineers directly.

How do you see Zencoder evolving over the next 6-12 months? Any specific developments that your users/customers should be excited about? 

We’re going to continue our journey toward building better integrations and covering more of the software development lifecycle. We’ll also keep making our agents more powerful—able to execute more, better, and with stronger soft guarantees, so you can be confident they’ve done the work you asked for at the level your colleague would. It’s really about increasing the intelligence of these agents and making them more integrated into your entire dev stack, your organization, your practices, and your repositories.

Lastly, how would you describe the culture at Zencoder? Are you hiring, and what do you look for in prospective team members joining Zencoder? 

We probably have one of the largest independent AI coding labs out there, with more than 50 people in the engineering organization. Our team comes from a variety of interesting technical backgrounds. Some are more on the research side, with prior experience training LLMs. Others come from information retrieval, helping build our repo grokking capabilities.

We also have folks from DevOps and DevTools backgrounds—like engineers from JetBrains—who are used to working with tooling and parsing code through abstract syntax trees. Altogether, it’s a big melting pot of ideas, with a very fast pace of innovation.

Conclusion

To stay up to date on the latest with Zencoder, follow them on X and learn more about them at Zencoder.ai.

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