Augment Code

Augment doesn’t just help me write code faster, it stays with me through code review and refinement, turning “done” into “deployment ready and solid”! It’s my 6th week at Augment, and one of the evenings, I decided to get to know the system the way I usually do: by chasing latency. I started with the basics i.e metrics dashboards, looking at p50 and p95 latencies for one of the RPCs, where I spotted a suspiciously slow one. From there, I dived into distributed traces, formed a hunch about the culprit, and jumped into IntelliJ. In the target method, I noticed an optimization opportunity. I asked Augment Code to craft a new function for the specific task, telling it exactly how I wanted it optimized. It generated the function, added tests, and replaced the slower call with the faster optimized one. Local tests passed, and the improvement looked great. Then came the part I really appreciate: Augment doesn’t stop helping once I’m “done coding.” I opened a PR and in came Augment Code-Review! It flagged an important edge case I missed. It helped me refine the change, not just write it. End result: In a rather unfamiliar codebase, I went from "something feels slow" to a well-reviewed performance win! Augment helps me both while I’m in the flow of coding and after, when it really counts. Now the PR was actually ready for review from my peers who appreciate that it has passed another set of vigilant inspections! #augmentcode

We just dropped a nightly build supporting parallel agent threads. https://lnkd.in/ejPYe5jx I was curious about the implementation so I asked Augment to "find where multithreaded conversation is implemented". This result is only possible because the Augment Context Engine provides the agent with a deep semantic understanding of my code. There are other semantic searches out there, but RAG doesn't scale so over a large codebase and you will find that the search results miss very obvious things (more prevalent as your codebase gets larger). Augment does a full search every time in <200ms regardless the size of your codebase (think hundreds of thousands of files of source code).

“If you’re not using AI to learn, you’re already behind.” Siham Darr gave some of the most grounded advice we heard at KubeCon: ➡️ Use AI to accelerate your learning ➡️ Use it to find resources, not shortcuts ➡️ Don’t copy blindly — understand the code ➡️ AI won’t replace you… but ignoring it will hold you back “AI is the best way to learn. It helps you scale.”

“Hallucinations aren’t random — they’re a context problem.” Rudraksh Karpe explained how his team ZS improved RAG pipelines for a major airline: ✅ Chunk the data ✅ Add a clear descriptor to each chunk ✅ Improve retrieval + reduce hallucinations This is exactly the same philosophy behind Augment Code’s context engine: better structured context → better answers → safer code.

“AI isn’t just for writing code — it’s for understanding it.” Ryan Cooke's team WorkOS scaled 30× in traffic in six months. ❓ That means architecture questions. ❓ Routing questions. ❓ Resiliency questions. And the bottleneck becomes understanding the system — not typing into it. “I love reading code. AI makes that accessible.” This is why Augment is built around the Context Engine: AI that actually reads your codebase, not just predicts text.

“AI won’t replace developers — but it WILL replace the things humans do badly.” Divine Odazie shared a powerful take at our KubeCon booth: 🤖 Continuous AI-powered security testing 🤖 Faster learning loops for devs 🤖 Tools that understand how we work 🤖 And agents that catch what humans miss “Humans are flawed. AI helps us run faster while staying secure.” He also called out something we’re incredibly excited about: AI code review is coming — and it’s going to change how teams ship safely. Stay tuned. 👀

“AI is becoming the senior engineer you bounce ideas off.” At KubeCon, Aniruddha Brahmamurthy shared a take we heard from a LOT of experienced engineers: AI isn’t just a code generator. It’s the neutral, calm, always-available idea partner. When he’s exploring patterns or evaluating architectural decisions, he uses AI as a “principal-engineer whiteboard” — a way to surface tradeoffs, explore alternatives, and refine thinking before he commits. This is exactly the kind of workflow Augment Code was built for: real engineering conversations grounded in real codebase context.

“AI is flattening the playing field — the old startup moats are disappearing.” — Vivek Ravishankar, Render At KubeCon, we asked the community for their spiciest AI takes. Vivek gave one that every startup CEO and PM should sit with: “The moats of the past won’t save you. AI lets everyone move fast — startups and incumbents.” Historically, speed was the moat. Being small made you nimble. Being early gave you an edge. But as Vivek points out: AI accelerates product development AI compresses iteration cycles Large incumbents can now pivot almost as fast as startups Winning features get copied immediately by model providers So what becomes the moat in a world where velocity is no longer proprietary? Craft. User experience. Deep understanding of the customer. At Augment Code, this resonates deeply. The tech stack matters — but what developers feel when they use your product matters more. Context, trust, precision, reliability… that’s craft. In a world where everyone can ship fast, only a few can ship beautifully. 🎥 Huge thanks to Vivek for sharing this thoughtful — and very real — perspective.

I'm a late adopter of new technology. I've been dragging my feet on leveraging AI outside of writing assistance tools. But as an EM, I'm exploring how to leverage AI through the SDLC -- and have to say I'm having a lot of fun. The past two weeks have been exploring using Augment Code across multiple stages. My impression is the quality is absurdly good. The first test was experimenting at the earliest stages with the Product Intake phase. How could Augment be used to facilitate the creation of Problem Briefs. Connecting Augment to Confluence, company OKRs, and GitHub; I gave it a problem that I knew would require a cross-team effort to address. With a relatively simple prompt, it was able to understand how the code contributed to the problem, describe it in detail - backed by code references, provide rough revenue estimates, and even provide 2-3 solutions with delivery estimates. The ability to quickly leverage code to scope problems are describe friction means Product and Business can quickly see high-quality documents set against company objectives. It means engineering and product can align on a intake process that takes engineering complexity into account. Further connecting research and data can help quantify the impact of addressing issues and should help inform roadmaps. Over the next few months, I plan on having engineering creating Problem Briefs as the first step in the intake process - reviewing these for accuracy - and asking my Product counterparts to polish them. So while the tool may be focused on the development phase, I'm very interested in bringing code insights into other parts of the development process. I have been experimenting on the usage of Augment for scrum-related activities and will create a separate post to describe this.

“We don’t do nearly enough code reviews — and AI agents should help.” — Robert Glenn When we asked engineers at KubeCon which part of the SDLC is most ready for intelligent agents, Robert didn’t hesitate: “Code reviews. We don’t do them enough.” He pointed out something every engineering leader is now quietly admitting: 🚀 AI is generating more code 🚀 Teams are shipping faster 🚀 PR volume is exploding And yet… code review hasn’t evolved Robert imagines an agent that can act like a context-aware static analysis partner — reviewing diffs, spotting regressions, understanding APIs, and giving developers the confidence that fast code is still good code. “I haven’t seen much of that yet. But it would be really exciting to see.” 👀 We won’t say too much…but let’s just say Robert might be onto something. The future of AI in the SDLC isn’t just writing code — it’s reviewing it with the same depth and context as your best engineer. Stay tuned.