Beyond grep: The case for a context-rich AI coding harness

What happened

Augment Code’s Vinay Perneti talks models, harnesses, and context. There are a lot of AI coding applications out there, and as impressive as large language models and the agents they enable have become, many of the most recent developments in AI-assisted development have been in the software that manages those models, not just the models themselves.

Perneti briefly described Augment Code’s differing approach, offered his own team’s evaluations of the advantages, responded to some of the arguments for a leaner harness, and shared his perspective on some of the concerns developers have about AI tools and agentic workflows more broadly. This interview has been edited for length and clarity. Ars Technica: Can you explain how Augment Code’s context engine works?

Vinay Perneti: So, if I take a step back, what is it that a particular developer is trying to achieve? They want a particular task implemented and they give it to an agent. And the interesting thing with agents today is they have a limited context window, and every time they need to essentially go get all of the context needed for that and then work on it.

There’s two approaches to context. One is grep-based. Claude Code and Codex and other agents have done that. The second is the semantic retrieval bit. For Augment, we’ve always taken the semantic route, and I’ll describe the building blocks of that. I would say there’s two core pieces.

Why it matters

Earlier this summer, I spoke with the head of product for Claude Code, Anthropic’s Cat Wu, about that company’s approach to building that software. Wu repeatedly came back to the same point in our conversation: Anthropic’s models (and those of its direct competitors) are improving so quickly that it makes little sense to plan too far ahead or to build opinionated or limiting features around them.

Rather, the Claude Code product team attempts to maintain what they call a lean harness. A harness is the software built around one or more AI models that determines how they are used. It decides what the models see, what actions they can take, and how they interact with the code base. Think of it like a layer between a model and the developer’s actual project.

It’s not that Claude Code as an application and harness has no opinionated features or design choices, but its product team does seem to err on the side of trusting where the models will take them over the next year. There are other harnesses besides Claude Code, though. There are OpenAI’s Codex, Google’s Antigravity, open source alternatives like OpenCode, and options from startups like Cursor or Augment Code.

Each can have different features, emphasis, or opinions about how a model can or should be utilized in agentic workflows. One key choice that Claude Code makes is to avoid approaches that build a structured context around a codebase in advance by default. “Going by the evals, we don’t see a measurable change,” she said of those approaches.

” Augment Code has made a substantially different bet, although the two companies are not necessarily testing the same interventions or optimizing for the same outcomes; its product pre-indexes the repository using embeddings, a retrieval model, and a vector database, then retrieves conceptually relevant code. So to get the other side of that discussion, I spoke with Vinay Perneti, Augment Code’s VP of Engineering.

What to watch

One is, we have an embedding and a retrieval model pair that are working in the system, and then you have a vector database and an entire, highly optimized back-end system that makes it possible to retrieve in sub-milliseconds. Ars: Does it have an advantage in one kind of code base versus another? Perneti: Yeah, it actually turns out the advantage comes in large, private codebases.

And here is why I say that. For all the public, open source repos where most of the benchmarks are run, eve