We're benchmarking coding agents on the wrong axis

Most teams evaluating coding agents fixate on the model underneath. GPT or Claude or Gemini? Which one scores higher on the latest leaderboard? It's a natural question and a mostly unhelpful one.

Swap one frontier model for another and you get a marginal difference in output. Give an agent real context on how your system is actually built, and the output changes completely. The quieter trend that's actually moving the needle isn't model choice — it's repository intelligence: how well an agent understands your codebase, your commit history, your architectural patterns, and the reasons behind the decisions baked into the code.

An agent inherits your codebase the way a new hire does

Here's the uncomfortable part. Most teams have made their repositories illegible — not to humans who've been around long enough to absorb the folklore, but to anyone, or anything, encountering the code cold.

Tribal knowledge lives in Slack threads that scroll away. Conventions are implied, never written down. The why behind that strange abstraction left the building the day the engineer who wrote it did. Human teams paper over this with tenure and hallway conversations.

An agent gets none of that. It inherits the confusion exactly the way a new hire would — except it won't ask a clarifying question. It'll just guess, confidently, and produce something that looks plausible and quietly violates three conventions you never wrote down. You'll find out at review time, or worse, in production.

The better the model, the more convincing the wrong guess. Raw capability doesn't fix missing context; it just makes the gaps harder to spot.

The highest-leverage work is making your repo legible

The instinct is to chase the perfect model. The actual leverage is making your codebase something an agent can reason about:

Document the architecture, not just the API. Endpoint signatures are easy. What's missing is the shape of the system — how the pieces fit, what the boundaries are, where the load-bearing decisions live.
Keep patterns consistent enough to be learnable. An agent infers conventions from examples. If your codebase does the same thing five different ways, it has no convention to learn and will happily invent a sixth.
Write the "why" into the repo, not the standup. Decision records, meaningful commit messages, comments that explain intent rather than restate the code. The reasoning has to live where the agent — and the next engineer — will actually find it.

A simple test

Would a competent engineer who joined yesterday, with no Slack history and nobody to ask, be able to make a correct change to this part of the codebase using only what's in the repo? If yes, an agent probably can too. If no, that's your backlog.

Good context engineering is just good engineering hygiene

None of this is novel. Documenting architecture, keeping patterns consistent, recording the reasoning behind decisions — these were always the markers of a healthy codebase. Agents didn't create the need. They just removed your ability to ignore it, because they expose the gaps immediately and at scale.

The teams that invested in legibility for human reasons are the ones whose agents work well now. That's not a coincidence — it's the whole point.

We're here to help founders and teams design and build digital products that are built to scale with you, not slow you down. If you're looking to build something, get in contact with us today!

The real question isn't which model you're running. It's whether your codebase is ready for something to read it — or just for humans to tolerate it.