Most comparisons of AI coding tools focus on the writing. Which model produces cleaner functions, which one handles TypeScript better, which one hallucinates fewer APIs. That is the visible half. The half that actually determines whether you keep the output is what happens before a single character gets typed.
An agent's first job on any real project is not generation. It is comprehension. And the way it builds that understanding decides whether the resulting diff belongs in your repo or fights everything around it.
The Reading Phase
When an agent starts on a project, it reads. Not only the files named in the task, but enough of the surrounding code to understand the architecture and the conventions in play. It picks up how you name variables, how you structure functions, how you handle errors, how you organize directories.
It also reads the things developers forget are informative: configuration files, package manifests, build scripts, test configuration. Those tell the agent what the project actually is. A project on Express and MongoDB needs different code than one on Django and PostgreSQL, even for an identical feature, and none of that is in the prompt. It is in the manifest.
This is why the output either reads like your team wrote it or reads like a stranger dropped in a snippet from a tutorial. How an AI coding agent works with your existing codebase goes deeper into the conventions layer, the part that linters never enforce but every reviewer notices.
Why One File Is Never Enough
Real features do not live in one file. Adding a field means touching the schema, the data access layer, the business logic, the API contract, and the UI that renders it. A tool that edits one file at a time pushes the coordination back onto you, and coordination is exactly where integration bugs come from.
An agent that has mapped the project makes those changes together by default. Add a field to a model and it also updates the serializer, the validation rules, the response shape, and the components that display it. That mapping step, identifying layers, frameworks, and the import graph, is what makes coordinated edits possible at all. Multi-file editing and how the agent maps project structure covers how that dependency map gets built.
The Legacy Code Surprise
Here is the counterintuitive part. Agents often do better on old code than people expect, and the reason is emotional rather than technical.
Legacy code is code that works in production and nobody wants to touch. The authors are gone, the docs are stale, the patterns are unfamiliar, and the downside of a mistake is large. Human developers approach it with dread, which makes them slow and tempted to work around the problem instead of through it.
An agent has no such reaction. It is not annoyed by a callback pyramid or confused by naming from 2012. It reads the code for what it is, traces the data flow systematically, and makes targeted changes in the existing style. What AI does with legacy code walks through that process from first read to first change.
The real constraint on legacy work is not the model, it is missing context. Undocumented contracts and thin test coverage are invisible to anyone reading only the code, human or machine. Characterization tests written before the refactor close most of that gap.
The Takeaway
If you are evaluating coding agents, stop scoring them purely on generated code. Ask what the agent read before it wrote, whether it can change five files coherently instead of one file cleanly, and what it does when you hand it something ugly and load-bearing.
The reading phase is not overhead on the way to the useful part. It is the useful part.
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